Oil and Gas Reservoir Prospecting and Exploration: High-Resolution Seismic (HRS) techniques and technology 3030843882, 9783030843885

Citation preview

Vladimir L. Trofimov Fanil F. Khaziev Alisa V. Trofimova

Oil and Gas Reservoir Prospecting and Exploration High-Resolution Seismic (HRS) Techniques and Technology

Oil and Gas Reservoir Prospecting and Exploration

Vladimir L. Trofimov • Fanil F. Khaziev Alisa V. Trofimova

Oil and Gas Reservoir Prospecting and Exploration High-Resolution Seismic (HRS) techniques and technology

Vladimir L. Trofimov Moscow, Russia

Fanil F. Khaziev Moscow, Russia

Alisa V. Trofimova Wilmington, NC, USA

ISBN 978-3-030-84388-5 ISBN 978-3-030-84389-2 https://doi.org/10.1007/978-3-030-84389-2

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Dedicated to the subsurface researchers— seismic geophysicists, extracting information about energy resources at the limit of the method

Preface

This book is devoted to topical issues of detailed seismic data interpretation using high-resolution seismic (HRS) techniques, which are based on the numerical method developed by the authors for solving the inverse dynamic seismic problem (IDSP). In English-language terminology, when solving interpretational seismic tasks, this line of investigation is called research using the seismic inversion procedure. It should be noted that the classical methods for solving inverse problems of mathematical physics and the numerous practical inversion techniques used in seismic prospecting are very different, as shown by a comparative analysis of the theoretical foundations and numerical methods for solving them. The software and methodical implementation of the HRS methodology was performed by the authors in the form of high-resolution seismic technology (HRS-Geo technology). The study of real subsurface media is focused mainly on the use of seismic record dynamic features, the realization of its maximum possible resolution—on the construction of 2D-sections and 3D-cubes of effective acoustic impedances (AI) and effective reflection coefficients (RC), which have vertical resolution, equal to the quantization step of the seismic record along the time coordinate. In the HRS-Geo technology, a significant role to the well data research is given. At the beginning of the research topic under review, we in brief discuss the conditions associated with the idea of creating the HRS-Geo technology and briefly consider the principles that were laid in the basis of the technology main component development—its interpretation systems and software modules. The practical experience of the development and application of the developed software and methodological tools of the HRS-Geo technology can serve for specialists involved in detailed geological interpretation of the seismic record dynamic characteristics as a guide for solving complex problems of oil and gas geology. Recall one of the statements given in the form of a response to the demand for maximum economic return, formulated by F. Hilterman: “Determine where, in the course of structural and dynamic interpretation, a deviation from science occurs and the dominant element of art appears. Speaking of risk in such terminology, the old vii

viii

Preface

dictum ‘Seismic interpretation is an art that takes years to master’ takes on a new meaning. Seismic interpretation is both a science and an art.” At the initial stage of technology development (since 1989), the authors used 2D CDP seismic materials, obtained in seismic and geological conditions of the Pripyatsk paleorift of the Belarus Republic. During this period of technology design, the main reasons for the development were: 1. Comparatively small linear dimensions of the studied oil-perspective objects occurring in the sediments of the “Shatilkovsky” anhydrite-limestone horizon (in the bottom of the 7th rhythm pack of the upper saline halite substratum); in intersalt sediments (associated with borichevsky sandstones, with local acoustic heterogeneities caused by zones of facial replacement and pinching, with thinlayer non-anticlinal traps, with relatively small flat-topped reef massifs with a characteristic zonality of the reservoir distribution and oil and gas saturation along the periphery area; with a comparatively narrow intersalt-block, not exceeding 400 m, which turned out to be almost completely oil saturated); relatively small combined structural lithology and tectonic screened traps in subsalt complexes of the Pripyat Trough sediments. 2. The need to improve the vertical and horizontal resolution of seismic records (in conditions of thin-layer and extremely small-sized oil-perspective objects) based on the seismic information division into two types: on information related to the source of shot for elastic seismic oscillations and on information related to the real geological medium. The solution of such a task, essentially the inverse dynamic seismic problem, was to provide: – first, on each discrete of digital seismic record, the complete elimination of the interference of seismic signals (almost all local seismic responses) resulting from the overlying (relative specifically studied object) acoustic heterogeneities; – second, the possibility of estimating weak seismic signals arising from the sought for heterogeneities, including such geological indicators as lithological composition, reservoir properties, and oil and gas saturation; – third, the possibility of constructing a thin-layer section model, on which one could effectively study the detailed internal structure of a real geological section (first of all, reservoir rocks, with possible saturation with hydrocarbons) and on this basis ultimately optimize geoexploration. 3. Conducted at that time by a number of authors (Kuznetsov O.L., Petukhov A.V., Zorkin L.M., et al.), numerous studies have shown that the mechanism for the propagation of elastic waves in reservoirs filled with gas-liquid fluids is not completely studied. In one of the works by N.Z. Zalyaev, materials with high prospects for the use of acoustic parameters of the geological section for the differentiation of water-saturated and oil and gas-saturated reservoirs are shown. By way of comparing functionally transformed well log curves, such as NL (neutron gamma-ray log) and SN (sonic (acoustic) log), in lithological homogeneous sections a noticeable effect of gas and oil on acoustic parameters is shown

Preface

ix

by Zalyaev N.Z. In sandstones with a porosity of 20%, the presence of gas leads to an overestimation of the parameters T (on the basis of which the elastic waves propagation interval time ΔT is calculated) compared to the water saturated by 50–60 μs/m, which is equivalent to an increase in porosity up to 15%. The effect of reservoir oil saturation in carbonate sediments with an average porosity of 8%, in quantitative terms, is 20–30 μs/m, which leads to a false overestimation of porosity in SN by about 1.8 times. This circumstance confirms the existence of prerequisites for predicting oil and gas deposits from seismic exploration data also in areas similar to the geological conditions of the Pripyat Trough. 4. In the process of further development of the HRS-Geo technology in 1993–94, cooperation with the foreign consulting company Scott Pickford Group (UK) took place. Specifically, for cooperation on the Inco-Copernicus program, experience with a demonstration of the performed research results was exchanged. The authors presented the results in the form of effective acoustic impedance (AI) and effective reflection coefficient (RC) sections, in which information about their detailed internal structure with the seismic record quantization step along the productive and prospective layers of the Pripyat Trough was presented. For a representative of this foreign company, the above results turned out to be some revelation. Western companies similar results have not obtained yet. Later (in the period 2000–2008), with active cooperation with Russian companies, the results of research on a significant amount of MCDP 2D and 3D surveys and well log data using HRS-Geo technology were obtained. Domestic experts to the development of the authors expressed their criticism, various judgments, which served as a starting point for the subsequent development and application of the HRS method. As part of the development of a high-resolution seismic survey ideology, the use of HRS-Geo technology is based on the following fundamental criteria: (1) saving the dynamics in the records during processing; (2) the separation of information about the source and the subsurface environment; (3) resolution within the limits seismic survey modifications; (4) adequacy of measurement scales and optimal coordination of seismic and well data; (5) physical conditionality of the used reconstructed geological and geophysical parameters of the real subsurface environment. The basis of the developed HRS-Geo technology are software packages—interpretive system with a significant number of software modules, in particular, of the system: SPS (Seismic Processing System), WPS (Well Processing System), RPS (Resource Processing System), VKSYNT (one-dimensional modeling with assessment of the elementary boundaries and strata contribution), and SKOR (velocity and elastic-deformation characteristics estimation). All these information systems are interconnected. In the process of technology development, in general, some systems were replaced by licensed software packages. In this paper, the authors tried with the necessary detail and accessibility to highlight the range of issues related to the development and application of the

x

Preface

HRS-Geo technology on a variety of seismic data. A significant practical material amount on the study of the detailed internal acoustic heterogeneities structure of productive and potentially productive layers in various seismic and geological conditions are analyzed. As far as possible, the authors avoided the abundance of mathematical formulas and the domination of highly specialized terminology. When using the work done by the authors, problems are solved: – deciphering the specifics and conditions for the formation of an interferential seismic wave field in a thin-layered real medium (Chap. 2); – solving an inverse dynamic seismic problem and building on this basis detailed thin-layered seismic–geological 2D and 3D models of a geological section (Chap. 3); – estimations of geological indicators of the studied sediments that are most important for the prediction, prospection, and industrial exploration of oil and gas deposits: lithological composition, reservoir properties, and under favorable seismic and geological conditions, the nature and degree of reservoir rocks saturation with fluids (Chaps. 5 and 6). The most important for further in-depth comprehensive data analysis is the development of historical, geological, seismic, stratigraphic, and facies analyzed through the use of the entire spectrum of geological and geophysical indicators of the real environment, obtained on the basis of high-resolution seismic (HRS) methods. This allows, on the one hand, to significantly increase the information content and geological effectiveness of seismic surveys for oil and gas, and on the other, to stimulate further comprehensive development of oil exploratory complex geological and geophysical studies. The described research directions and the results obtained here can be useful for specialists and students of geophysical and geological specialties for the purpose of in-depth study of the problems under discussion. Moscow, Russia Moscow, Russia North Carolina, USA

Vladimir L. Trofimov Fanil F. Khaziev Alisa V. Trofimova

Foreword

Globalization of energy supply problems requires, if not reconstruction, a significant adjustment of the management and engineering decision mechanisms. Thus, in the oil and gas industry, easily accessible resources of raw materials are rapidly reduced, mining and geological conditions of production are complicated, and, as a result, the costs and risks of implementing relevant production programs are increased. From the moment of the emergence of the first fields to the present day, the only tool for the identification and development of hydrocarbon deposits remains the drilling of prospecting, exploration, and production wells. A certain paradox is that the level of development of drilling equipment and technologies, in principle, corresponds to the complexity of the tasks to be solved in the process of searching for oil deposits or their development. At the same time, errors in setting the task, for example, the priority of gross indicators in management or “savings” in research, design, and scientific and technical support of work, lead to the fact that this is the unique (at the same time very expensive) tool often works inefficiently. Thus, there are significant reserves in reducing unproductive time and financial resources. The use of these reserves will have a positive effect on both the basic indicators of production and the investment attractiveness of the relevant projects or enterprises. As one of the possible ways to solve this difficult problem, we can suggest the development of principles and methods for modeling the processes (objects) of oil and gas production. Effective management of oil and gas field development is impossible without a detailed geological and technological reservoir model. The initial and residual reserves localization in space and in time is a mandatory step for the successful, economically feasible use of modern technologies to intensify oil production and enhance oil recovery. A formalized description of a hydrocarbon deposit is computer digital model of the geological space. The mass modeling process is carried out and it is already clear that in the future it is the basis of scientific and technological progress in the oil industry. The development of modeling technologies is stimulated by the increase in the volume of geological, geophysical, and field information used, the complexity of the xi

xii

Foreword

geological structure of the studied deposits, the need for systematic modeling of the field as a single object, taking into account the heterogeneous structure of reservoirs, deposits, and time processes occurring during their operation. One of the most important elements of the geological model constructing is seismic exploration; it has no alternative, both in terms of searching structures and new oil fields, and in terms of the parameters interpolation of the exploration drilling carpet over the entire study area. The relevance of the monograph is determined by the need to improve the methods for enhancing the geological informativity of seismic results obtained under conditions of thin-layered section and extremely small-sized oil and gas prospective objects by increasing the seismic record vertical and horizontal resolutions. Resolution enhancement is determined on the basis of seismic information division into two types: related to the source of elastic seismic oscillations and associated with the real subsurface geological environment. The authors, based on many years of experience in the development and application of a high-resolution seismic data complex interpretation method, solve the problem of prospecting and exploration of relatively small-sized hydrocarbon traps and study their fine internal structure, and present the methodology and results in the monograph. It should be noted that the term “high-resolution seismic method” refers primarily to field seismic surveying methods. Given that the seismic exploration method involves performing a certain work stages: field survey, field seismic data processing, integrated geological interpretation of seismic and well data. Therefore, at each stage it is important to solve the problem of improving the data resolution. The final results depend largely on the specific techniques, methods, and various technologies used at each stage of the work, which are related to improving the seismic data resolution. At the field survey and preprocessing stages for any resolution of wave signals, seismic wave interference is almost always present in the data and is directly related to the wavelength finiteness of the seismic pulse, which probes subsurface medium. This fact is a significant detailed limitation of the data obtained about the environment (including various predictive geological indicators). The final resolution of seismic data at all stages depends on the applied signal conversion methods. The authors, by solving an inverse dynamic problem, significantly increase the resolution of seismic data and eliminate the sounding impulse influence from a seismic record. At the same time, on the one hand, the wave interference is practically excluded, and on the other hand, the study detail increases (by about an order of magnitude with respect to the seismic data obtained in the first two stages—at the stage of their field surveying and subsequent typical processing). Therefore, as a result of solving the inverse dynamic seismic problem, at the output is an increase in the resolution of the seismic record with the time discreteness step detail in the form of effective reflection coefficient and acoustic impedance sections and cubes. In this regard, the authors of the book in the technology development and its practical application for a number of years use the term “high-resolution seismics” as applied to the stage of processing and complex interpretation of seismic data. Here, it

Foreword

xiii

is essential that there is a possibility of transition from the positions and principles of wave seismics to reflection coefficient and acoustic impedance seismics, i.e., to the geological environment parameters and compare them with geophysical data on wells. In the theoretical and methodological section of the book, the authors examined a fairly wide range of tasks in the seismic data processing and interpretation related to the general direction of the study—“high-resolution seismics.” It is worth mentioning the most important author's achievements in this direction. This is a softwaremethodical implementation of the high-resolution seismic method and the technique of its application made in the form of HRS-Geo technology. The solution of the inverse dynamic problem, which is the main part of the technology, is found by an optimization method using the well-known formulas for solving a direct problem for calculating the seismic wave field. The results of solving an inverse dynamic problem on test and real materials using the developed technology software are performed. Important is the author’s development of automated processing and interpretation of standard well logging data (GBS), which is produced by the functional transformation method of geophysical parameters by integrating them into information systems. On this basis, the continuity of data processing and data analysis, the quantitative linking of geophysical parameters, taking into consideration their genetic relationships, is ensured. At the same time, the material composition, porosity, content of bound water, and useful reservoir capacity are determined by wells; the oil and gas saturation of reservoirs is estimated; and a number of physical characteristics of the geological section are calculated. Due to these two main developments, the authors carried out a lot of research on numerous real well, land, and marine seismic materials with deep theoretical, methodological, and experimental study. Assessing the work done by the authors as a whole, it should be noted its high scientific and methodological level. The studies of the authors, the proposed software and methodological solutions, and the positive results of testing software on real objects confirm the correctness and validity of the developments made. It should be noted that due to the use of the high-resolution seismic method developed by the authors, or rather a new approach to the processing and interpretation of seismic data, wave seismic surveys are inherently modified to highresolution acoustic seismics. These studies determine the direction of seismic exploration development in terms of building a detailed seismic–geological model of oiland gas-perspective objects. The monograph publication will be useful and interesting for a wide range of specialists in the oil and gas industry: geophysicists and geologists, students, bachelors, masters, and graduate students engaged in the processing and geological interpretation of seismic and well data.

xiv

Foreword

It will also be interesting to specialists of oil and gas companies, whose activities are related to the exploration and assessment of the new and previously studied areas prospects. Executive Director of JSC “Central Geophysical Expedition”, Doctor of Technical Sciences, PhD in Physics and Mathematics, Associate Professor, Corresponding Member of the Russian Academy of Natural Sciences, Honorary Oilman of the Russian Federation, Moscow, Russia

S. A. Kirillov

Introduction

This work is aimed at considering a number of search and exploration issues of complex lithologically and stratigraphically limited hydrocarbon traps on seismic data by developing and using the high-resolution seismic (HRS) method. The calculated data and corresponding visualization materials that meet the requirements of exploration for oil and gas are presented. It is very important for each of the studied oil and gas field or search object to have materials in the form of cubes and sections, which on the basis of the effective reflection coefficients (RC) and effective acoustic impedances (AI) are built. On their basis, detailed seismic–geological models are built, including productive intervals of the section with predicted values of such geological indicators as lithological composition, reservoir properties, and under favorable seismic and geological conditions, the nature and degree of reservoir rock saturation with fluids. It is important to have an idea of the adequacy of the predicted geological indicators, which are obtained only from seismic data (especially under relatively “hard” conditions such as the lack of well information and drilling materials in the available a priori geological and geophysical information). When interpreting seismic data, one often encounters the problem of adequately mapping relatively thin strata of a geological section or their set at appropriate seismic recording intervals. The particular difficulty in this case is the determination in the seismic wave field of the place of displaying specific time intervals, which are directly connected with productive oil-saturated or potentially productive intervals of the geological section. From the correct (adequate) assessment of the location and nature of the display of the latter will largely depend on the results of certain geological indicators predicting, as well as their qualitative and quantitative characteristics. The use of seismic modeling programs, well materials, and seismic data allows to perform a quantitative assessment of the influence of the composition and section properties under study on the seismic record dynamics and on its various transformations. This influence turns out to be different at different time intervals of the seismic record and the results of the transformation of the studied section in the xv

xvi

Introduction

conditions of the almost universal manifestation of wave interference. Extraction of the maximum possible information (in the form of contributions—local seismic responses) from target geological indicators at specific time intervals of the interference wave field and the results of its transformations can significantly increase the efficiency of predicting according to seismic data of various geological indicators. Comprehensive determination of the composition and properties of rocks of oil promising strata according to geophysical borehole survey (GBS) is necessary for the formation of a detailed environment geoacoustic model containing information on the distribution of acoustic velocity, density, lithology, reservoir properties, and oil saturation of the reservoir rocks in the studied interval of the section. Specifically, such model is formed at the stage of integrating the results of solving an inverse dynamic seismic problem and GBS due to an adequate “convergence” of highresolution seismic and GBS data. To do this, it is necessary to adequately match the scale of seismic exploration measurements and GBS to the level of the possibility of studying the matter, facies, and structural features of the geological section. The use of HRS-Geo technology in the studied areas is focused primarily on extracting information on geological indicators from seismic data. With a positive solution of the problem, the information on the vertical geological section extracted from the seismic data becomes comparable to the results of processing and automated interpretation of the GBS data: the seismic trace is converted into an impulse response of the environment using seismic records by applying the IDSP solution procedures. From such paths, sections of effective reflection coefficients (RC) and effective acoustic impedance (AI) are formed, in which the discrete step of RC and AI along the vertical is equal to the sampling step of the seismic record over time. It should be noted that the existing modern methods of seismic data processing have, on the one hand, a significant software “arsenal” and methodological tools that almost completely exclude various interferences from the “useful” part of the seismic wave field and “increase” the signal-to-noise ratio. On the other hand, there are still no effective methods for estimating the informativity of seismic data, the results of which could confidently say that when used in seismic dynamics without strong distortions, the necessary target information about the section under study would be saved. We are talking about preserving in the seismic dynamics of the necessary amount of identifying information about the target geological indicators. At the same time, it often turns out that various methods for processing and interpreting seismic data are either not well developed for the full forecast cycle— from “physical properties and parameters of a geological section” studied in sections of existing deep wells to “predicted physical properties and parameters of a geological section” in sections of the so-called predictive wells, built on high-resolution seismic data. Or they are focused only on the use of various “attribute” (and essentially purely interference) dynamic parameters of the seismic wave field. As is well known, such forecasting methods (including “attribute” methods) in complex seismic and geological conditions do not fully provide positive results for predicting the composition and properties of real geological media (including due to the implicit and indirect nature of the estimates, i.e., lack of direct assessment methods).

Introduction

xvii

Research experience shows that the difficulties in solving this problem are largely inherent in the very nature of the seismic method, more precisely, in the methods of solving the inverse dynamic seismic problem. With regard to the study objects (they are the purpose of search and exploration), these difficulties are due to natural factors, namely, the complex geological structure of the studied oil-perspective objects, i.e., features of the detailed internal structure of the desired objects and the surrounding geological environment. Such features of the studied section structure often correspond to the reduced information content of the initial seismic data, i.e., weak in the reflection intensity. To effectively predict the prospects of oil and gas potential, it is known that the necessity to study various geological factors and geological and geophysical parameters of real environments which together and in close relationship causes the emergence and development of processes of oil and gas formation and accumulation. The necessity to improve the main stages of the predicting method for various geological indicators using high-resolution seismic data is substantiated. The use of a special (optimal) graph of seismic data processing ensures the preservation of undistorted seismic recording dynamics. The latter is important for three reasons: (1) target information is preserved (therefore, there is the possibility of extracting it); (2) the encrypted useful (target) information is in the record structure in the most simple and understandable form for extraction; (3) the record contains weak signals (which are most susceptible to distortion and are most vulnerable to irretrievable data loss) from acoustically low-contrast targets. Processing according to this graph provides minimal distortion of the seismic record dynamics and the possibility of extracting from it the geological indicators important for exploration and industrial prospecting. Already after the application of the optimal processing graph, the seismic traces are inverted in the distribution of the reflection coefficients and acoustic impedances for each of the traces used. The resulting acoustic model of the real environment is restored with a discretization step of seismic record over time and with an interval equal to the distance between the CDP laterally in the form of 2D sections or 3D cubes of reflection coefficients (RC) and acoustic impedances (AI). At this stage of research, the “no-well” version of seismic data inversion into sections and cubes of the AI and RC is also successfully applied. In a complex geological situation, it is often necessary to solve rather complex problems of oil geology using high-resolution seismic, GBS, and drilling. This is the geological substance components definition — lithological composition, zones of claying and wedging out of productive layers, reservoir development sites, possible non-anticlinal type traps, clarifying the boundaries of oil and gas deposits along the external contours of oil-bearing capacity, the nature and degree of saturation of reservoir rocks with fluids, etc. The issues of area forecasting based on seismic data of various geological indicators, including the density of hydrocarbon distribution, assessment of total hydrocarbon resources, and recommendations for further exploration and development of the research area are also relevant.

xviii

Introduction

In relation to the conclusions presented in this book, it is worth noting that all of them were based on many years of research by the authors. In a number of cases, the authors touch upon developments in the problem under consideration of domestic and foreign experts. The above problems are the reason for writing this book.

Contents

1

2

3

Research Direction: Brief Outline of Environmental Geological Indicators Using Reflected Wave Dynamic and Kinematic Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Main Research Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Book Content Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . .

. . . . .

1 1 6 13 14

Seismic Modeling of Wave Field Dynamic Parameters . . . . . . . . . . 2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of Elementary Boundaries and Strata . . . . . . . . . 2.1.1 Interference Contributions Matrix . . . . . . . . . . . . . . . . . 2.1.2 Contribution of Boundaries and Layers to the Results of Pseudo-Acoustic Transformations . . . . . . . . . 2.1.3 The Contribution of Boundaries and Layers to Instantaneous Dynamic Parameters . . . . . . . . . . . . . . . . 2.1.4 The Contribution of Boundaries and Strata to the Seismic Recording Deconvolution Results . . . . . . . . . . . 2.2 Quantitative Assessment of the Geological Section Parameter Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Lithological Component Contribution . . . . . . . . . . . . . . 2.2.2 Porosity Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Water Saturation Contribution . . . . . . . . . . . . . . . . . . . 2.2.4 Oil Saturation Contribution . . . . . . . . . . . . . . . . . . . . . . 2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

.

15

. .

17 20

.

21

.

24

.

28

. . . . . . .

33 35 41 42 42 47 47

Methods for Solving Inverse Dynamic Seismic Problems . . . . . . . . . 3.1 A Brief Review of Seismic Data Interpretation Mathematical Problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 53

xix

xx

Contents

3.2

Inversion Technologies to Refine the Seismic-Geological Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Acoustic Deterministic Inversion . . . . . . . . . . . . . . . . . . . 3.2.2 Synchronous (Elastic) AVO/AVA Inversion . . . . . . . . . . 3.2.3 Geostatistical Inversion Technology . . . . . . . . . . . . . . . . 3.2.4 Neural Networks in the Dynamic Interpretation of Seismic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 The Classification of Seismic Facies is One of the Important Seismic Data Interpretation Directions . . . . . . . 3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Velocity Determination . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Porosity Determination . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Terrigenous Rock Density Determination . . . . . . . . . . . . 3.3.4 Reflection Coefficient Ratio Determination . . . . . . . . . . . 3.3.5 Fluid Nature Determination . . . . . . . . . . . . . . . . . . . . . . 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Physical Basis for Finding the Properties of a Real Medium and Searching the Optimal Solution to the Inverse Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Optimization Method for Solving the Inverse Problem in the HRS-Geo Technology . . . . . . . . . . . . . . . 4.1.2 The General Scheme for Solving an Inverse Dynamic Problem and Interpreting Results in the HRS-Geo Technology . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Model Study of the Trace Inversion Algorithm . . . . . . . . 4.2.2 Application of the Inversion Algorithm for the Real Seismic Data . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Version 1: Special Processing Using HRS-Geo Technology . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4 Version 2: Gdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Version 3: JDow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.6 Version 4: KMGph . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.7 Version 5: MU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.8 Version 6: Prdgm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.9 Version 7: PtrAlnce . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.10 Version 8: SvMGph . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.11 Version 9: SbNGph . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.12 Version 10: Svginf . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58 59 61 63 67 72 74 76 77 77 78 78 88 89 97

98 103

109 110 111 120 126 139 143 146 149 152 155 157 160 163

Contents

xxi

4.2.13 Version 11: TNGph . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.14 Version 12: YtGph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Seismic Data Processing Using HRS-Geo Technology . . . . . . . . . 4.3.1 Useful Signals and Noises in Seismic Exploration, Noise Classification, and Suppression (Attenuation) of Signal Distortion Factors and Noises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Noise Suppression (Attenuation) . . . . . . . . . . . . . . . . . . . 4.3.3 Seismic Data Processing Using a Special Graph . . . . . . . . 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Processing and Automated Interpretation of Well Logging Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Physical and Geological Rationale for the Study of Sections According to Well Data . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Lithological Composition Determination . . . . . . . . . . . . 5.1.2 Porosity Determination . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Oil and Gas Saturation Determination . . . . . . . . . . . . . . 5.1.4 Evaluation of the Reservoir Filtration Properties . . . . . . 5.1.5 Classification of Pre-Jurassic Basement Sediments . . . . . 5.1.6 Assessment of the Productive Sediment Saturation Nature by Wells of the Studied Areas . . . . . . 5.2 Velocity and Elastic-Deformation Characteristic Determination from VSP Data Study of the Physical Property Spatial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Study of Geological and Geophysical Processes Taking Place in Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

163 168 174

176 178 180 191 192

. 197 . . . . . .

198 200 201 202 202 205

. 209

. 214 . 223 . 231 . 232

6

Elastic Wave Velocity and Velocity Gradient Fields for Heterogeneous Geological Media . . . . . . . . . . . . . . . . . . . . . . . . . 237 6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

7

Determination of Dependencies between Geological and Geophysical Characteristics of the Real Subsurface Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Multidimensional Dependence Determination Between Seismic and Well Field Geophysical Characteristics of the Section . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Petrophysical Equation System in the High-Resolution Seismic Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. 251

. 252 . 268 . 280 . 280

xxii

8

Contents

Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural Province . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan). . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Automated Processing and Interpretation of GBS Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Geological Structure Prediction Along Reference Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.3 Predicted Geological Indicators Based on HRS, GBS, and Area Drilling Data . . . . . . . . . . . . . . . . 8.1.4 Oil-Saturated Object Distribution in the Research Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.5 Evaluation of the HRS-Geo Technology Use Effectiveness on the Studied Promising Objects . . . . . . . 8.2 Detailed Study of Carboniferous, Upper and Middle Devonian Deposits (Orenburg Region) . . . . . . . . . . . . . . 8.2.1 Processing and Interpretation of GBS Materials . . . . . . . 8.2.2 Geological Structure Prediction along Reference Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.3 Prediction Geological Indicators According to HRS, GBS Data, and Area Drilling . . . . . . . . . . . . . . 8.2.4 Comparison of Oil Saturation Contours . . . . . . . . . . . . . 8.2.5 The Results of the Identified Object Opening . . . . . . . . . 8.3 Geological Indicator Estimation in Productive and Prospective Middle and Lower Carboniferous and Upper and Middle Devonian Sediments (Samara Region) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Automated Processing and Interpretation of GBS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Prediction of Geological Structure along Reference Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 The Results of Structural Constructions . . . . . . . . . . . . . 8.3.4 Volume Structural Tectonic Model of the Real Medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.5 Area Prognosis: Geological and Geophysical Indicators in the Sediments of the Kynov Horizon . . . . . 8.3.6 Area Prognosis: Geological and Geophysical Indicators in the Sediments of the Pashiisk Horizon . . . . 8.3.7 Area Prognosis: Geological and Geophysical Indicators in the Sediments of the Ardatov Horizon . . . . 8.3.8 Prospective Areas for Optimal Opening of Predicted Oil-Saturated Objects . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. 283 . 290 . 291 . 293 . 297 . 301 . 303 . 306 . 306 . 309 . 314 . 322 . 330

. 332 . 333 . 337 . 341 . 344 . 345 . 349 . 351 . 353 . 355 . 356

Contents

9

Examples of HRS-Geo Technology Used in Other Regions . . . . . . . 9.1 Structural-, Tectonical-, and Lithological-Shielded Oil-Perspective Object Identification in Reservoirs of the Upper Devonian (Timan-Pechora Province) . . . . . . . . . . . . . 9.1.1 Structure Features of the Incised Valley According to High-Resolution Seismic Data . . . . . . . . . . . . . . . . . 9.1.2 Channel Filling Features of the Buried River System . . . 9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective Objects in the Jurassic Complex and the Top of the Pre-Jurassic Formation Reservoirs (West Siberian Province) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Automated Processing and Interpretation of GBS Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Prediction of Geological Structure along Reference Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 Tectonic Disturbance Manifestations in the Research Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.4 The Results of Structural Constructions . . . . . . . . . . . . . 9.2.5 Area Geological Indicators According to HRS, GBS, and Drilling Data . . . . . . . . . . . . . . . . . . . . . . . . 9.2.6 Structure of the Upper Part of the Pre-Jurassic Basement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.7 Perspective Points for Optimal Opening of Predicted Oil-Prospective Objects . . . . . . . . . . . . . . . . . 9.3 Composition and Property of Oil-Perspective Strata Prediction Using High-Resolution Seismic Data (Saudi Arabia) . . 9.3.1 Geological Structure of the Study Area . . . . . . . . . . . . . 9.3.2 Processing and Interpretation of Deep Well GBS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.3 Prediction of Geological Indicators in the Sublatitudinal Direction . . . . . . . . . . . . . . . . . . . . . . . . 9.3.4 Prediction of Geological Indicators in the Submeridian Direction . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.5 Afterword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxiii

. 359

. 360 . 362 . 370

. 371 . 372 . 373 . 378 . 380 . 381 . 385 . 389 . 391 . 392 . 395 . 397 . . . .

403 405 406 406

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

Chapter 1

Research Direction: Brief Outline of Environmental Geological Indicators Using Reflected Wave Dynamic and Kinematic Parameters

Abstract A brief essay on the research direction is given, in which the features of the various geological factors influence on the distribution of the studied physical quantities – on the acoustic and dynamic characteristics of the seismic record are generally emphasized. The chapter deals with the specific features of changes in the dynamic and kinematic characteristics of seismic waves caused by the presence of hydrocarbon deposits in the real geological environment, the use of the corresponding anomalies of the seismic wave field for predicting (indicating) hydrocarbons. It is shown that the acoustic parameters in real media are variable and have a combined effect on the predicted parameters (primarily on porosity and oil and gas saturation) through the dynamic characteristics of the record. The authors largely use the experience of numerous researchers in the field of predicting the parameters of a geological section from seismic data, indicating hydrocarbons, mathematical modeling of the reflected waves parameters, various modifications of methods for solving the inverse dynamic problem of seismics, linking high-resolution seismic data with geophysical well surveys (GBS) ones, etc. In all these areas, sections that review the results of various authors' works and studies performed by the authors of the book are indicated.

1.1

The Main Research Areas

Studies aimed at extracting from the seismic data the maximum possible amount of geological information about the studied section, as is known, are extremely relevant at present. On the one hand, this is due to the diversity of the wave process manifestation with a complex interference recording structure, which is excited by sources of elastic oscillations and is formed in a real thin-layer medium directly during seismic exploration. On the other hand, there is necessity to create specialized software and methodological support, with the help of which it is possible to adequately implement the integration of seismic data and GBS (the results of interpretation of land (marine) seismic exploration data and field geophysics should be reduced to some adequate scale of study). There is a need to take into account the main features of the geological environmental model formation, as well as to take © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_1

1

2

1 Research Direction: Brief Outline of Environmental Geological Indicators. . .

into account the basic principles of theories, signal conversion, noise immunity, and information theory (conservation laws of information quantity and quality in various signal and interference transformations), in the process of solving nonlinear seismic tasks. This section highlights the development and use of specialized software and methodological support on model and factual materials, which is implemented to process and interpret seismic data using high-resolution seismic techniques, and substantiates the need for an integrated approach to solving the problem of studying and predicting complex, small-sized, and deep-seated local physical and geological heterogeneities which are associated with hydrocarbon traps. The seismic method, like any geophysical method of measuring physical magnitudes, has certain limits of applicability, which are determined by many factors, ranging from field observation methods, survey conditions, mathematical transformations of observation materials to quantitative and qualitative assessments of various properties and composition of the geological environment under study. The possibilities and limitations of the seismic method for the direct detection of oil and gas deposits are known to be based on the study of their seismic images (a set of dynamic and kinematic parameters whose values in the contour zone of the deposit differ from the values of these parameters outside one). The lithophysical properties (velocity, density) of saturated reservoir layers and attenuation of elastic seismic waves are influenced by a set of factors such as the physical properties of fluids, the structure of the pore space, the lithological-facies composition of reservoir rocks, and thermodynamic conditions of subsurface deposits. The formation of hydrocarbon deposits, as is known, is accompanied by the following changes in the parameters of the real medium: a decrease in the propagation speed of longitudinal seismic waves; reduction in rock bulk density; increased absorption of elastic waves; the formation of horizontal reflective boundaries corresponding to the water and gas-oil contacts; and an increase in anisotropy (Mikhaltsev A.V., Mushin I.A., Pogozhev V.M.). The limits of change of these characteristics according to different authors are different: from 4% for oil-saturated and 10% for gas-saturated rocks (Trapeznikova N.A., Grebneva I.L., Blumenzweig V.I.) to 8–18% for oil and 9–40% for gas deposits (Averbukh A.G., Gelfand V.A., Gogonenkov G.N., etc.). This is displayed in the form of corresponding anomalies of seismic wave fields, among which are (1) an increase (or decrease) in the amplitudes of reflections from reservoir boundaries, sometimes accompanied by a change in polarity; (2) changes in the frequency composition of reflections due to the dependence of absorption on frequency and changes in the frequency response of the reservoir; (3) an increase in the timescale of the reservoir thickness; (4) the wave regularity deterioration; (5) regular distortions of the effective velocities to the reflecting horizons located below the deposit (Berezkin V.N., Kirichek M.A., Kunarev A.A.); etc. There are a large number of works by other authors (Garanin V.A., Medovsky I. G., Ballakh I.Ya., Davydova L.N., Sergeev L.A., Churlin V.V., Afanasyeva I.A., Lyakhovitsky F.M., Rapoport L.I., Avchan G.M., Mustafaev K.A., Ivanchuk A.M., Sibiryakov B.P., Zolotarev P.P., Rudnitskaya D.I., Rapoport M. B. and others) that

1.1 The Main Research Areas

3

provide materials, showing that in the volume of homogeneous reservoirs as a result of formation water replacement with hydrocarbons in the deposit, there occurs a decrease in the average density by 0.10–0.25 g/cm3; decrease interval velocity by 10–20%; and an absorption coefficient increase at the frequency band of 30–70 Hz by an order of magnitude and higher. Using the experience of a number of researchers (Averbukh A.G., Sheriff R., Geldart L., MacQuillin R., Bacon M., Zalyaev N.Z., etc.), the authors, along with the development of methods for predicting lithological composition and reservoir properties of the studied productive layers, developed methodology for the hydrocarbon indication and the corresponding software to it. As a data source, this technique was focused on the use of the AI and RC traces, formed after the seismic record inversion for the corresponding sections or cubes (resulting from the IDSP solution) and on the set of petrophysical properties of the well section being studied. A detailed study of the obtained results of hydrocarbon indications from seismic data and GBS materials reveals important features that have a strong influence to the obtained results of hydrocarbon prediction. A significant obstacle in this matter was the initial dynamics of the seismic record, in which the identification information on the most important geological indicators, such as reservoir properties and oil and gas saturation, can be either preserved or not. The authors devoted Chap. 2 to this question, based on the development and use of special modeling of the reflected wave dynamic parameters, considering the contribution of elementary boundaries and strata and the assessment of the contribution of various geological indicators to the dynamics of the interference wave field. The emergence of new interpretation methods, using the achievements of modern methods of computational mathematics and mathematical statistics, is reduced, as is well known, to solving inverse problems of mathematical physics. Researches of Gamburtsev G.A., Riznichenko Yu.V., Berzon I.S., Epinateyva A.M., Petrashen G.I. et al., who laid the theoretical foundations of seismic prospecting, made it possible to formulate the inverse dynamic seismic exploration problem for a wave field containing primary and multiple waves. A detailed development of the inverse dynamic problem is given in the works of Alekseev A.S., Kunetz G., Mikhaylova N. G., Nikolsky E.V., Antonenko O.F., and others. The most complete and comprehensive problem of solving an inverse dynamic seismic problem was studied by Alekseev A.S. in relation to the fields of longitudinal and transverse waves [1]. He showed, in particular, that the final result of solving such a problem in the field of longitudinal waves is the product of velocity and bulk density (acoustic impedance), and for transverse waves, a separate estimate of bulk density and velocity of transverse waves is possible. Various modifications of the inverse dynamic problem solution are discussed in the works of Gogonenkov G.N., Kozlov E.A., Bamberger A., and others. All methods brought to a numerical solution for simplified media models are obtained. In all these works, a horizontally layered medium and excitation by a plane wave, normal to the bedding boundaries, are assumed. The instability of the solution is also emphasized, and various methods for the regularization of solutions are proposed. However, even after regularization, the proposed algorithms showed acceptable

4

1 Research Direction: Brief Outline of Environmental Geological Indicators. . .

results on theoretical models with a low noise level and were unsatisfactory when applied to experimental data. The authors devoted a brief review to methods for solving inverse dynamic problems (Chap. 3). In general, the topic of mathematical problems of seismic data interpretation for detailed restoration of environmental parameters based on solving systems of elasticity theory equations, formulated both in the spectral and in the time domains, is very extensive and rather complex. To solve this kind of problem, as is well known, which is incorrect in the classical sense (or conditionally correct according to A.N. Tikhonov), distinguished by nonuniqueness and instability of the solution, the authors have found a numerical solution that ensures optimal minimization of the developed objective functions. At the same time, the authors developed numerical algorithms that formed the basis for the creation of HRS-Geo technology components (Chap. 4 and Sect. 4.3). Examples of solving inverse dynamic problems on model and real materials are given in Sect. 4.2, as well as in Chaps. 8 and 9. For the complex geological interpretation of high-resolution seismic data (as well as for traditional seismic data), GBS and drilling data are usually used. For processing and automated interpretation of these standard well logging GBS methods, the authors used the method of functional transformation of geophysical parameters by integrating them into information systems [2–4]. This ensures the continuity of data processing and analysis, the quantitative interconnection of all geophysical parameters on the basis of their genetic relationships. According to the set of these parameters located in a certain system, the material composition, porosity, bound water content, and useful reservoir capacity are established; the oil and gas saturation of reservoirs is estimated; and a number of very important physical characteristics of the geological section are calculated (Sect. 5.1). On this basis, the construction of lithologic-stratigraphic columns and detailed geoacoustic models in vertical sections of the well section is carried out. The GBS interpretation system is supplemented with an important software and technology unit, on the basis of which the optimal coordination of the section physical parameters extracted from the GBS data with the corresponding high-resolution seismic data is carried out. The data processing of vertical seismic profiling (VSP and PM VSP) is performed by the authors using the SKOR software package to determine the velocity characteristics of various wave types (longitudinal, transverse, exchange), effective elasticdeformation parameters, and the stress state of the real medium in situ conditions of rock mass. The obtained velocity parameters of the section are most often used by the authors for structural and complex interpretation of high-resolution seismic data (Sect. 5.2). The peculiarity of the complex interpretation of the data obtained with the use of high-resolution seismic technology (HRS-Geo) is to find such a set of geological and geophysical parameters, which in specific seismogeological conditions could be effectively used to study and analyze the complex of geological patterns that cause the spatial placement of the desired productive objects and ultimately determine the spatial location of oil and gas objects (oil and gas traps); oil and gas accumulation

1.1 The Main Research Areas

5

zones; local heterogeneities, characterized by a certain set of composition and properties; etc. The application of high-resolution seismic technology (HRS-Geo) is ultimately focused on obtaining results that characterize the distribution in the studied section of the lithological composition and reservoir properties of rocks. Under favorable seismic and geological conditions, it is possible to determine the nature and degree of reservoir rock fluid saturation. The process of extracting the necessary information about the geological parameters from seismic data appears as a whole as a process of complex geological interpretation of geophysical data and substantiation of the geological structure of the specifically studied oil prospective object. This process includes both the analysis of various geological and geophysical data (primarily using high-resolution seismic data) and a number of automated procedures performed in a certain sequence. The task of complex interpretation is not to assess any single sign of oil and gas potential, but to analyze all information about the environment, in other words, to construct a set of interpretational models of the environment that reflect all those features of the geological structure and geological history that collectively determine oil and gas prospects. One of the ways to implement this approach is to assess the dependencies (mathematical models of the relationship) between the geological and geophysical characteristics of the section. The basis for their study is the idea of the geological environment model as a complex object, the geophysical parameters of which are functions of many variables (Sect. 7.1). The nature of the distribution of measured geophysical quantities, as noted above, is influenced by lithology, the structure of the pore space, the presence of clayey material in the rock and the nature of its distribution, temperature and pressure conditions, the nature of the saturating fluid, and other factors. The correlation dependences determined in such conditions allow us to calculate the approximate values of some section parameters from the values of other ones, especially if their possible deviations from the true values are within the limits of the environmental parameter variation coefficients. It should be noted here that the identification of dependencies between the geological and geophysical parameters is one of the components of the solution to predicting the problem in a geological section using seismic data (Sect. 7.2). In general, various geological factors affecting the distribution nature of the physical quantities under study are, by their nature, variable and have a combined effect on the prediction parameters. The method of studying the influence of geological indicators on the acoustic and dynamic characteristics of seismic record and determining the relationships between them is a good physical and geological basis for analyzing the interference structure of the wave field and studying various combinations of the composition and properties of the studied section in relation to seismic characteristics. On this basis, it is possible to establish the specific features of the seismic data interpretation.

6

1.2

1 Research Direction: Brief Outline of Environmental Geological Indicators. . .

The Book Content Overview

The book deals with a wide range of processing and interpretation problems of seismic data related to the general direction of the research on “high-resolution seismics.” The research is aimed at extracting the maximum possible amount of information about the real geological environment from the seismic data. The results of a complex interpretation of the seismic observations materials obtained using the HRS-Geo technology developed by the authors in various seismic geological conditions for the different regions of Russia and abroad are presented. These results are characterized by increased detail in studying the features of the environment geological structure. In complex geological conditions, the lithological composition and reservoir properties of rocks are predicted. In favorable seismogeological conditions, the nature and degree of reservoir rock fluid saturation is determined. Chapter 1. A brief outline of the research direction. Here, in general terms, the features of the influence of various geological factors on the nature of the studied physical quantity distribution on the acoustic and dynamic characteristics of the seismic record are emphasized. By their physical and geological nature, the acoustic parameters in real environments are variable and, through the dynamic characteristics of the recording, have a combined effect on the predicted parameters (primarily on porosity and oil and gas saturation). The authors rely to some extent on the experience of a number of researchers in the field of predicting the parameters of a geological section from seismic data, hydrocarbon indication, mathematical modeling of the reflected wave parameters, various modifications of methods for solving the inverse dynamic problem of seismics, linking high-resolution seismic data with data from geophysical borehole surveys (GBS), etc. Chapter 2. Seismic modeling of the wave field dynamic parameters. In this section, the question of the phenomenon of all-encompassing reflected wave interference, which manifests itself both on real and model seismic records, is in detail discussed. A detailed study of the geological parameter influence on its acoustic characteristics and the seismic record dynamics shows that the rather complex structure formation of the seismic wave field is affected by the interference of local components of the seismic signal from the overlying acoustic heterogeneities. Such “seismic interference” in a certain sense is a reflection of the corresponding “interference of geological indicators,” which is also very complex in its interference structure. The result of the “interference of geological indicators” can be directly seen in the outcomes of automated GBS data processing, where each point of the real geological section model (especially on the lithologic and stratigraphic columns in the productive intervals) contains specific quantitative information about the lithological composition, reservoir properties, and oil and gas saturation of reservoir rocks. These indicators, in turn, influence the dynamics of the seismic record. In Sect. 2.1, the features of the reflected wave dynamic characteristics are discussed, considering the contribution of elementary boundaries and strata to the results of the “input” wave field, pseudo-acoustic transformations, instantaneous dynamic parameters, seismic record deconvolution, etc. In Sect. 2.2, the contributions of lithology,

1.2 The Book Content Overview

7

porosity, and oil and gas saturation to the seismic record and to the results of its various transformations are evaluated. The information content of the elements of a thin-layer section is analyzed by quantifying the contribution of elementary boundaries and strata to the interference wave field. The contributions of local responses from lithology, porosity, water, and oil and gas saturation are estimated using the “interference contribution matrix” (ICM). Chapter 3. Methods for solving inverse dynamic problems of seismics. Section 3.1 provides a brief overview of the mathematical problems of interpreting seismic data. The conditions for the existence, unambiguity, and stability of the inverse problems solution are briefly discussed, since they are among the so-called incorrect problems. When processing approximate data obtained from the experiment, small changes in the input data can correspond to any large changes in the solution. In the works of A. S. Alekseev, it is noted that “. . .the method of inverse dynamic problems, even if we do not keep in mind its practical use, can already serve as a new tool in theoretical seismics for analyzing the general quantitative relationships between the properties of wave fields and the characteristics of the studied medium structure.” We consider a statement and an example of solving inverse dynamic seismic problems, where the functions Vp(z), Vs(z), and ρ(z) are uniquely determined in the spectral representation of solutions to direct problems and the subsequent inversion of such representations. The foundations of the theory and practice of studying inverse mathematical physics problems are laid and developed by the fundamental works of many modern scientists - domestic and foreign authors. In Sect. 3.2, the authors give a brief overview of various inversion transformation technologies, the choice of which depends on the quantity and quality of the observed geological and geophysical information, as well as on the set geological tasks for determining the composition and properties of the rocks under study. Numerous practical methods and techniques of inversion are very different from the so-called classical methods of IDSP, which are usually formulated as inverse problems of mathematical physics. According to the type of seismic data used, the inversion of this data is realized both before and after data stacking. The authors used the materials placed in the works of many domestic and foreign researchers to form this subsection. Examples of the results obtained by these researchers are given, and the features of the application of various inversion transformations are highlighted. In Sect. 3.3, an original method of indicating hydrocarbons based on the use of dynamic parameters of seismic recording is considered. To date, it remains relevant, but at the same time, it has not been fully studied and its wide application is not noted in practice. The possibility of using the dynamics of seismic data for direct search of oil and gas using the average time equation and wave field amplitudes is considered. The essence of the method is the direct use of the mean-time equation, which describes the dependence of the elastic wave velocity in sedimentary rocks on porosity, the wave velocities in the fluid and solid phase, and the amplitudes of reflected waves from the reservoir. A rather “hard” assumption is made that the amplitudes of the seismic recording of the section are proportional to the values of the reflection coefficients. It is necessary in such a problem to determine the nature of the fluid by the ratio of the reflection coefficients, to determine the type and volume

8

1 Research Direction: Brief Outline of Environmental Geological Indicators. . .

of the collector. At the same time, the limitations concerning the application of the mean time equation and a number of other factors due to different types of porosity in the study of complex reservoirs are also noted. One of the main disadvantages of this approach is the use of interference wave fields as input data for solving the inverse problem. The exclusion of such a distorting factor as wave interference from the seismic data leads to a significant increase in the reliability and detail of predicting geological indicators in the search for oil and gas deposits. In addition, modern approaches and methods of inversion do not work without well data as a priori information. Chapter 4. Solving the inverse dynamic problem of seismics in the HRS-Geo technology. The main features of the software and methodological implementation of the method developed by the authors and the methodology of its application, made in the form of the HRS-Geo technology, are considered. The solution of the IDSP is found by the optimization method, which consists in the selection of models of the AI and RC to a given structure of the wave field (WF) according to the known formulas for solving the direct problem for calculating the seismic wave field (Sect. 4.1). In this case, the convolutional model algorithm is used, in which it is possible to take into account the noise level, the residual background of multiple waves, and the regularization factor. In general, the study of real media is focused on the use of dynamic features of the seismic record, the implementation of its maximum possible resolution, namely, the construction of 2D sections and 3D cubes of effective acoustic impedance (AI) and reflection coefficients (RC), which have a vertical resolution equal to the sampling step of the seismic record along time. The problems of IDSP solution optimization are based on the development of a objective functions system, in which various types of residuals between real and model data are iteratively calculated and analyzed (Sect. 4.1.1). The inversion procedure is applied to the stacked seismic data separately and is not a classical full-wave inversion. Therefore, it is not critical to computing resources, although it requires some computing power. At the same time, although the method is not a full-wave inversion, the algorithms developed by the authors can be used as an integral part of the full-wave inversion to significantly improve its results. This technology can be used separately for recording longitudinal P waves, transverse S waves, and exchange PS waves, VSP observations, etc. There is experience in applying the technology to the original field seismic data. One of the main advantages of the technology over modern inversion methods is that it is not tied to borehole data. The detail of the section study, i.e., the vertical resolution of the inversion results, in the complete absence of well data, is the sampling step of the seismic survey (in 1 or 2 ms, which correspond to a depth of 3–6 m). Section 4.1.2 provides a general scheme for solving the inverse dynamic problem of seismics and interpreting the results in the HRS-Geo technology. In Sect. 4.2, the correctness of the chosen approach to solving the IDSP is evaluated, and the reliability and accuracy of the acoustic model restoring results are determined. Examples of solving the inverse dynamic problem of seismics on test (Sect. 4.2.1) and real (Sect. 4.2.2) materials are given. It is shown which factors have a significant impact on the results of the reconstruction of a detailed thin-layer

1.2 The Book Content Overview

9

medium model and the methods of their accounting. Testing of the IDSP procedure is implemented on real materials obtained by using different processing graphs (seismic data recorded only on one of the seismic profiles for one of the Western Siberia areas). For all processing options, an objective comparative analysis of the obtained energy spectra, the signal-to-noise ratio, the vertical and horizontal resolutions of the original wave field, the results of reconstructing the medium model, and the effectiveness of the research results as a whole is given. Section 4.3 discusses the features of seismic data preprocessing using the HRSGeo technology, which ensures the maximum possible preservation of primary seismic waves against the background of various non-useful regular waves and random noise. Taking into account the presence of weak seismic signals in the seismic record, local responses associated with such important geological indicators as porosity and oil and gas saturation, a complex processing of seismic data is performed according to some optimal graph. In the processing graph, only those processing system procedures are used that allow maintaining the dynamics of the seismic record, which is largely adequate to the actual distribution of acoustic heterogeneities of the studied geological section. Chapter 5. Processing and automated interpretation of well geophysical survey data. Section 5.1 discusses the specifics of the methodology for processing and interpreting data from standard well logging (GBS) methods. This method implements a method of functional transformations of geophysical parameters by integrating them into information systems. In addition to the continuity of data processing and analysis, this ensures the quantitative interconnection of all geophysical parameters based on their direct relationships, as well as the integrated use of data and the results of solving individual problems. The physical prerequisite for solving such problems is the different sensitivity of different geophysical parameters to changes in lithological and reservoir properties. When normalizing the readings of individual methods in rocks of one type, according to the lithology and nature of the fluid, there is a characteristic discrepancy in the readings for rocks of another type. The main stages of GBS data processing are (1) determination of the lithological composition; (2) determination of porosity; (3) determination of oil and gas saturation; and (4) assessment of the filtration properties of the studied reservoirs. The results obtained are used to study the thin-layer internal structure of prospective reservoirs and to predict various geological and geophysical parameters by combining borehole data and high-resolution seismic materials using software tools of the HRS-Geo technology. Section 5.2 describes the methods and results of studying the distribution of physical parameters in space and their changes in time, i.e., the study of geological processes occurring in the environment. This section presents the results of processing data of vertical seismic profiling (VSP) by the polarization method (PM VSP), obtained on the basis of the development and use of the SKOR software package for determining the velocity characteristics of various wave types (longitudinal, transverse, exchange), effective elastic-deformation parameters, and the stress state of the real environment in situ conditions of a rock mass. Based on this, a reliable relationship is traced between the physical parameters of rocks found with the help of the SKOR software package and the features of the geological

10

1 Research Direction: Brief Outline of Environmental Geological Indicators. . .

structure of the section. At the same time, it is possible to form a set of seismic indicators that are directly related to the material composition of rocks and their saturation. The authors also proposed a method for studying the stress state of rocks in the areas of possible earthquakes, which is based on the use of information kinematic and dynamic parameters of the seismic wave field obtained using one of the modifications of the polarization method (PM) modifications of seismic exploration—its borehole modification of vertical seismic profiling (PM VSP). At the same time, the method of the VSP PM observations is supplemented by special regime (monitoring) measurements in the well and specialized processing and interpretation of the obtained observation materials (Sect. 5.3). A system for processing and interpreting data for monitoring the stress state of a real geological environment and an example of its use on the materials of the Tengiz oilfield are presented. An example is a method for studying geological processes that occur over time. The described technique (technology) can be effectively used for direct prediction of changes in the stress state of rocks in the areas of possible manifestations of artificial (man-made) or natural earthquakes. Chapter 6. Velocity and velocity gradients fields of elastic waves for heterogeneous geological media. The chapter presents materials that characterize the separation of lithological and structural-tectonic features of the medium using the modulus of the true velocity gradient vector and its angle in the plane of the seismic profile. In this case, the value of |grad V (x, z)| is due to changes in the lithological composition of rocks in the studied medium, and the angular function θ (x, z) is related to the structural features of the section (the geometry of the medium). The article describes the features of the methodology for constructing velocity fields and their gradients based on the use of data from the seismograms velocity analysis and presents the results obtained on one of the seismic lines of the CMP method. The developed methodology for studying the velocity fields and velocity gradients of seismic waves allows us to extract specific quantitative information about the profile from the CMP materials in a convenient and easily interpreted form. Based on the use of this technique, it is shown that it is possible to identify local heterogeneities of the real environment associated with the horizontal variability of the lithology and tectonic faults. There is a close relationship between the kinematic and dynamic characteristics of the reflected waves, i.e., between the velocity and velocity gradient fields of elastic waves and the acoustic impedance and reflection coefficients of the real geological environment. Chapter 7. Determination of the dependencies between the geological and geophysical characteristics of the real medium. In the process of performing seismic studies, it is important to determine the assessment of the geological informativeness of various dynamic parameters based on the establishment of direct and inverse relationships between the seismic and well field geophysical characteristics of a thinlayer section and their subsequent use in solving various predictive tasks of seismic exploration. In Sect. 7.1, a package of application programs for statistical processing of experimental data was preselected for solving this problem and as such data, a set of geological and geophysical characteristics of the inter-salt stratum of one of the wells of the Pripyat trough. Using the programs of step-by-step regression, partial

1.2 The Book Content Overview

11

correlation, and multivariate regression, and direct and inverse multivariate dependencies are found, which allow us to evaluate the relationships of each dependent variable with all other independent variables selected by the step-by-step P2R procedure as their optimal set. A decrease in the dimension of the studied feature space is shown without compromising the informativeness of the variables. Strong, significant, and moderate dependencies have been established. The materials are also presented in a graphical form with the possibility of efficient editing and evaluation of the approximating curve shape. The considered method of studying the influence of geological indicators on the acoustic and dynamic characteristics of a seismic record and determining the relationships between them is a good physical and geological basis for analyzing the extremely complex interference structure of the wave field and studying the various combinations of the composition and properties of the studied section in relation to the seismic characteristics. On this basis, it is possible to solve the problem of estimating the error of the reflected wave stratigraphic tying to geological horizons and to study the process of displaying weak seismic signals in the structure of the seismic record dynamics. As shown in Sect. 7.2, at the final stage of the observation material complex interpretation, in accordance with the technological scheme of dynamic data processing (which widely uses the procedures of the HRS-Geo technology), the composition and properties of oil-bearing deposits are predicted. We consider one of the algorithms and the results obtained for predicting various geological indicators based on the use of high-resolution data in the form of 2D sections or 3D cubes of effective RC and AI. A special system of petrophysical equations is developed to determine the lithological composition and the fluid saturation nature of reservoir rocks. With a comprehensive approach to the analysis of the results obtained, the extraction of the maximum possible amount of geological and geophysical information from seismic data is achieved for the purpose of searching for promising oil and gas objects, evaluating their characteristics for calculating hydrocarbon reserves and resources and optimal placement of exploration and production wells, and in general for justifying the development strategy of the industry. Before the direct prediction of the desired parameters, the investigated section interval is compared with the physical parameters previously obtained for this depth interval from GBS data (in the wells section, if such are available in the study area), and then the desired geological and geophysical indicators on the lithological composition, reservoir properties, and oil saturation of reservoir rocks are determined for each point of the target depth interval (reservoir). Starting from Chap. 8 (Detailed interpretation of high-resolution seismic data in the Volga-Ural province), the authors present the results of a comprehensive interpretation of seismic observations obtained using the developed HRS-Geo technology in various seismogeological conditions for different regions of Russia and abroad. The materials of Chaps. 8 and 9 represent not only the results of the highresolution seismic technology application in the construction of medium models but independent reference data when describing the structure of oil-bearing objects

12

1 Research Direction: Brief Outline of Environmental Geological Indicators. . .

in various geological conditions, which can be used for a detailed study of analogous deposits in other regions of the world. In the conditions of the Volga-Ural oil and gas province, the prospects of oil and gas potential are largely associated with the deposits of the terrigenous Devonian, which is confirmed by the presence of a significant number of oil and gas fields in them. To assess the reliability of oil-promising objects identified within one of the areas of the Volga-Ural Province, an independent analysis (with the participation of the customer’s specialists) of the convergence of the drilling results and the results of the work presented by the authors was carried out (Sect. 8.1). The convergence analysis of the results was performed separately for the deposits of the Kynovian and Pashiisk horizons. The obtained coefficient of convergence of the results was at least 0.8. In one of the areas of the Orenburg region in the eastern part of the Kama-Kinel system of uncompensated troughs, a detailed study of Carboniferous, Upper, and Middle Devonian deposits was carried out (Sect. 8.2). In the course of the performed research and further testing of the identified oil-promising objects, data confirming the prediction results made using the HRS-Geo technology were obtained. In the Samara region, the assessment of geological indicators in productive and prospective deposits of the Middle and Lower Carboniferous and Upper and Middle Devonian was performed (Sect. 8.3). The authors limited themselves to a comparative analysis of the obtained prediction results of various geological indicators for different horizons by combining oil saturation contours and a comprehensive analysis of the considered forecast geological and geophysical parameters. In this case, all the obtained prediction information for the target intervals of the section was used. On this basis, the optimal areas for further exploration are planned. Chapter 9 provides examples of the HRS-Geo technology use in the TimanPechora province, Western Siberia, and Saudi Arabia. Due to the in-depth complex interpretation using the HRS-Geo technology, GBS, and drilling data, a number of new promising areas in reservoir formations were identified; the structure of the geological environment, the contours of the oil content, and the oil prospects of the real medium were significantly refined. On the territory of the Timan-Pechora province (Sect. 9.1), oil-promising objects of structural, tectonically, and lithologically shielded types in the Upper Devonian reservoir formations were identified. The features of the structure and development of buried river systems (paleoriver incisions, boundaries of erosion sides, structures of internal sediment filling of paleochannels, etc.) are studied. In order to improve the efficiency of the predicting of acoustic heterogeneities, such as ancient paleochannels, elements of their filling structures, channel incisions of river paleovalleys, erosion depths, etc., a comprehensive study of a variety of geological information about prospective objects, including the kinematic and dynamic characteristics of reflected waves, which are restored only using the method of high-resolution seismics, is necessary for complex diagnostic features. On the territory of Western Siberia (Sect. 9.2), oil-prospective objects of structural, tectonically, and lithologically shielded types in reservoir layers of the Jurassic

1.3 Summary

13

sediment complex and the top part of the pre-Jurassic base were studied. Using a whole set of forecast geological indicators within the combined oil saturation contours for the considered productive horizons and reservoirs, prospective points for the opening of oil and gas deposits are established. The maximum ratio of the average values of oil saturation, porosity, oil-saturated thicknesses, and oil saturation coefficients, as well as the resources of hydrocarbon raw materials, was taken into account when determining promising objects of lithologically shielded and structural-lithological types. For oil-prospective objects of structural type, in addition to the specified forecast geological and geophysical parameters, the structural factor was also taken into consideration. Section 9.3 provides an example of solving the petroleum geology problem on the basis of seismic observations recorded on two intersecting seismic profiles in complex seismic-geological conditions of the Unayzah formation deposits (Saudi Arabia). On the obtained sections of effective acoustic impedance, average values of oil and gas saturation in the trace-by-trace form and predicted litho-fluid columns, relatively long and intense oil saturation of the desired sections in the sediments of the Unayzah A, Unayzah B, partially Unayzah A siltstone, and Unayzah C horizons, are quite confidently highlighted. In addition to the prediction indicators, the structural and tectonic factor that controls the preservation of the local accumulation of hydrocarbons is confidently manifested here. It should be noted that as a result of further research (after predicting the geological indicators performed by the authors, based on the materials of two seismic profiles), the oil company Lukoil Overseas, which conducted geological exploration here, announced the discovery of a hydrocarbon field with resources of more than 100 million tons of conventional fuel (million tons of cu.t.) (www.lukoil-overseas.ru).

1.3

Summary

A brief analysis of the possibilities and limitations of the seismic method for the direct detection of oil and gas deposits, which, as is known, are based on the study of their seismic images (a set of dynamic and kinematic wave parameters, the values of which in the contour of the deposit differ from ones in the outside contour zones), is given. The specific features of changes in the dynamic and kinematic characteristics of seismic waves caused by the presence of hydrocarbon deposits in the real medium and the use of the corresponding anomalies of the seismic wave field for predicting (indicating) hydrocarbons are discussed. Using the experience of numerous researchers, the authors show that along with significant recent advances in the application of seismic exploration for the search and exploration of oil and gas deposits, there are a number of problems related primarily to methods for solving inverse dynamic problems of seismics. The latter circumstance was the justification and reason for the development and application of the HRS-Geo technology, which provides the prediction of lithology, porosity, and oil and gas saturation.

14

1 Research Direction: Brief Outline of Environmental Geological Indicators. . .

References 1. Alekseyev, A. S. (1967). Obratnyye dinamicheskiye zadachi seysmiki (Inverse dynamic seismic problems). Book: “Nekotoryye metody i algoritmy interpretatsii geofizicheskikh dannykh”. Nauka., 9–84. 2. Zalyayev, N. Z. (1981). Izucheniye razrezov slozhnogo litologicheskogo sostava po dannym geofizicheskikh issledovaniy v skvazhinakh (Study of complex lithological composition according to geophysical surveys in wells). Region., razved. i promysl. geofizika. VIEMS. Review., M, 51. 3. Zalyayev, N. Z. (1990). Metodika avtomatizirovannoy interpretatsii geofizicheskikh issledovaniy skvazhin (Technique of automated interpretation of well logging) (p. 142). Izd. Universitetskoye. 4. Trofimov, V. L., Khaziev, F. F., & Milashin, V. A. (2007). Avtomatizirovannaya obrabotka i interpretatsiya dannykh GIS dlya obnaruzheniya nefteperspektivnykh obyektov metodami vysokorazreshayushchey seysmiki (Automated processing and interpretation of well logging data for the detection of oil-prospective objects by high-resolution seismic methods). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 54–66.

Chapter 2

Seismic Modeling of Wave Field Dynamic Parameters

Abstract On the basement of seismic modeling of the wave field dynamic parameters, the issue of the reflected wave comprehensive interference phenomenon, which manifests itself both on real and model seismic records, is revealed in detail. For the first time, the concept of an elementary boundary and an elementary layer studied by high-resolution seismics for the investigation of a thin-layer geological environment is introduced. It is shown that the formation of a rather complex seismic wave field structure is influenced by the interference of seismic signal local components from the overlying acoustic heterogeneities. Such “seismic interference” in a certain sense is a reflection of the corresponding “geological indicator interference,” which is also very complex in its interference structure. The result of the “interference of geological indicators” can be observed on the results of automated geophysical borehole survey (GBS) data processing, where each point of the real geological model (especially on the lithological and stratigraphic columns in the productive intervals of the section) contains specific quantitative information about the lithological composition, reservoir properties, and oil and gas saturation of reservoir rocks. In turn, they influence the dynamics of the seismic record. The features of the reflected wave dynamic characteristics are considered, taking into account the contribution of elementary boundaries and strata to the results of the “original” wave field, pseudo-acoustic transformations, instantaneous dynamic parameters, deconvolution of the seismic record, etc. The contributions of lithology, porosity, and oil and gas saturation to the seismic record and to the results of its various transformations are evaluated. The information content of the thin-layer section elements is analyzed on the basis of a quantitative assessment of the elementary boundaries’ and layers’ contribution to the interference wave field. The contributions of local responses from lithology, porosity, water, and oil and gas saturation using the “interference contribution matrix” (ICM) are estimated.

Interpreting seismic data often have to deal with the problem of adequate display of relatively thin layers of geological section or their sets into the appropriate intervals of seismic time sections or seismic recording cubes. It is particularly difficult to determine the location of the display in the seismic wave field of specific time intervals of the studied section, directly related to the productive (oil and gas © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_2

15

16

2 Seismic Modeling of Wave Field Dynamic Parameters

saturated) or potentially productive intervals of the geological section. It is obvious that the results of predicting geological indicators (primarily, such as lithological composition, reservoir properties, oil and gas saturation), as well as their qualitative and quantitative characteristics, largely depend on an adequate (correct) assessment of the image location of the latter. In practice, a number of aspects are often not considered that have a significant impact on determining the display location of target intervals of the geological section under study into corresponding intervals of the seismic wave field. The question of the existing difference in such an important characteristic as the vertical resolution of the seismic wave field data and the resolution of these standard logging methods (GBS) is almost not considered. This difference, as is well known, can be estimated at
1–2 orders of magnitude (depending on the seismic record frequency composition and the research depth), and it is not in favor of the seismic method. It is possible to state essentially lower vertical resolution of seismic records in comparison with curves of GBS methods. The question of the comprehensive interference phenomenon presence on seismic record on both real and model ones is not disclosed. The phenomenon of the reflected wave interference, as is well known, is expressed in the fact that each of the seismic record amplitudes at each of the time samples contains information in the form of a response to a seismic impulse, probing the real medium. Such a reaction is formed from the above acoustic heterogeneities. As noted in [1], on the one hand, each of the seismic amplitudes is characterized as the sum of the contributions from the upstream “elementary layers” with their unique interference. On the other hand, information about acoustic inhomogeneities of a real medium in the form of a response to a probing seismic pulse manifests itself in the form of responses—specific local seismic signals. Quantitatively, this information can be presented in the form of the corresponding curves of the elementary boundaries or sequences contributions in the total interference seismogram. At the same time, acoustic heterogeneities of the section are manifested as a change in acoustic impedance (the product of density and velocity ρV ) and reflectivities [2]. The contribution curves of the total interference seismogram are displayed on the time interval of the full seismic pulse length probing the real medium, plus the thickness of the elementary formation or stratum (meaning the thickness of the section interval, which is directly analyzed) [1, 3]. As a result of such interference, information from each of the elementary layers is distributed along the time coordinate of the seismic impulse probing the real medium throughout its passage through the elementary layer (or a set of elementary layers). It should be noted that this situation is only some external part of the problem of “compliance” (most likely “difference”) of seismic and GBS data. A more detailed analysis of the influence of geological parameters on its acoustic characteristics and dynamics of seismic record shows that, on the one hand, the formation of a rather complex structure of seismic data is influenced, as noted above, by the interference of local seismic signals [1, 4, 5]. On the other hand, it should be borne in mind that in the real geological section at each point, the result of some “interference of geological indicators” is manifested. In this case, “seismic interference” in a sense is an image of the corresponding “geological indicators interference” (also very complex

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . .

17

in its interference structure). The result of the “interference of geological indicators” can be visually seen directly on the results of automated GBS data processing [6, 7]. It is obvious that each point of the real geological section model (especially in the productive intervals of the section) contains some specific quantitative information about the lithological composition, reservoir properties, and oil and gas saturation of reservoir rocks (which affect the dynamics of seismic recording).

2.1

Reflected Wave Dynamic Characteristics Considering the Contribution of Elementary Boundaries and Strata

The seismic record features are known to be determined by the amplitude and phase characteristics of the seismic impulse excited in the source and propagated through acoustic heterogeneities in the studied real subsurface environment. When assessing the superposition of local seismic signals and its direct influence on the structure of the resulting interference seismic record, one should keep in mind the process of local seismic signal interference formation. The mechanism of the wave field formation manifests itself in its different dynamic characteristics. Along with the above questions, the issue of the influence of such important geological parameters as lithology, reservoir properties, and oil and gas saturation on the dynamic characteristics of the reflected waves remains unsolved. The informativeness question of the seismic data dynamics as a whole, the problem of its recoverable or non-recoverable structure (if we keep in mind the possibility of obtaining from the seismic record dynamics an adequate model of acoustic inhomogeneities of the real environment with a sufficiently correct numerical solution of the inverse dynamic seismic problem as a result of the implementation of a special graph of seismic data processing, including the procedure of seismic inversion by initial seismograms and final time sections or cubes of seismic record [8]), and the influence of various noise waves on the structure of the wave field have not been studied. The problem of “weak” seismic signals [4], which is directly related to the assessment of the contribution to the seismic wave field of lithology, porosity, and oil and gas saturation, is also not investigated. From the enumeration of the above questions, it is clear that many of them have not been fully investigated, but their relevance is not lost. Obviously, the accuracy of predicting, using seismic data important for prospecting and exploration of geological indicators, will largely depend on their solution. We define the basic concepts (terms) used in this section: elementary layer, elementary boundary (“horizon”), thin-layer section. Elementary layer is a layer whose thickness corresponds to the maximum attainable resolution in the model obtained from seismic data and here is equal to the thickness of the layer corresponding to the sampling step of seismic record over the time. Accordingly, the elementary boundary is the boundary of the elementary layer. The thin-layer model is a model of the stratum, representing a set of elementary layers.

18

2 Seismic Modeling of Wave Field Dynamic Parameters

This subsection deals with the question on how the structure of reflected wave interference manifests itself in various dynamic parameters of seismic waves based on the study of the influence of the contributions of elementary boundaries and sequences directly to the interference structure of these dynamic parameters. Partially this question was touched upon in [1, 3, 9]. The following shows how the productive and unproductive intervals of the studied section are displayed in the dynamic parameters of the waves based on the wave field decomposition by the contributions of elementary boundaries and strata in the seismic recording dynamics. One of the results for 1D seismic simulation is taken as a model example. This result is obtained using materials from one of the wells of the Shaim oil and gas region of Western Siberia, which is shown in Fig. 2.1 [1, 10]. On the presented graphs, the name of each of them (located in the title of each graph) determines their purpose and their role in the analysis of the presented seismic modeling results (Fig. 2.1). A distinctive feature of the model experiment under consideration is the use of an equitime model with a discretization time step equal to 1 ms. With such a step, the seismic data of 3D CDP surveys were processed and interpreted on the considered area of Western Siberia [1, 8]. The equitime model as a result of approximation of the initial acoustic velocity, density, and acoustic impedance curves, presented in Fig. 2.1a–c corresponds to those graphs on which equitime boundaries limit the layers of the minimum thickness determined by the discretization step of the seismic record in time. The productive oil-saturated formations in this model are the formations with numbers 2 and 3. One permeable oil-saturated interlayer is located in the central part of each of these formations. These interlayers have an oil-saturated thickness equal to
1–2 m. The first three graphics in Fig. 2.1 a–c are the initial curves of acoustic logging, rock density distribution, acoustic impedance (ρV ), and their corresponding equitime models of velocity, density, and acoustic impedance (i.e., the result of approximation of these initial curves, which correspond to a set of elementary layers and boundaries, with a step of seismic record discretization along time). The minimum-phase seismic impulse is probing the real geological environment [1]. It is considered in conjunction with the amplitude- and phase-frequency spectra. This seismic pulse was extracted from real seismic traces located near the vertical profile of the section, timed to the vertical line of the well under consideration. The following three graphs (Fig. 2.1d–f ) show the distribution of reflection coefficients, synthetic and real seismic traces, and the total contribution of each of the elementary boundaries (elementary layers) in the interference wave field. A relatively small difference between the model and the real seismic traces (found in practice almost always when comparing the results of solving the direct problem of seismic exploration and actual seismic data) is explained as follows. First of all, it is a limited time interval of the analysis; use, as a rule, some approximation of the curves of velocity, density, and acoustic impedance; not enough full consideration of multiple reflections and properties of the absorbing layered medium; the difference of the considered vertical section line from the actual spatial position of the longitudinal seismic wave propagation path; etc.

Fig. 2.1 Seismic modeling results of one of the wells of the Shaim oil and gas region. The description of the graphs and numerical data in the headings for a–m in the text is given

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . . 19

20

2.1.1

2 Seismic Modeling of Wave Field Dynamic Parameters

Interference Contributions Matrix

The nature of the seismic wave interference reflected from each of the selected ten layers (strata) is presented on the contribution curves of each of these layers (Fig. 2.1g). In numerical form, the wave interference is given in the “formation (stratum) contribution” graph. These are practically the same stratum contribution curves but presented in the form of the corresponding numerical matrix (Fig. 2.1h). The authors call it the “interference contributions matrix” (ICM). It is a specific mapping of the process (mechanism) of the interference wave field formation. The physical and informational meaning of the contribution matrix is as follows. The matrix of contributions is a rectangular table containing the amount of information (contributions) about a particular reservoir parameter under study in the wave field characteristics or its transformations. The value of each matrix element is the contribution of the reservoir parameter under consideration to the discrete amplitude at a given time (depth). It is obtained by calculating the direct problem and comparing values from other strata involved in the formation of the field in question, its transformation, or attribute. The columns of the matrix in this case represent the contribution (influence) distribution of the reservoir under study along the spatial coordinate, and the rows represent the ratio of the information amount from different layers for a given point of the section under study. ICM describes quite full the process of forming field characteristics for a given environment model and is a universal and convenient table for studying, at a quantitative and qualitative level, the possibilities of extracting necessary information from seismic data. Its dimension depends on the dimension of the used real medium model (1D, 2D, 3D) and on the type of operator solving the direct problem (convolutional, ray, wave, etc.). The spectrum of ICM application is quite wide: from processing field data, solving an inverse dynamic seismic problem, implementing interpretive predictive algorithms to obtaining final results in the form of areal parameter maps. The matrix of contributions can also be used to set and solve special problems—to reduce non-correct problems to their correct analogue (constructing a regularizing functional, choosing regularization parameters, analyzing and synthesizing processing parameters, examining weak signals, etc.). The type of ICM depends on what problem is being solved at the moment and what information needs to be obtained: from dividing the medium into layers to the model parameterization (acoustic impedance, reflectivities, absorption coefficient, and others), etc. Thus, the whole process of wave interference is represented point-by-point (in both graphical and numerical forms) with a discretization step of seismic recording over time. At the same time, it is clearly seen that each of the synthetic seismic trace amplitudes contains information from a specific set of layers located above. As noted above, each of the amplitudes of the seismic recording on each of its discretes is characterized by a unique interference contribution from the layers under consideration, which distinguishes it from the amplitudes on all other discrete seismic records. Each of them is also characterized by a different contribution ratio of given sequences and the dominance of one of them over all the others.

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . .

21

Figure 2.1i–j presents the graphs of the “Synthetic trace content” and the “Real trace content,” which, in addition to the summary records of the interference seismic field, show the normalized local records as a result of the seismic responses to the acoustic heterogeneities of the studied section. These records show that within each of the stack seismogram phases, with the exception of the very first phase, the interference (superposition) of the formation contributions is manifested. The distribution in the vertical coordinate of the most intense values of the formation contributions among all the others is presented in Fig. 2.1k and shows that from productive strata 2 and 3, intensive contribution values are formed only on two relatively unextended (compared with the initial arrangement of strata in the section, Fig. 2.1l) seismic trace intervals. The length of the seismic traces located at times t0 (1728–1734 and 1734–1742 ms, respectively) is rather small, equal to 6 and 8 ms At the same time, the indicated time intervals are confined to those parts of the seismic trace that are characterized by relatively low amplitudes (Fig. 2.1e). The information presented in Fig. 2.1m shows that the layers with numbers 1, 4, and 9 have the highest average contribution values of each of the considered layers of the interference wave field. Productive layers 2 and 3 are characterized by some intermediate average values of the formation contribution. In the seismic modeling of the wave field dynamic characteristics, the VKSYNT program complex developed by the authors was used directly [1, 5]. On its basis it is possible to solve the following tasks: – Studying the reflected wave formation features in the considered section interval based on the assessment of the elementary boundary and strata contributions in the interference wave field. – Stratigraphic tying of reflections. – Determining the time interval for the desired parameter prognosis by the selected predicting system, which uses various parameters of the seismic record shape. – Evaluating the geological information content of reflections (including weak seismic signals) corresponding to the target intervals of the section. – Classifying existing wells in the work area in accordance with the defined geological reflection information and the choice of reference wells. – Forming a seismic image for each of the reference wells in the form of a set of dynamic and kinematic characteristics that reflect the main seismic record shapes.

2.1.2

Contribution of Boundaries and Layers to the Results of Pseudo-Acoustic Transformations

To obtain information about the rock physical characteristics (in particular, information approaching the acoustic and density logging) and solve the problem of geological section dismemberment, the technique of pseudo-acoustic logging (PAL) has recently been widely used. The principal feature of this technique application is

22

2 Seismic Modeling of Wave Field Dynamic Parameters

that the seismic trace is converted into an acoustic impedance curve according to the well-known formulas [2, 11]: ðV  ρÞi ¼ ðV  ρÞi1 

1þKj 1 þ Ki ¼ ðV  ρÞ0  Πi j¼1  1  Ki 1Kj

ffi ðV  ρÞ0  Πi j¼1 

1 þ A1  S j , 1  A1  S j

where (V  ρ)i is the product of velocity by density (acoustic impedance) of the i-th layer and Ki is the reflection coefficient of the i-th boundary and is calculated according to the known formula: Ki ¼

ðV  ρÞi  ðV  ρÞi1 : ðV  ρÞi þ ðV  ρÞi1

If the seismic trace Si is considered as a sequence of reflection coefficients, then, having information about the acoustic impedance of the first layer and the amplitude of the seismic signal A, it is possible to uniquely calculate the acoustic impedance of the underlying layers [2]. Figure 2.2a–b presents the results of pseudo-acoustic transformations of the seismic trace (Fig. 2.1) in two versions—in the form of the traditional (Fig. 2.2a) and broadband (Fig. 2.2b) PAL. From these figures, one can clearly see the main features of the contributions of each of the ten layers, the tops of which are directly related to certain reflecting horizons. These features are visible in the contribution curves of each of the layers under consideration; in the interference contribution matrix (ICM), layer-by-layer PAL trace decomposition in its traditional non-normalized and normalized images—local curves of the PAL records. Two important features of the results should be noted. The first feature of the performed transformations is that the results of the PAL transformation (Fig. 2.2a–b) turn out to be relatively close to the distribution of acoustic impedance constructed from well data, despite the rather strong “averaging” (PAL procedure) of “acoustic details,” directly established based on the use of “elementary boundaries and layers” (Fig. 2.1c). The second important feature of the PAL transformations is that, using the transformations performed, it is not possible to free oneself from the rather strong interference of the considered layers in each of the PAL trace amplitudes. Oil perspective formations with numbers 2 and 3 at the same time almost do not show up on the layer-by-layer decomposition curves.

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . .

23

Fig. 2.2 The contribution of elementary reflectors and strata assessment in the interference field of pseudo-acoustic transformations (PAL, pseudo-acoustic logging) of seismic record: (a) traditional PAL, (b) broadband PAL

24

2 Seismic Modeling of Wave Field Dynamic Parameters

2.1.3

The Contribution of Boundaries and Layers to Instantaneous Dynamic Parameters

The dynamic characteristics of reflected waves are widely used in the practice of interpreting seismic survey data obtained under conditions of a complex structure of a geological section. The dynamic analysis of reflected waves is based on the differential dynamic characteristics of seismic records, based on the Hilbert transform and the concept of an analytical signal [11–14]. The calculation of the instantaneous signal parameters allows us to study separately the dynamic characteristics of the wave process and to associate their changes with the geological features of the subsurface medium when interpreting seismic data. In this method, the instantaneous characteristics of signals are directly measured, which are assigned a specific physical meaning. The analysis of signals in a complex form is widely used in radiophysics and electrical engineering. The complex function of the seismic record Z(t) of a real variable is determined as an analytical signal corresponding to a “physical” signal as a real function s(t). In the complex area, it appears as Ζ(t) ¼ S(t) + iU(t). Here, the real S(t) and imaginary U(t) (called the conjugate S(t)) Z1 Sðt Þ 1 components are interconnected by the Hilbert transform: U ðt Þ ¼ dτ . tτ π 1

Using the real component, it is possible to unambiguously determine the imaginary and restore the seismic trace in the complex domain. The stability of the formal signal transformation method, the Hilbert transform, is ensured by performing the physical feasibility of the “analytical signal,” i.e., s(t) ¼ 0 if t  0. In addition, a necessary condition is the differentiability of the function s(t) for the entire time interval under study, to which seismic traces are almost always satisfied [13]. The main instant characteristics are the following: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1. The instantaneous amplitude Aðt Þ ¼ U 2 ðt Þ þ S2 ðt Þ , which has the physical meaning of the envelope of all trace extremes, makes it possible (with an error of up to 5%) to quantify the difference in the velocities and densities of the layers. In this case, the reflection intensity does not depend on the phase and frequencies and is associated with lithological changes at the horizon of the layers, for example, with unconformity strata, as well as with possible hydrocarbon deposits. 2. The instantaneous phase φ(t) has the physical sense of the envelope A(t) vector rotation in the complex plane asiit moves along the time axis t from the start of h registration: φðt Þ ¼ arctg  USððttÞÞ . It will characterize the absolute arrival time of

each sample—the instantaneous current value of the seismic signal amplitude. At the same time, the slope of the instantaneous phase or the number of its drops in the time interval characterizes the interlacing frequency and the nature of the sedimentation [14]. It does not depend on the reflection intensity and can be used to select weak signals, discontinuities, faults, and trace wedges [13, 14]. 3. The instantaneous frequency f(t) has the physical meaning of the vector envelope rotation velocity over time. Frequency can be expressed through the derivative of the phase in time, as well as through real and imaginary traces:

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . .

25

     dφðt Þ d U ðt Þ dU ðt Þ dSðt Þ 1 ¼ Sð t Þ  U ðt Þ arctg  f ðt Þ ¼ ¼ dt dt dt Sð t Þ 2πA2 ðt Þ dt The instantaneous frequency is related to the phase change as a derivative of the phase in time. Obviously, the faster the instantaneous phase increases per unit time, the higher the instantaneous current frequency. With the help of instantaneous frequency, it is possible to measure the continuous change in the frequency composition of the seismic signal both in time and horizontally along the stratification. At the same time, it is possible to trace lateral changes in lithology and oil saturation in the productive strata, taking into consideration changes in the properties of sediments in the enclosing stratum, since the phase and frequency most fully characterize the nature and frequency of sedimentary stratification [14]. On the same basis, as noted in [12], pinch zones are localized, leading to significant frequency changes, zones of hydrocarbon contact with formation water are identified, and a shift toward low frequencies is fixed (“low-frequency shadow”) on reflections from horizons below gas-saturated rocks. In Figs. 2.3a–b and 2.4a, a strata contribution assessment results in relation to instant dynamic characteristics of the reflected waves for a section interval which is also presented in Fig. 2.1. These figures show the section dismembering realized with the help of the dynamic characteristics in accordance with their physical described above meaning. At the same time, the contributions of each of the ten stratum of the section under consideration to each of the instantaneous dynamic characteristics of the waves are diverse and individual in structure. This is well manifested in the curves of the layer contribution, expressed in the numerical table (matrix) of contributions (ICM) and the results of the layer-by-layer decomposition of the characteristic trace in unnormalized and normalized versions—on local curves of instantaneous phases, frequencies, and amplitudes (Figs. 2.3a–b and 2.4a). On the contribution equitime curves of elementary boundaries to the interference field of instantaneous amplitudes, their information content is estimated in the range from 5 to 55% (Fig. 2.4a). It should be noted that many of the layer contribution curves on the instantaneous amplitudes (Fig. 2.4a) are very close in their level, unlike those for the instantaneous phases and frequencies (Fig. 2.3a–b). According to the contribution of elementary boundaries into the interference field of instantaneous phases and instantaneous frequencies (Fig. 2.3a–b), their information content is not assessed as high compared to the results of pseudo-acoustic transformations (Fig. 2.2a–b). The range of the boundaries contribution variation to the interference field of the phases is from 85% to 100%, frequencies from 88.6% to 88.7%, whereas on the results of pseudo-acoustic transformations, the contribution of elementary boundaries to the interference field of the PAL is estimated by the range from 0% to 100%.

26

2 Seismic Modeling of Wave Field Dynamic Parameters

Fig. 2.3 The contribution of elementary reflectors and strata assessment in the interference field of the instantaneous dynamic characteristics: (a) instantaneous phase, (b) instantaneous frequency

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . .

27

Fig. 2.4 The contribution of elementary reflectors and strata assessment in the interference field of the reflected wave dynamic characteristics: (a) instantaneous amplitude, (b) current energy of the seismic record

28

2 Seismic Modeling of Wave Field Dynamic Parameters

On the contribution equitime curves of elementary boundaries to the interference field of instantaneous amplitudes, their information content is estimated in the range from 5% to 55% (Fig. 2.4a). It should be noted that many of the layer contribution curves on the instantaneous amplitudes (Fig. 2.4a) are very close in their level, unlike those for the instantaneous phases and frequencies (Fig. 2.3a–b). Despite the well-defined physical-geological meaning of seismic wave field transformations, which are dynamic parameters (instantaneous amplitudes, phases, and frequencies), one cannot get rid of the strong layer interference in each of the trace sections of the used reflected wave dynamic characteristics. Oil perspective formations with numbers 2 and 3 on the curves of the rock formation decomposition into contributions do not manifest themselves. Obviously, to more effectively use the characteristics of signals when interpreting seismic data, one should first clear off the interference phenomenon in the initial wave field. As some comparative material for instantaneous amplitudes (Fig. 2.4a), Fig. 2.4b shows the results of calculations in the form of the current energy, which is defined as the averaging of the seismic trace amplitudes squares in the energy operator sliding window of a given length. The current energy curve (Fig. 2.4b) of the seismic signal largely repeats the curve of instantaneous amplitudes (Fig. 2.4a). But in the structure of the boundary and layer contribution curves, a numerical table of contributions (ICM), in the results of the layer-by-layer trace decomposition of the considered characteristics in unnormalized and normalized versions (on local curves of instantaneous phases, frequencies, and amplitudes), the differences are quite significant (Fig. 2.4b). In addition, on equitime curves of the elementary boundary contribution to the interference field of the current energy, their informativeness turns out to be significant—changes in the contribution of these boundaries are estimated in the range from 0% to 100%.

2.1.4

The Contribution of Boundaries and Strata to the Seismic Recording Deconvolution Results

The deconvolution procedure (inverse filtering), which allows extracting the medium reflective characteristics from the convolutional seismic recording model, is, as is known, decomposing the observed time series formed by the convolution into its components. Such procedures have long been widely used in the practice of seismic data processing [2, 11, 15–18]. In addition, a whole range of other tasks are solved: suppression of multiple reflections, satellite waves, wave reverberation, increasing the time resolution of the recording, compensating for the distorting influence of the medium, reducing the fluctuation of the waveform, etc. Due to the limited frequency band of the seismic recording and its complication by random noise, especially on those components where the signal energy is small enough, it is

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . .

29

practically impossible to separate all convolutional components; it is necessary to remain within the wave field structure elements. In this paper, it is not necessary to list all the advantages and disadvantages of inverse filtering (deconvolution) methods. Numerous articles and monographs are devoted to these methods [2, 11, 15–18]. Here we touch upon only the known principal side of the question. Let the seismic signal x(t) be the sum of elementary signals and has the same shape but with different arrival times and amplitudes. The form of the elementary signal and the spectrum of this function S(ω) are assumed known. Consider an abstract linear system with a spectral characteristic GðωÞ ¼ Sð1ωÞ, i.e., a filter G that has a frequency response that is inverse to the signal spectrum. When applying to the filter input signal S(ω) at the output, we get S(ω)  G(ω) = 1, i.e., the output signal, which is a single pulse, and the output noise (if we talk about noises)—white noise. In a more detailed consideration of the subject: if the input signal system filed x(t), n P ak SðωÞ eiωτk , the output will receive a signal the spectrum of which Sx ðωÞ ¼ k¼1

y(t), having a spectrum Sy ðωÞ ¼ Sð1ωÞ  Sx ðωÞ ¼ Sð1ωÞ

n P

ak SðωÞ eiωτk ¼

k¼1

n P

ak eiωτk .

k¼1

Since eiωτk is the spectrum of the “shifted” δ—function δ(t  τk), the output signal n P will be as follows: yðt Þ ¼ ak δðt  τk Þ. Thus, the filter G, defined by the formula k¼1

GðωÞ ¼ Sð1ωÞ , gives an ideal resolution of elementary signals, compressing each of them into an absolutely short pulse. However, the described reverse filtration pattern is idealized. The signal spectrum decays indefinitely with frequency, so the frequency response G(ω) should grow indefinitely: |G(ω)| ! 1 at S(ω) ! 0. To avoid this, the frequency band is limited to a range within which the signal spectrum does not decrease below a certain limit. At the same time, within a limited frequency range, there may be areas with a low level of spectrum components, which are also an obstacle to obtaining an ideal inverse filter. A pulsed random process that describes the useful part of a seismic record is almost always accompanied by noises e nðt Þ. Noises that prevent the selection of individual reflections can be represented as P an f ð t  τ n Þ þ e nðt Þ. Assuming that the noise e nðt Þ and the useful part of the seismic n

record are not correlated with each other, we obtain the power spectrum of the sum of two random processes in the form c2 jSðωÞj2 þ υ~n ðωÞ, where υ~n ðωÞ is the power spectrum of noise and c2 is positive proportionality coefficient. According to the above, the optimal filter will be: G ð ωÞ ¼

S  ð ωÞ S  ð ωÞ ¼ 2 υN ðωÞ β jSðωÞj2 þ υ~n ðωÞ

30

2 Seismic Modeling of Wave Field Dynamic Parameters

where S(ω): complex-conjugate function with S(ω). S(ω): frequency response of the signal component. β2: positive coefficient of proportionality. The physical meaning of adding the term υ~n ðωÞ in the denominator is that it limits the value of the filter’s transmission coefficient at those frequencies where it exceeds β2|S(ω)|2. This automatically eliminates the danger of a sharp increase in broadband noise, inherent to the ideal inverse filter GðωÞ ¼ Sð1ωÞ. For the white noise υ~n ðωÞ ¼ const, denoting γ 2 ¼ βυ~n2 and neglecting the constant factor in the denominator, we 

Þ obtain the Künez inverse filter: GðωÞ ¼ jSðSωÞðjω2 þγ . This formula, as is well known, has 2

found practical application in calculating the frequency response of an inverse filter in the presence of wideband noise. In this case, the positive coefficient γ 2 is inversely proportional to the average signal-to-noise ratio at the input. The smaller this ratio,  Þ the more the optimal filter GðωÞ ¼ jSðSωÞðjω2 þγ differs from the ideal inverse filter 2

GðωÞ ¼ Sð1ωÞ. We only note that for practical purposes, complex algorithms for constructing inverse filters with a strict geophysical and mathematical formulation of the problem are used [2, 15, 16, 18]. In practice, deconvolution is almost never used as a procedure for the complete compression of a seismic signal. As the authors [16] rightly point out, in the presence of noise, a limited frequency range of the signal such a task cannot be solved even with a known and unchanged pulse shape. As noted, it should be added that deconvolution is nothing more than a trivial attempt to solve the inverse dynamic seismic problem. One of its significant drawbacks is that this procedure cannot determine which frequency components need to be “pulled out” and which are “left alone” (in the latter case, as a rule, false reflections occur). The peculiarity of its application is that after wave field transformations by means of deconvolution, which is a nonlinear procedure with nonoptimal and incorrect parameters, it is almost impossible to restore an adequate acoustic model of a real medium due to the fact that the signal dynamics is disturbed. There is another distinctive feature of this procedure. So, Fig. 2.5a shows the situation in general and in particular with the problem of interference of elementary boundaries and strata in the interference field, transformed by the deconvolution procedure. At the same time, it is clear that the deconvoluted trace has become significantly high-frequency compared to the original seismic record (Fig. 2.1e), and it to some extent repeats the behavior of the acoustic impedance curve established by well data (Fig. 2.1c). The information content in the form of distribution over the section of equitime curves of the elementary boundaries contribution turns out to be somewhat underestimated (as compared with the original non-transformed seismic trace (Fig. 2.1f )), estimated by the range of their change from 30 to 85%. The layer contribution curves in accordance with the physical sense of the transformations performed by the deconvolution procedure are significantly highfrequency in comparison with similar curves of the original seismic record. This is manifested both in numerical form (in the interference matrix of contributions) and

2.1 Reflected Wave Dynamic Characteristics Considering the Contribution of. . .

31

Fig. 2.5 The contribution of elementary reflectors and strata assessment in the interference field of the seismic record dynamic characteristics: (a) seismic record deconvolution, (b) second derivative of the seismic record

32

2 Seismic Modeling of Wave Field Dynamic Parameters

on the results of the layer-by-layer decomposition of the deconvoluted trace in the non- and normalized variants (Fig. 2.5a). It is very important that the results obtained in general using the deconvolution procedure are not free from the influence of the interference effect of the contributions from the layers under consideration to each of the amplitudes of the stack deconvoluted trace. In the column display of layers in the deconvolution trace, it can be seen that the information from the oil perspective layer 2 turns out to be “fragmented,” overlapping mainly with information from the overburden 1 and the underlying layer 3. Information from the oil perspective layer 3 in the vertical interval of the deconvoluted section is reduced by about 1.5 times compared with the initial position of this reservoir in a vertical geological section. As some comparative materials for assessing the result of the boundaries and formations contribution to the interference deconvoluted trace (Fig. 2.5a) in Fig. 2.5b are given, the result of calculations is in the form of an initial seismic record transformation into a trace of its second derivative with inverted polarity. It is clearly seen that the curve of the second derivative of seismic record (Fig. 2.5b) has an external similarity with the deconvoluted seismic trace (Fig. 2.5a). However, a second high-frequency component is “superimposed” on the trace of the second derivative from the initial seismogram, which is associated with the relatively high sensitivity of the second derivative of the seismic record to noise. Otherwise, the differences with similar results of deconvolution turn out to be significant (Fig. 2.5b). On equitime curves of the elementary boundary contribution to the interference field of the second derivative, their informativeness is slightly higher than on the results of deconvolution—the changes in the contribution of these boundaries are estimated here from 0 to 80%. The main conclusions of the research are as follows: 1. Without considering the contribution of elementary boundaries and the strata to the interference wave field, as well as to the various dynamic parameters of the reflected waves, it is very difficult to interpret the acoustic heterogeneities of the section studied only on the basis of the elements of the wave field structure. Without the abovementioned field components and its dynamic parameters (contributions of elementary boundaries and sequences), it is difficult to determine the location of the specific target intervals in the time section associated with productive or potentially productive intervals of the geological section. 2. Under conditions, when interpreting seismic data (especially when predicting various geological indicators), it is necessary to work with interference seismic records and their dynamic characteristics; one should first estimate the contributions of elementary boundaries and layers in the field of dynamic parameters. The correct choice of the target time interval in which the maximum possible information (contribution) from the target object is located will make it possible to significantly increase the efficiency of predicting. 3. All seismic data transformations (linear and nonlinear amplitude recalculations, differentiation and integration of records, calculation of instantaneous characteristics, the use of various single- and multichannel filters, etc.) that do not include

2.2 Quantitative Assessment of the Geological Section Parameter Contribution

33

the IDSP solution procedures do not eliminate seismic records from interference phenomena and can enhance the interference, complicating the recording of especially relatively weak useful signals. 4. The ideal way to eliminate the wave interference effect and build adequate thinlayer models of a real medium is to numerically solve the inverse dynamic seismic problem, which allows to divide the information contained in the seismic record into two, information about the source of elastic oscillations and information about the real geological environment [1, 3, 5, 6, 8, 9, 19], and get rid of the interference created by the excitation source. Thus, in the process of interpreting seismic data, especially when using dynamic parameters of reflected waves in order to improve the prediction efficiency of acoustic heterogeneities, the composition, and properties of the geological section, it is advisable to make a detailed assessment of the contribution to the interference wave field and the dynamic characteristics of the reflected waves.

2.2

Quantitative Assessment of the Geological Section Parameter Contribution

When interpreting seismic data (especially when predicting various geological indicators), one has to deal with a very complex structure of interference wave fields and their dynamic characteristics (with their interference “filling” in the form of appropriate contributions from certain geological and geophysical parameters). In this regard, there is a need to assess the contributions to the structure of the wave field (WF) and the results of its transformation of the various components of the geological section. The use of a complex of seismic modeling programs, well materials, and seismic data allows to perform a quantitative assessment of the influence of the composition and properties (lithology, porosity, water and oil saturation) of the studied section on the seismic record dynamics and, for example, on its pseudo-acoustic transformations (PAL). This influence is different at different time intervals of the seismic records and the results of the PAL with the almost ubiquitous manifestations of interference. From the point of view of solving the main tasks of seismic exploration, the study and quantification of anomalies of the seismic wave field and its parameters (contributions of geological indicators) when detecting oil prospective objects can be one of the conditions for the complex interpretation of seismic data. In this case, the used mathematical modeling method for solving this kind of problem is in many respects similar to the above-considered method of studying the contribution of elementary boundaries and strata to the parameters of the interference wave field, which is discussed above (in Sect. 2.1 and works [2, 20, 21]). Approximately the same mechanism of seismic modeling is used—the formation of the wave field and its dynamic characteristics and the “interference matrix of contributions” (ICM). The latter should contain in numerical form information about the contribution of each of

34

2 Seismic Modeling of Wave Field Dynamic Parameters

the geological indicators under consideration. A distinctive feature of this task is that when constructing the ICM, the method of component substitution that make up the medium and the superposition principle of the whole set of various factors are used. On the substitution method when looking for contributions to seismic data, the following should be noted. When determining the contributions of the sand component, such a lithological component is replaced by clay, the clay component (the medium becomes seismically homogeneous with respect to the parameter under study), and then, during the formation of the field and its parameters, changes in the local characteristics of the original seismic record are found. The degree of their change characterizes the contribution (influence) of the corresponding component, which is displayed in the graphical table of ICM. The effect of porosity is determined by the replacement of porous facies with nonporous. The contribution of a fluid type is also measured by substitution. For example, to determine the contribution of pore oil saturation, oil is replaced by formation water, and a seismic wave field with the characteristics of the changed reservoir is constructed, followed by the correlation of the replacement result with the original data. Thus, it is possible to analyze the contribution of a certain type of pores, if the reservoir rocks have a complex structure, for example, formed by different types of pores. At the same time, the principle of superposition is implemented—the total contribution to seismic data is equal to the sum of individual contributions from objects (layers) (i.e., the amount of information in the information balance is preserved). Previously, the authors performed studies for multilayer absorbing media with an estimate of the elementary boundaries and strata contribution [1, 9]. The concept of an elementary boundary (elementary layer) is introduced, which characterizes the maximum possible model detailing of a real medium, its thin-layer structure in the class of piecewise constant functions in the interval of seismic sampling step over time. It was noted that without taking into account the contribution of elementary boundaries and strata to the interference wave field, it is very difficult to orientate the elements of its structure, as well as in the informativeness of the acoustic heterogeneities of the section under study associated with these elements. It is also shown that the seismic wave absorption can lead to a significant change in the interference pattern of the wave field relative to the field corresponding to the model of the medium without absorption. It is also worth noting that the total contributions of the boundaries to the seismic wave field can be formed only due to the wave absorption presence in the oil-saturated interval for different absorption coefficient. Considering the comments made, the solution of the question of how the studied characteristics, such as lithological composition (clay content + sand content), porosity, water, and oil saturation of the terrigenous section manifest themselves in the form of relevant contributions, is considered below. As a model example, the authors took borehole and seismic materials for 1D seismic modeling, which are discussed above in Sect. 2.1. According to the hierarchical scheme of real environment models developed by E. A. Kozlov [22], the model under study belongs to a continuous, elastic, isotropic, and homogeneous within an ordered sequence of layers—elementary boundaries

2.2 Quantitative Assessment of the Geological Section Parameter Contribution

35

separated from each other by the discretization step of seismic recording in time (changes in lithology, porosity, water, and oil saturation in this model are given through the propagation velocity of elastic waves and density at the corresponding intervals of the studied section). The results of the model experiment are demonstrated in the productive interval of a real well using its lithologic-stratigraphic column over the considered interval, starting from the acoustic impedance curve (AI) and the “equitime” approximation of this “initial curve” AI with the sampling step along the time. This column is pre-built by the authors using the method of functional transformations of geophysical parameters by integrating them into information systems [6, 7]. A more detailed consideration of the method of processing and interpreting geophysical borehole survey (GBS) data is given in Sect. 5.1. Returning to the model example characteristics, we note that the productive (oil-saturated) layers in this model are the layers with numbers 2 and 3. The signal probing the real geological medium (a means of elastic oscillation generating) is a minimum phase seismic impulse (Fig. 2.6r). It was extracted from real seismic traces located near the wellbore vertical of the used well [1, 23]. The results of the obtained quantitative estimates of the geological indicators contribution to the interference seismic record and its pseudo-acoustic transformations (PAL) are given below.

2.2.1

Lithological Component Contribution

The nature of the interference manifestation of such geological indicators as the lithology of the terrigenous section (clayiness + sandiness) in the interference structure of the reflected waves is shown in Fig. 1.6. Here, as in Figs. 2.7, 2.8, 2.9, 2.11, the name of each of these graphs determines their purpose and role in the analysis of the generated seismic modeling results. The first three graphs are presented as follows: – Lithological column (with features of the ratios of various lithological components, porosity, and the nature of fluid saturation of reservoir rocks) (Fig. 2.6a). – Vertical distribution (along the borehole) of analyzed layers 1–10 (strata), defined as a set of lithologic-stratigraphic intervals of the studied section (Fig. 2.6b). – Curves of acoustic impedance (ρV) in the form of the initial curve ρV and the result of its equitime approximation (Fig. 2.6c). The following two graphs (Fig. 2.6d–e) show the distribution of the clayiness and sandiness curves in the form of the corresponding original curves and the results of their equitime approximation. The following three graphs give the distributions: – The curve of the total contribution of each of the elementary boundaries in comparison with the curve of the total contribution solely of lithology for each of these elementary boundaries (Fig. 2.6f).

Fig. 2.6 Detailed quantitative lithology contribution assessment in the interference seismic record and pseudo-acoustic transformations

36 2 Seismic Modeling of Wave Field Dynamic Parameters

Fig. 2.7 Detailed quantitative porosity assessment contribution in the interference seismic record and pseudo-acoustic transformations

2.2 Quantitative Assessment of the Geological Section Parameter Contribution 37

Fig. 2.8 Detailed quantitative water saturation contribution assessment in the interference seismic record and pseudo-acoustic transformations

38 2 Seismic Modeling of Wave Field Dynamic Parameters

Fig. 2.9 Detailed quantitative oil saturation assessment contribution in the interference seismic record and pseudo-acoustic transformation

2.2 Quantitative Assessment of the Geological Section Parameter Contribution 39

40

2 Seismic Modeling of Wave Field Dynamic Parameters

– The contribution curves of only the lithological component for each of the considered layers in the form of the corresponding contribution curves (Fig. 2.6g), which are displayed (manifest themselves) on a relatively long time interval of the full length of the seismic pulse probing the real environment, plus the thickness of each of the layers under consideration. – The contribution curves of the strata presented in the form of a corresponding numerical matrix (Fig. 2.6h). The graph “Seismic trace lithology content” in addition to the stack interference seismic records shows the normalized local seismic records as a result of seismic responses to acoustic heterogeneities of the studied section (Fig. 2.6i). In particular, it can be seen that within each of the phases of the stack seismogram, with the exception of the very first phase, the interference (superposition) of the strata contributions is manifested. The distribution along the vertical coordinate of the most intense contributions of the strata among all the others shows that the lithological components from reservoirs 2 and 3 as intensive values of the contributions of these components are formed in four windows of the seismic trace (if we compare them with the initial layer arrangement in the section presented in Fig. 2.6b) (Fig. 2.6j). The lengths of the seismic trace windows located at time intervals t0 1715–1722, 1726–1733, 1733–1745, and 1752–1755 ms is 7, 7, 12, and 3 ms, respectively. The specified time intervals are confined to those sections of seismic traces that are characterized by relatively low and average amplitudes (Fig. 2.6i). The information presented in Fig. 2.6k shows that layers 1, 4, and 9 have the highest average values of the lithological component contribution of each of the layers under consideration in the interference wave field. Production layers 2 and 3 are characterized by some intermediate average values of the lithological component contribution for these layers. Almost similar materials were obtained by the authors in the process of studying the contribution of the lithologic component in the pseudo-acoustic transformations of the seismic trace. Such results for the PAL trace, in particular, are presented in Fig. 2.6 l–q. These results can be compared with the results of assessing the contribution of the lithological component on the original seismic record. It can be seen that the information interference associated only with the lithological component on the PAL trace undergoes significant changes. The ratios of the total contributions of the boundaries and the total contributions of the lithology at different parts of the seismic trace and the PAL trace differ, but the observed difference in these features of information interference (associated with lithology) occurs throughout the time interval under consideration. In this case, the PAL transformations do not exclude the interference phenomenon of the target information (local curves of the lithological component contribution), but only complicate it in a certain way. Noting in general the nature of lithology in both the original wave field and its transformations (in particular, pseudo-acoustic), the effect of lithology in terms of the interference intensity on the wave field in the described case is quite significant. We note another feature of the lithology contribution: it depends on the ratios and

2.2 Quantitative Assessment of the Geological Section Parameter Contribution

41

mineral composition of the reservoir and the non-reservoir matrix rocks and the substitution method used in the simulation. The remaining fraction of the lithological component of the studied section is due to the influence of other geological indicators, such as porosity and the fluid saturation nature of reservoir rocks.

2.2.2

Porosity Contribution

To assess the effect of porosity on the original wave field and its pseudo-acoustic transformations, the same algorithm was used to analyze the influence of the lithological component on the dynamics of seismic recording and its PAL transformations (Fig. 2.6). Presented in Fig. 2.7 are the results of studying the effect of porosity on the dynamic features of seismic recording and the conversion of PAL in the form of the corresponding graphs which have the same meaning and the same names that determine their purpose and role in the analysis of simulation results. As in Fig. 2.6, out of the entire set of ten layers used in the analysis, the layers with numbers 2 and 3 are confined to one permeable oil-saturated interlayer with an oil-saturated thickness of
1–2 m. The said interlayers are characterized by increased open porosity values reaching Kp
22% (Fig. 2.7d). In comparison with the contributions of lithology, the total contributions, which are manifested exclusively by porosity for each of the permeable layers, in relation to the total contribution of each of the elementary boundaries (Fig. 2.7e), are significantly lower than on the “original” seismic trace and on the PAL trace (Fig. 2.7e, k). A similar decrease in the values of the porosity contribution to the seismic trace and the PAL trace (in comparison with the influence of the lithology factor) is shown on local porosity contribution curves (“stretched” by the length of the seismic pulse probing the real medium, plus the thickness of each of the layers) (Fig. 2.7f, l). The same pattern is observed on the stratum contribution curves presented in the form of the corresponding numerical matrix ICM (Fig. 2.7g, m), which corresponds to the logic that characterizes the fact of a significant excess of the contribution from lithology over the contribution from porosity. As can be seen from the curves of the seismic trace (Fig. 2.7g–i) and PAL trace (Fig. 2.7m–o) and the corresponding local contribution curves of the layers under consideration, as a result of pseudo-acoustic transformations, there is a significant redistribution of information on the porosity contributions at different time samples. Information on porosity from target objects (primarily from layers 2 and 3) is significantly “stretched” on the timescale against the background of their superposition with information from above and below the objects.

42

2.2.3

2 Seismic Modeling of Wave Field Dynamic Parameters

Water Saturation Contribution

The effect of water saturation of reservoir rocks on the original wave field and its pseudo-acoustic transformations is estimated from the standpoint of the substitution principle of the formation water factor for reservoir hydrocarbon saturation through the corresponding parameters (velocity and density) of the substituting components. It should also be noted that the effects of water and oil saturation of reservoir rocks are separated due to the fact that the reservoir water and hydrocarbons differ quite significantly in the elastic wave propagation velocity in them, as well as in density (these indicators make different contributions to the seismic wave field and his transform). In Fig. 2.8, the results of studying the effect of reservoir water saturation on the dynamic features of seismic recording and PAL transformation show a significant decrease in the information amount in relation to the information amount for the above geological indicators—lithology (Fig. 2.6) and porosity (Fig. 2.7). It can be seen that the values on the curves of contributions to water saturation only in relatively small areas (at rare points) exceed
20%. This applies to the contribution of the considered indicator both in the original wave field (Fig. 2.8f–h) and in the PAL transformations (Fig. 2.8l–n). On the seismic and on the PAL traces, information on water saturation from reservoirs 2 and 3 is manifested by low values. On the seismic and the PAL traces (“content” (Fig. 2.8h, n)), as well as on the numerical values of the ICM matrix (Fig. 2.8g, m), the target information is also overlapped by information from the above and lower layers. The reason for such low values of contributions to water saturation is obvious—it is associated with rather weak seismic signals, responses to the impact of a probing seismic pulse directly related only to water saturation.

2.2.4

Oil Saturation Contribution

The study of such an important geological indicator of reservoir rocks as oil saturation using seismic survey methods is a very urgent task. It turns out to be rather complicated, since one has to deal with rather weak seismic signals (local seismic responses, which are formed on acoustic inhomogeneities due to the oil saturation factor). In addition, one has to face almost universal manifestation of seismic recording interference (weak signals, of course, interfere with stronger ones associated with lithology and porosity). As in assessing the contribution of lithology, porosity, and water saturation, the effect of oil saturation on the seismic recording dynamics and PAL transformations was evaluated from the standpoint of the substitution principle of the hydrocarbon saturation factor of a reservoir by formation water through the velocity and density of the substituting components. The ratios of contributions from virtually all the considered geological indicators for the target objects (oil-saturated strata 2 and 3)

2.2 Quantitative Assessment of the Geological Section Parameter Contribution

43

are determined in the form of the corresponding numerical values of the information before and after using replacement components. The results of studying the effect of reservoir oil saturation on the dynamic features of the seismic recording and PAL transform are shown in Fig. 2.9. In general, oil saturation, as well as water saturation, manifests itself in relatively low values of the graphical and numerical information under consideration (on the curves of the oil saturation contribution from permeable formations 2 and 3). The results of the seismic modeling clearly show that the values on the oil saturation contribution curves, confined to formations 2 and 3, on the presented wave field curves (Fig. 2.9f–h) and PAL transformations (Fig. 2.9l–n) only in rare points exceed values
20%. At the considered time intervals of the seismic recording, the local contribution curves of oil saturation are located in relation to the seismic trace (Fig. 2.9h) and to the PAL trace (Fig. 2.9n) at relatively low values of interference amplitudes (i.e., in those recording areas where all local components interfere with each other). To increase the sensitivity of seismic data with respect to changes in oil saturation, the authors used materials from the well, in which reservoirs were discovered with a slightly greater oil-saturated thickness than on those considered above (Figs. 2.5–2.9). The well chosen by the authors (Fig. 2.10) was drilled in the same research area as the well, for which the 1D seismic simulation considered above was obtained and has oil-saturated thickness of permeable interlayers, much higher than those examined on the materials of the previous well (Figs. 2.5–2.9). The processing of well log data for the considered well (Fig. 2.10) was also performed by the authors according to the method of functional transformations of well logs by integrating geophysical parameters into information systems (see Sect. 5.1 and [6, 7]). A distinctive feature of the geological structure of the reservoir 3 is that the mineralogical composition of the reservoir is polymictic rocks with quartz and feldspars. The reservoir cement is essentially clayey with a kaolinite content (up to 85% in coarse sandstones). The carbonate component of the pore cement is represented by siderite, calcite + dolomite (which is important for the permeability of reservoir rocks, which largely depends on the structure and composition of clay cement). In the well section, rocks are interbedded with diametrically with the opposite grain size and structural maturity, which, together with significant and uneven compaction, predetermines a wide variation of reservoir properties. For the well under consideration (Fig. 2.10), the maximum values were obtained at a depth of 2075.6 m: oil permeability, ko ¼ 27.04 mD (milliDarcy); porosity, Kp ¼ 19.7%; and oil saturation coefficient, Ko ¼ 62.9% (and, on average, for the formation
10.6%). In general, it can be stated that the opened depth interval is thin-layered, having reservoir properties with good productivity. The simulation results for assessing the effect of oil saturation of formation 3 on the dynamics of seismic record and its transformations are shown in Fig. 2.11. Despite the fact that the oil saturation of the reservoir 3 here is significantly higher in oil saturated thickness than in the previous well (28.3% in thickness, compared to 7.27% in reservoir 3 in the section of the previous well), in general oil saturation has lower values oil saturation contribution curves. These relatively low values are

44

2 Seismic Modeling of Wave Field Dynamic Parameters

Fig. 2.10 Litho-stratigraphic column with distribution of fluid saturation character and degree of the Lower Cretaceous and Upper Jurassic reservoir rocks

Fig. 2.11 Detailed quantitative assessment of the oil saturation contribution, it display in the interference seismic record and in pseudo-acoustic transformation for a productive formation with a greater oil-saturated thickness

2.2 Quantitative Assessment of the Geological Section Parameter Contribution 45

46

2 Seismic Modeling of Wave Field Dynamic Parameters

recorded both on the seismic record (Fig. 2.11g–i) and on its pseudo-acoustic transformations (Fig. 2.11m–o). At the considered recording time intervals, the oil saturation contribution curves are located with respect to the seismic trace (Fig. 2.11i) and to the PAL trace (fig. 2.11o) also at relatively low values of the interference amplitudes. However, on the whole, despite the fact that oil saturation (as some acoustic heterogeneity of a real geological section) occurs in the seismic recording time interval, on which relatively small reflection coefficients are recorded, the contribution of this geological target is still quite significant, sometimes (in individual points) reaching 20–30% of the original interference wave field. As a result of PAL seismic recording transformations, not only the interference amplitude ratios are redistributed but also the oil saturation contribution curve itself, with a certain redistribution of the percentages of this contribution (Fig. 2.11g–h, n– o). At the same time, it can be seen that the local amplitudes on the curve of the oil saturation contribution on the ICM for the initial seismic record (Fig. 2.11h) turn out to be slightly higher than on the ICM for the PAL trace (Fig. 2.11n). The main conclusions of the research are as follows: 1. Quantitative assessment of the geological indicator contribution (lithology, porosity, water, and oil saturation) that are most important for research and industrial prospecting to the seismic wave field and its parameters using 1D seismic modeling showed that their influence on the record dynamics and its pseudo-acoustic transformations (PAL) is generally different and quite significant. Among the indicators mentioned, lithology is characterized by the strongest influence—its contributions to the interference seismic recording and the PAL transform are in the range of 40–80%, sometimes reaching 90%. The effect of porosity on average is estimated at a range of 20–40%, reaching 60% only at selected points. The effect of water and oil saturation on the dynamics of recording and PAL is significantly lower compared to lithology and porosity and averages 10–15%, reaching only 20–40% in rare points. 2. The results of studies assessing the effect of geological indicators on the dynamics of seismic recording and PAL transformation show that there is a real opportunity to determine the location of displaying specific target parameters at specific time intervals of seismic recording of the studied sections in conditions of practically ubiquitous interference. In these intervals, as shown in the simulation results, the maximum possible information (contribution) from specific geological characteristics is located, which ultimately can significantly affect the increase in the prediction efficiency of seismic data of various geological indicators. 3. The outcomes of determining the contribution to the seismic wave field and its pseudo-acoustic transformation of such geological characteristics as porosity and oil saturation (characterized by relatively weak seismic signals—local responses from the associated acoustic heterogeneities) imply the following seismic preliminary processing and would provide the most minimal distortion of its dynamics necessary to preserve in it the identifying information of the target geological indicators. This is provided based on the application of a special graph developed by the authors for processing and interpreting seismic survey data using the procedures of high-resolution seismic technology (see Sect. 4.3 and [8]).

References

2.3

47

Summary

On the basis of wave field dynamic parameter seismic modeling, the question of the phenomenon of reflected wave comprehensive interference is revealed, taking into account the contribution of elementary boundaries and strata, as well as the contributions of lithology, porosity, and oil and gas saturation to the seismic wave field and its various transformations. The information content of the thin-layer section elements is analyzed using the “interference contribution matrix” (ICM). Using this matrix, the contributions of local responses from lithology, porosity, and oil and gas saturation to the seismic wave field and to the results of its various transformations are estimated. Based on the results of seismic modeling, it was found that the most powerful influence is characterized by lithology—its contribution to the interference seismic record is in the range of 60–80%, sometimes reaching 90%. The effect of porosity on the seismic record and its pseudo-acoustic transformations (PAL) is estimated on average in the range of 10–30%. The effect of oil saturation on the dynamics of the record in comparison with the lithology and porosity is significantly lower and is on an average of about 10–15%.

References 1. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2011). Otsenka vklada elementarnykh granits i tolshch v seysmicheskoye volnovoye pole dlya mnogosloynykh pogloshchayushchikh sred (Assessment of the elementary boundaries and strata contribution to the seismic wave field in multilayer absorbing media). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 86–96. 2. Gogonenkov, G. N. (1987). Izucheniye detal’nogo stroyeniya osadochnykh tolshch seysmorazvedkoy (The detailed sedimentary strata structure study by seismic exploration). M., Nedra, p. 221. 3. Khaziev, F. F., & Trofimov, V. L. (2003). Model'nyye issledovaniya rezul'tatov resheniya obratnoy dinamicheskoy zadachi seysmiki (Model studies of the solving inverse dynamic seismic problem results). Spetsial'nyy vypusk Geofizika: Tekhnologii seysmorazvedki (Seismic Technologies), 27–37. 4. Milashin, V. A., Trofimov, V. L., & Khaziev, F. F. (2004). Vydeleniye slabykh signalov metodom seysmicheskoy inversii (Weak signals detection by the method of seismic inversion): VII Scientific-Practical Conference “Ways of realizing the oil and gas potential of the KhantyMansiysk Autonomous Okrug”, t. 2, pp. 295–307. 5. Trofimov, V. L., & Khaziev, F. F. (2011). Izucheniye vliyaniya sostava i svoystv porod na geologo-geofizicheskiye parametry nefteperspektivnykh otlozheniy (Study of the influence of the composition and properties of rocks on the geological and geophysical parameters of oilpromising deposits). Tekhnologii seysmorazvedki (Seismic Technologies), 1, 22–33. 6. Trofimov, V. L., Khaziev, F. F., & Milashin, V. A. (2007). i dr. Avtomatizirovannaya obrabotka i interpretatsiya dannykh GIS dlya obnaruzheniya nefteperspektivnykh obyektov metodami vysokorazreshayushchey seysmiki (Automated processing and interpretation of well logging data for the detection of oil-prospective objects by high-resolution seismic methods). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 54–66.

48

2 Seismic Modeling of Wave Field Dynamic Parameters

7. Zalyayev, N. Z. (1990). Metodika avtomatizirovannoy interpretatsii geofizicheskikh issledovaniy skvazhin (Automated interpretation technique of the well logging data). Minsk. Izd. Universitetskoye, p. 142. 8. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., et al. (2009). Spetsial'naya obrabotka i interpretatsiya dannykh seysmicheskikh nablyudeniy v slozhnykh geologicheskikh usloviyakh metodom vyokorazreshayushchey seysmiki (Special processing and interpretation of seismic observation data in difficult geological conditions by the method of high-resolution seismics). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 36–50. 9. Trofimov, V. L., & Khaziev, F. F. (1991). Modelirovaniye volnovykh poley dlya mnogosloynykh pogloshchayushchikh sred s otsenkoy vklada elementarnykh granits i tolshch (Modeling of wave fields for multilayer absorbing media with an assessment of the contribution of elementary boundaries and strata). BelNIGRI collection of scientific papers “Izucheniye glubinnogo stroyeniya Pripyatskogo progiba metodami razvedochnoy geofiziki (Study of the Pripyat trough deep structure by methods of exploration geophysics)”. pp. 3–14. 10. Khaziev, F. F., Trofimov, V. L., & Shkol'nik, S. A. (2014). Kolichestvennaya otsenka vklada geologicheskikh pokazateley v interferentsionnuyu seysmicheskuyu zapis' i yeye psevdoakusticheskiye preobrazovaniya (Quantitative assessment of the geological indicators contribution to the interference seismic record and its pseudoacoustic transformations). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 70–83. 11. Boganik, G. N., & Gurvich I. I. (2006). Seysmorazvedka (Seismic exploration). Izd. AIS, p. 744. 12. Pudovkin, A. A. (1984). Algoritmicheskoye i metodicheskoye obespecheniye litologicheskogo raschleneniya otlozheniy po dinamicheskim kharakteristikam otrazhennykh voln v metode mnogokratnykh perekrytiy (Algorithmic and methodological support of lithological dismemberment of sediments according to the dynamic characteristics of reflected waves in the method of CDP). M., Razvedochnaya geofizika (Exploration Geophysics). VIEMS Review, p. 65. 13. Tyapkin Yu, K., Bel'fer, I. K., Pogozhev, V. M., Mushin, I. A., & Mitrofanov, G. M. (1986). Otsenka vozmozhnostey ispol'zovaniya mgnovennykh dinamicheskikh kharakteristik seysmicheskikh zapisey pri poiskakh nefti i gaza (Evaluation of the possibilities of using the instantaneous dynamic characteristics of seismic records in the search for oil and gas). M., Razvedochnaya geofizika (Exploration Geophysics). VIEMS Review, p. 76. 14. Ptetsov, S. N. (1989). Analiz volnovykh poley dlya prognozirovaniya geologicheskogo razreza (Analysis of wave fields for predicting a geological section). M., Nedra. p. 135. 15. Kozlov Ye, A. (1977). Prognosticheskaya fil'tratsiya kratnykh voln v neideal'nykh usloviyakh (Predictive filtration of multiples in imperfect conditions). M., Nedra, Prikladnaya geofizika (Applied Geophysics). Release 87, pp. 3–19. 16. Zenov, A. A., Malkin, A. L., Sorin, Y. A., & Finikov, D. B. (1985). Sovremennyye metody obratnoy i korrektiruyushchey fil'tratsii seysmicheskikh zapisey (Modern methods of inverse and corrective seismic records filtering). M., VNIIOENG. Obzornaya informatsiya (Overview information). Seriya neftegazovaya geologiya i geofizika (Oil and gas geology and geophysics series), p. 60. 17. Kanasevich, E. R. (1985). Analiz vremennykh posledovatel'nostey v geofizike (Time sequences analysis in geophysics). M., Nedra. p. 300. 18. Sil'via, M. T., & Robinson, E. A. (1983). Obratnaya fil'tratsiya geofizicheskikh vremennykh ryadov pri razvedke na neft' i gaz (Inverse filtering of geophysical time series in oil and gas exploration). M., Nedra, p. 247. 19. Trofimov, V. L., Khaziev, F. F., & Trofimova, A. V. (2018). Tekhnologiya VRS-Geo. Izucheniye nefteperspektivnykh ob"yektov metodom vysokorazreshayushchey seysmiki (HRS-Geo technology. Study of oil-prospective objects by the method of high-resolution seismic). “Oil & Gas Journal Russia”. № 1–2(123), pp. 28–35.

References

49

20. Geneticheskiye Algoritmy. (2017). 1. Vvedeniye. 2. Osnovnyye ponyatiya. 3. Klassicheskiy geneticheskiy algoritm. (Genetic algorithms. 1. Introduction. 2. Basic concepts. 3. Classical genetic algorithm). Retrieved July 24, 2017, from https://coderlessons.com/ 21. Aoki, M. (1977). Vvedeniye v metody optimizatsii (Introduction to optimization methods). Nauka, M., p. 344. 22. Kozlov Ye, A. (2006). Modeli sredy v razvedochnoy seysmologii (Medium models in exploration seismology). Tver': GERS, p. 480. 23. Trofimov, V. L., Khaziev, F. F., & Milashin, V. A. (2012). Dinamicheskiye kharakteristiki otrazhennykh voln s uchetom vklada elementarnykh granits i tolshch (Dynamic characteristics of reflected waves taking into account the contribution of elementary boundaries and strata). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 12–24.

Chapter 3

Methods for Solving Inverse Dynamic Seismic Problems

Abstract The features of the theory and practice of the inverse mathematical problem study, which are laid down and developed in the fundamental works of many domestic and foreign researchers, are discussed. The solution of the inverse dynamic problem of seismics (IDSP) in the classical formulation is justified. The conditions for the existence, uniqueness, and stability of such a solution are determined. On the example of the works of A.S. Alekseev, we consider the formulation and example of solving inverse dynamic seismic problems, where the functions VP(z), VS(z), and ρ(z) are uniquely determined in the spectral representation of solutions to direct problems and the subsequent inversion of such representations. A brief overview of various inversion transform technologies is given, the choice of which depends on the quantity and quality of the observed geological and geophysical information, as well as on the set geological tasks for determining the composition and properties of the studied section rocks. Numerous practical methods and techniques of inversion are very different from the so-called classical methods of IDSP, which are usually formulated as inverse problems of mathematical physics. According to the type of seismic data used, its inversion is realized both before and after stacking. An original method of indicating hydrocarbons based on the use of seismic record dynamic parameters is considered. In this case, the dynamics of the seismic record is used for direct search for oil and gas using the average time equation and wave field amplitudes. One of the main disadvantages of this approach is the involvement of interference wave fields as input data for solving the inverse problem. The exclusion of wave interference from the seismic data leads to a significant increase in the reliability and detail of predicting geological indicators in the search for oil and gas deposits. In addition, modern approaches and methods of inversion do not work without well data as a priori information.

Chapter 2 presents the results of solving the direct problem. It is formulated as follows: for a given source of the field and a model of the medium that determines the interaction of this medium and the field, it is required to find the wave field [1]. The examples presented in Chap. 2 reflect the quantitative assessment of the contributions of elementary boundaries or sequences to the stack interference seismogram, as well as the results of studying the contributions to the seismic wave field © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_3

51

52

3 Methods for Solving Inverse Dynamic Seismic Problems

of such geological indicators as lithology, porosity, and water and oil saturations. For seismic prospecting, as well as for other methods of exploration geophysics, the theory and practice of methods for solving direct problems are developed quite well [2–9]. On this basis, various approaches to the processing and interpretation of geophysical data are usually built. In accordance with [2, 10], for the direct dynamic seismic problem, the conditions for its mathematical correctness (existence, uniqueness, continuous dependence of the solution on the input data) and methods for obtaining an effective solution are given. Modern methods of computational mathematics allow, as is well known, to obtain a numerical solution of direct dynamic problems for fairly general cases of the nonuniform structure of the real environment. Direct tasks make it possible to analyze the regularities of field formation in various geological environments with given sources of fields. Having solved them for a certain class of models, one can understand the physics of the processes and set the problem of selecting a model, the field for which would coincide with the measured, i.e., solve the inverse problem [11]. The formulation of the direct problem and the choice of methods for solving them and analyzing the results by quantifying the degree and nature of the initial model studied characteristics influence make it possible to choose the class (complexity) of the model, optimally describe (parameterize) the working model, and determine the applicability of the selected model. On the same basis, we can formulate the inverse problem, determining the conditions of existence, uniqueness, and stability of the solution. Iteratively testing a model with different parameters determines the region of solution existence. If solution instability is detected in the inversion process, then the algorithm is configured to obtain an approximate solution by means of regularization. Regularizing parameters (creating approximate, but quite adequate models) must satisfy certain conditions. The following is implemented: as the effect of a parameter decreases, the algorithm must continuously reduce the solution to the true one, provided that there is no noise. The main feature of an optimally regulated model is compliance with the characteristics of a real subsurface environment with an acceptable dispersion (less than in the absence of regularization). By setting adequate constraints for the model search area and for the expected decisions, the working algorithm of the inversion process is determined. The question of the detailed model description and the range of the model class in the case of direct problems is associated with the tendency to obtain sufficiently fine physical laws. This section discusses some general concepts of solving an inverse dynamic seismic problem (IDSP), affecting this direction of research in so far as it concerns the author’s development—the HRS-Geo technology. In this case, the inverse problem is formulated in the following way: data describing the wave field outside of the subsurface medium or on some part of this medium, a class of models for the solution search, noise characteristics, and constraints on the solution are given; it is required to determine (estimate) the characteristics of the medium under study [1]. That is, with respect to seismic studies, such inverse problem solution means the parameter restoration of the oil and gas reservoir from the observed seismic wave field.

3.1 A Brief Review of Seismic Data Interpretation Mathematical Problems.

53

The section below provides a brief overview of mathematical interpretation tasks (Sects. 3.1–3.3); a brief methodology for solving the IDSP implemented in the HRS-Geo technology (Chap. 4) is presented. Examples of solving such problems on test and real seismic materials (Sect. 4.2) and using the complex optimal graph of seismic data preprocessing (Sect. 4.3) are considered.

3.1

A Brief Review of Seismic Data Interpretation Mathematical Problems.

In the absence of the direct measuring possibility the characteristics of the object being studied, it is necessary to solve inverse problems, performing them on the basis of processing and interpreting the available observable data. Many inverse problems are among the so-called incorrectly set ones—when processing approximate data obtained, for example, from an experiment, arbitrarily large changes in the solution can correspond to small changes in the input data. The contemporary theory of solving incorrectly defined problems, as is known, based on the works of Russian mathematicians A. Tikhonov, V. K. Ivanov, M. M. Lavrent’ev and their scientific schools, allows to overcome the arising difficulties [1, 11–15]. In this subsection, only some inverse problems solution features are touched upon, and the main possibilities of such a research line, which are opened as a result of the use of modern mathematical and numerical methods and computational resources, are shown. Their effectiveness, as we know, is confirmed by practice. We present some features of the formulation and solution of inverse dynamic seismic problems in the form of partial schemes, as shown in the works of A.S. Alekseev [2, 10]. The schemes developed by him make it possible to uniquely determine the functions VP(z), Vs(z), and ρ(z) in certain depth intervals z with the spectral representation of direct problem solutions and the subsequent inversion of such images. In [10], by observing the SH waves from a point source of elastic oscillations for determining Vs(z) and ρ(z), the solution using the method of variables separation was constructed. Inversion formulas for the Fourier-Bessel integral, various mathematical transformations, some solutions of special representations of the potential function e qk ðxÞ using the Riccati equation system, M.G. Krein’s inverse boundary value problem [16], methods for constructing the objective function from given spectral functions (methods of V.A. Marchenko, I.M. Gelfand, and B.M. Levitan [17, 18]) through the solution of the M.G. Krein integral equation, which is a second-type Fredholm equation for a fixed value of the parameter x were used. By varying the parameter x and solving each time (iteratively) using the M.G. Krein equation, we obtain the function G2x(2x), which is used to solve the inverse problem for a given spectral function ρ(λ) and measure the function Gz(t) [10, 16]. The necessary substitutions, various transformations, and the expression solutions of the obtained integral and differential equations with the fulfillment of the additional conditions “А,” “Б,” В,” “Г,” and “Д” [2, 10] were used.

54

3 Methods for Solving Inverse Dynamic Seismic Problems

We show only some features of the inverse problem solution using the wave equation in a cylindrical coordinate system, which are as follows. It is assumed that an impact effect is applied to the boundary z ¼ 0 in the form of the surface moment of forces σ z ¼ 0, σ rz ¼ 0, τϑz ¼ a(t)b(r) at z ¼ 0, where z and θ¼arctg(y/x) are cylindrical coordinates. In a vertically inhomogeneous medium, only transverse elastic waves of the SH type arise, whose displacement vector has the form ! ! ! U ðr, ϑ, z, t Þ ¼ U ϑ ðr, z, t Þ ϑ 1 , where ϑ 1 is the unit vector of the cylindrical system ! and Uϑ is the vector component of the direction ϑ 1 . The process of oscillation propagation Uϑ(r, z, t) for the half-space z  0 is described by the [2, 10]: 2 2 2 ∂ U ϑ 1 ∂U ϑ U ϑ ∂ U ϑ μ0z ðzÞ ∂U ϑ ρðzÞ ∂ U ϑ  ¼ þ þ þ , r ∂r μðzÞ ∂z μðzÞ ∂t 2 r2 ∂r 2 ∂z2   ϑ with interface condition ∂U ¼ μ1 aðt Þbðr Þ and initial data Uϑ|t < 0  0. ∂z  z¼0

0

The physical essence of this task is as follows. The right side of the wave equation describes the behavior of medium particles in space, the left one is their motion in time. The medium parameters μ(z) and ρ(z) tie in (synchronize) the spatial distribution of the medium particle displacement along z with a time factor for the argument t. To bring the medium out of equilibrium state, the interface condition on the surface z ¼ 0 is specified in the form of a pulse varying in lateral r and in time t according to the law μ1 aðt Þbðr Þ, where μ0 is the medium shear modulus in the wave pulse 0 generation area. The fact that the medium was in equilibrium, until time t ¼ 0 is specified by the initial condition Uϑ|t < 0  0. In addition, this condition allows us to identify a single solution, providing the problem with a uniqueness property. By setting the initial impulse a(t)¼δ(t) as a Dirac function, the function b(r) through the expression using the Bessel function J1(kr) and also by virtue of a condition that corresponds to the surface intensity rotation δ(t), we look for integrals for Uϑ(r, z, t) and U(z,t,k). Note that the use of a shot pulse a(t) approximation by the Dirac function simplifies the task, eliminating the consideration of problems associated with the influence of the shape and energy of the excitation pulse and reducing the task to working with impulse response or transfer function of the medium (for solving practical problems, this assumption is not allowed, i.e., finding the characteristics of the shot impulse, and the transition to the medium impulse response is a separate target and a complex problem in seismic exploration). In integral expres1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ð sions, the values σ s and x have the form σ s ¼ ρðzÞV s ðzÞ ¼ μðzÞρðzÞ, x ¼ Vdς . s ð ςÞ 0

In the new variables x and W(x,k,ω), the problem are reduced to solving equations: d2 W dx2

  þ ω2  qk ðxÞ W ¼ 0; dW dx  h1 W ¼ 1 with x ¼ 0;

moreover, a continuous function is determined by the formula qk ðxÞ ¼ V 2k k 2 þ  0 2 σ 00 σ 0 ð0Þ 3 σs  12 σss , and the h1 has a value: h1 ¼  12 σ ss ð0Þ. Due to the presence in one of 4 σs

3.1 A Brief Review of Seismic Data Interpretation Mathematical Problems.

55

the last parameter expressions k2, it is possible to independently determine VS(z) and ρ(z). Note that in practice, the separate determination of the wave velocity and density of the medium, which are included in the acoustic impedance in the form of a product, presents considerable difficulties due to the ambiguity of the product and the presence of a complex relationship between them. Based on such transformations, parametric functions VS(z) and ρ(z) with necessary and sufficient conditions for the solution existence are obtained. The actual calculation construction requires the solution of the M.G. Krein integral equation, which is performed by numerical methods. Based on the general results of A.N. Tikhonov [13] and the uniqueness of the solution to the inverse problem under consideration, the stability of the solution with respect to the small errors ε(r, t) in the input data can be seen. The determination of Vp(z) from the observation of longitudinal waves for a point source is made for a horizontally layered half-space z  0 [2, 10]. Due to the hyperbolicity of the wave propagation process and the concentrated nature of the primary perturbation, the functions integration for the displacement components is always carried out in finite limits. The transition from the displacement function Uz (x, y, z, t) to the function W(z, t) consists in replacing the point source problem with a stress σ z | z = 0 = δ (t) uniformly distributed in the plane z ¼ 0 one. In this case, a 1D process of propagation of “normally incident” plane waves occurs. The component Uz exactly coincides with the function W(0, t), and the experiment on observing the function W(0, t) can practically be performed using the plane front (wave) technique. By specifying the function G(t) ¼ W (0, t) for the derived formula W(0, t), the spectral function ρ(λ) is determined, and it is used to solve the inverse problem for an equation system of Sturm-Liouville type and thereby determine the function σ p(x). Then again using the M.G. Krein method, an expression is found for the kernel of an integral equation, which, as noted above, is a Fredholm-type equation for a fixed value of the parameter x. For this kernel Hp(t), the final formula is obtained: H p ðt Þ ¼

0 1 Gz ðt Þ 2 G ð 0Þ

In this equation G0z is the time derivative of the function described by the total oscillation of the point z ¼ 0. Using the expression and the function Г2x (t) as a solution of the special Krein equation [2, 16]: Ð2x Г 2х ðτÞ þ αG0z ðτÞ þ α Г2x ðsÞГ 0z ðτ  sÞds ¼ 0; τ  2x, where α =  σ p(0)/2. 0

and the formula for acoustic impedance is found under the assumption that α is known:

σ p ð х Þ ¼ σ p ð 0Þ e

4

Ðх 0

Г ð2ζÞ ð2ζ Þdζ

:

56

3 Methods for Solving Inverse Dynamic Seismic Problems

Ðx wherein z ¼ V p ðζ Þdζ. Assuming that α is known, according to a given Gz(t), 0

only acoustic impedance is determined from the Krein equation without determining the functions Vp(x) and ρ(x) separately or at least one of them. Putting ρ(x) ¼ ρ0, the approximate value of Vp(z) is determined by the formulas Ðx σ ðxÞ σ ð xÞ V p ðxÞ ¼ pρ , and z ¼ pρ dx. The exact value of Vp(z) is determined if ρ(z) is 0

0 σ p ðxÞ ρðzÞ ,

0

and bearing in mind the relationship between x and z, known. Then V p ðxÞ ¼ one can use the differential equation dz ¼ Vp(x)dx, equivalent to the equation ρ(z)dz ¼ σ p(x)dx. Solving this equation under the condition z ¼ 0 and x ¼ 0, we obtain the equality z ¼ φ(x) (with the well-known function φ(x)), which ultimately determines the desired function Vp(z). It should be noted that in the process of solving the Krein integral equation by the iteration method, limited to the first approximation, an important approximate solution of the inverse problem is obtained, which is represented as: ln

σ p ðxÞ Gð2xÞ ¼1 : σ p ð 0Þ G ð 0Þ

The obtained formula turns out to be a modification of R. Bortfeld and E.V. Nikolsky. It is physically interpreted as the ratio between σ p(x) and G(t) in a problem with normal propagation in a heterogeneous mediumhof a plane i longitudinal 1 z wave with the shape of the primary pulse U ðz, t Þ ¼ σp ð0Þ δ t  V p ð0Þ disregarding multiple waves. The solution of this inverse problem without taking into consideration multiples in the medium can be written in the form: h σ p ð х Þ ¼ σ p ð 0Þ e

Gð2xÞ

1 Gð0Þ

i :

As shown in [10], for smooth and monotonously varying along the depth of the medium parameters, this approximation often gives good accuracy. However, as shown by the numerical experiment, the accuracy of this approximation turns out to be very low as soon as the medium becomes inhomogeneous (there are a large number of layers in the medium with lower σ p(x) values). As a rule, the real geological environment is layered (thin-layer), i.e., highly heterogeneous, and, as a result, the solution of inverse dynamic problems in practice becomes much more complicated. In the same work, A.S. Alekseev considered more rational approaches to solve the inverse problem under consideration: based on the use of the numerical formation of the medium impulse transition function from seismic data; constructing an inverse problem for a 3D wave equation in an heterogeneous half-space; some numerical methods for solving inverse dynamic problems as an optimization method, the adjoint direct problem operator, the linearization of the inverse problem

3.1 A Brief Review of Seismic Data Interpretation Mathematical Problems.

57

operator, and the inversion of difference schemes of the direct problem operator. The author [10] notes that “. . .the method of inverse dynamic problems, even if one does not bear in mind its practical use, can already serve as a new tool in the theoretical seismic analysis of general quantitative relationships between the properties of wave fields and the structure characteristics of the studied media.” It should be mentioned that the theory and practice of the mathematical physics inverse problem study founded and developed the fundamental works of the scientists of our time—A. N. Tikhonov, A. S. Alekseev, M. M. Lavrentiev, V. G. Romanov, I. M. Gelfand, B. M. Levitan, M. G. Krein, V. A. Marchenko [2, 12, 16–24], etc. Further development of research related to the solution of the inverse dynamic seismic problems are found in the works of V. G. Yakhno, S. I. Kabanikhin, B. G. Mikhailenko, M. S. Zhdanov, A. G. Fatianov [21, 23, 25–27], and others. The method of inverse problem studying, as is well known, was applied in studies of a wide range of inverse problems by their students, colleagues, and followers— Yu.E. Anikonov, V.Ya. Arsenin, A.V. Baev, A.S. Barashkov, A.V. Belinsky, A.S. Blagoveschensky, B.A. Bubnov, A.L. Bukhheim, M.I. Belishev, N.P. Volkov, V.I. Dmitriev, A.D. Iskanderov, V.K. Ivanov, A.L. Karchevsky, V.S. Kornilov, M.V. Klibanov, A.N. Kremnev, V.A. Morozov, R.G. Mukhometov, B.S. Pariyski, V.I. Priimenko, A.I. Prilepko, T.P. Pukhnacheva, V.N. Strakhov, V.P. Tanana, A.M.Fedotov, V.A. Cheverda, V.G. Cherednichenko, A.G. Yagola, and others. A number of results in this direction have been obtained in recent decades by foreign authors, such as D.G. Berriman, M. Grazelli, R.R. Green, R. Barridge, I.M. Gen, A. Lorenzi, D.K. Liu, A. Rakesh, P. Saks, V.V. Sayms, F. Santoza, etc. The list of work bibliographies by noted authors is quite impressive, and it is almost impossible to enumerate it. Bearing in mind the above, and, as noted in [2], most of the seismic practical tasks are adequate in the formulation of the corresponding inverse dynamic problems. However, the difficulties of obtaining, the weak stability of solutions to inverse problems and the usual lack of initial information in practice do not allow us to avoid attracting direct problems and additional data. In this regard, the desired equivalence of direct and inverse problems in the propagation theory and in the methods of processing seismic fields has not yet been achieved. Direct problems, as is known, can now be solved numerically for virtually any heterogeneous 3D model of the medium. Inverse dynamic problems are numerically solved only for 1D and close to 1D models of media. This gap does not allow the use of a closed-loop research of complex practical problems of seismology and seismic exploration. It is worth emphasizing that the use of methods for solving and interpreting inverse dynamic problems requires the researcher to equally have professional knowledge and intuition in the field of geology, geophysics, and mathematics. Therefore it is necessary to establish a broad partnership of geologists and geophysicists with mathematicians. Below, in Chap. 4, the method of solving the inverse dynamic seismic problem is considered in detail, which was put into the software and algorithmic basis of the

58

3 Methods for Solving Inverse Dynamic Seismic Problems

HRS-Geo technology, and Sect. 4.2 gives examples of solving this problem on test and real materials.

3.2

Inversion Technologies to Refine the Seismic-Geological Model

Many oil and gas companies use inversion technology to increase the information content of seismic data and improve the reliability of forecasts, for example, to assess the porosity and filtration-capacitive properties (FCP) of rocks. Currently, there are various inversion technologies, and their choice depends on the quantity and quality of the observed geological and geophysical information, as well as on the geological tasks set for determining the composition and properties of rocks of the studied section. In this case, the simplest of these is acoustic inversion, and the most complex is geostatistical. The inversion results, together with the dynamic characteristics of the wave field, are interpreted using the results of statistical analysis methods to directly predict the FCP of the sediments under study. Component and factor analysis methods, statistical classification methods, multidimensional regression analysis, models of artificial neural networks, etc. are also actively used. Relationships are established between different seismic attributes and the reservoir FCP, for example, with a porosity coefficient КP determined from well log data. An effective tool for interpreting the high lateral variability of reservoir properties is usually 3D seismic data inversion technology into cubes of elastic parameters (acoustic and shear impedances, density, velocity of longitudinal, and transverse waves and their derivatives), which are related with the FCP using materials from laboratory core studies or the results of petrophysical interpretation of GBS data. The solution of the inverse dynamic seismic exploration problem, aimed at restoring the elastic parameter distribution of the geological medium over the registered wave field, is known to be called seismic inversion. Numerous practical methods and techniques of inversion are very different with the so-called classical methods of IDSP, which are usually formulated as inverse problems of mathematical physics. By the type of seismic data used, their inversion is realized both before and after the data stacking. From the entire set of algorithms, inversion algorithms are implemented, providing both the resolution of seismic data and the results that allow one to get closer to the well log data resolution. The seismic data inversion task, as described in numerous papers of this research line, can be considered as deterministic or geostatistical (stochastic). The result of the deterministic inversion is the unique model of elastic properties that satisfies the seismic data and a priori constraints, and the result of the stochastic inversion is essentially a set of equally probable realizations of the elastic and discrete property distribution in the real environment subsurface model.

3.2 Inversion Technologies to Refine the Seismic-Geological Model

59

For the formation of this section, the authors used materials placed in the works of domestic and foreign researchers [19, 28–64]. This section provides some examples of the results obtained in these works, highlighting the features of the various inversion transforms application based on the use of specialized software packages and individual developments.

3.2.1

Acoustic Deterministic Inversion

In the conditions of a seismic data inversion algorithms variety, it is possible to choose the optimal ones for specific seismic-geological situations. The least demanding to the a priori volume of well information is acoustic inversion, which is most often used in seismic exploration. Acoustic inversion involves several steps: (a) linking seismic data with well information; (b) building a low-frequency background (trend) model; and (c) carrying out the acoustic inversion procedure itself. (a) Stratigraphic binding of the reference reflectors using traditional time sections is carried out, as known, by matching the time sections with synthetic seismograms derived from the use of the stratified lithological and acoustic models. It is an important stage of interpretation, since the accuracy of structural constructions and the results of further interpretation depend on the correct and accurate identification and tying of reflections (reflecting horizons) to the corresponding geological boundaries [30, 45, 54]. Practically, the tying of the wave field to the well data is carried out using one of the software packages selected for operation based on the use of 1D seismic modeling, which sequentially implements the loading of the initial information on the wells; calculation of the synthetic trace; and linking seismic and synthetic trace. Most often, this uses a statistical impulse extracted from seismic data in the profile close to the near-wellbore space. The input data for the 1D modeling are the materials of acoustic and density logging (usually this is the original sonic SN curve or data recalculated from other methods of NL, LL, density GGR curve, etc.) and the real seismic wave field trace located in the zone of vertical sections of the analyzed wells. The model of the real medium used, based on well data, should also be adequate, i.e., acceptable for modeling the seismic wave propagation process in a real subsurface environment. At the same time, GBS data, namely, SN and GGR methods, obtained from measurements in wells, have their own characteristics (as well as specific distortions), due to the measurement technology. For SN and GGR methods, this is usually the Z-distortion, the influence of the well design, a significant difference in the wave frequency composition of the acoustic method from the seismic ones, and a small coverage of the measured rock volume (only the wellbore part, which is partially deformed by the drilling process, the appearance of various zones of drilling mud penetration). Often, the absence of acoustic or density logging curves makes it necessary to simulate the GBS curves and recalculate them from

60

3 Methods for Solving Inverse Dynamic Seismic Problems

other log methods (e.g., find dependencies for different rock complexes and calculate acoustic data from electrometry data or determine data on rock density from acoustic logging data). Synthetic traces are compared with the wave pattern obtained at the sites of seismic profiles closest to the wells. At the same time, the similarity of the dynamic features of real and synthetic traces is visually observed and quantitatively assessed. The made tying allows to establish the correspondence between the main seismic and logging reference points within the studied area. As a result of such a binding, stratification of seismic benchmarks occurs, which are confined to regionally sustained formations and to regional interruption horizons in sedimentation and to some intermediate reflections. It should be said that the procedures for preliminary processing of seismic data can contribute to the distortion of the seismic wave field. Seismic amplitudes should largely correspond to the reflection coefficients and take into account the signal energy loss due to divergence and attenuation. On the sections of the wave field, the residual of multiple waves is often preserved; unknown level, nature and degree of wave absorption influence (insufficient absorption compensation); distortion of multichannel smoothing; and distortions introduced by various procedures and the seismic data processing graph in general. To correctly compare the model wave field obtained from well data and the actually measured and processed seismic wave field, it is necessary to take into account the above and a number of other distorting features in the GBS and seismic data. The model of the studied section obtained from well data should be unambiguous for the formation of the comparative wave field characteristics, the structure of which would most fully present the features of the real geological medium obtained during the processing of the input seismic data. These and a number of other features (factors) of the wave field often determine the ambiguity of reflector identification attached to the model of the geological section. The disappearance of the thin-layer nature of the boundaries on the compared wave fields virtually eliminates the tying of many of the reflectors (especially those that are characterized by relatively weak intensity due to the relatively low reflection coefficients). (b) As noted above, the advantage of acoustic inversion is its comparative computational simplicity, since it requires minimal specialized seismic data preparation. The main problems arising from its implementation are the evaluation of the seismic impulse shape and the addition of a low-frequency component (up to 10–15 Hz) to the solution, which is most often absent in seismic data. As such data, a low-frequency acoustic impedance model constructed from well data is usually used. The number of wells that are within the 3D survey, the uniformity of their location over the area, and the quality of the recorded or restored logs play an important role in the construction of the low-frequency model. The creation of a low-frequency trend model is most often based on interpolating the logging data into a thick-layer model, considering the behavior of the main reflectors. To build a background low-frequency model, wells are selected with the necessary initial SN and GGR information in the target interval and with a relatively high correlation coefficient in the process of performing stratigraphic binding.

3.2 Inversion Technologies to Refine the Seismic-Geological Model

61

(c) Acoustic seismic inversion (the inversion of stack seismic traces or normal incidence traces) consists in restoring the vertical distribution of acoustic impedance along the traces of primary reflection longitudinal waves at each point on the surface. At the beginning of the process, a low-frequency trend model, a 3D seismic data cube, and a seismic pulse are applied to the inversion procedure input. Testing of inversion parameters is carried out at well location points. Most deterministic algorithms are based on the principle of minimizing the objective function, which usually includes components that form [45] (1) the difference between observed seismic traces and synthetic traces obtained by convolution of a seismic impulse with reflection coefficients of the refined medium acoustic model and (2) geological and geophysical restrictions on the model of the medium (the requirement of a reflection coefficient sequence, the range of possible acoustic properties, etc.). When selecting the parameters, the correlation coefficient between the original seismic data and synthetic ones, as well as the convergence of acoustic impedance according to logging and inversion data is estimated. One of the criteria for the quality of the performed inversion is the assessment of the convergence of the measured acoustic logging and acoustic impedance curves restored from seismic data at the well location. There are numerous examples [28, 29, 64], where for the terrigenous sediments, a close relationship was established between its porosity and the acoustic impedance, on the basis of which the porosity prediction is performed. In the interval of terrigenous reservoirs, rock-collectors are separated from noncollectors with a significant overlap zone of the acoustic impedance threshold values: the values of acoustic impedance for collectors are up to 11,500 m/sg/cm3, noncollectors— from 9500 m/sg/cm3. Such a rather formal approach allows us to observe the most promising areas in the study area. Borehole data and drilling and testing results allow an independent assessment of the inversion performed. To control the quality of the acoustic inversion, the correctness of the acoustic impedance restoration is determined—the result of the inversion by comparing with the acoustic impedance curve obtained from the logging data performed in the well. The main uncertainties in solving the problem, as a rule, are the strong variability of sediments and the interbedding of high-contrast rocks, poor quality of well data, a relatively small number of wells with sufficient a priori information about impedance characteristics of the subsurface medium. In such a situation, the inversion results are most often considered at a qualitative level.

3.2.2

Synchronous (Elastic) AVO/AVA Inversion

In contrast to acoustic inversion, the synchronous AVO/AVA inversion algorithm uses several angular or offset stack seismic sets and their derivatives to restore the impedance distribution of longitudinal and transverse waves, VP/VS ratio, and density, simultaneously. It uses a set of seismic data stacked in different ranges.

62

3 Methods for Solving Inverse Dynamic Seismic Problems

This approach allows the analysis of data to use several independent measurements (stacks) and to determine (restore) the set of seismic characteristics of the medium by the result. This allows you to go from the acoustic version, corresponding to the normal incidence of reflected waves, to an elastic one, which takes into account the dependence of the reflection coefficient on the approach angle of a direct wave and three independent elastic parameters: the velocity of the longitudinal, transverse waves, and density. For each stack from the input seismic data set, a seismic pulse is estimated using well data, and low-frequency acoustic models are constructed for each elastic parameter. Since synchronous inversion imposes higher requirements than acoustic on the necessary set of well data, one of the conditions for its implementation is the presence in the well data of broadband acoustic logging in addition to acoustic and gamma-gamma ray density logging in a significant depth interval covering not only the target interval. The main advantage of AVO attributes is that they are calculated by seismogram, not by stacks, and therefore more sensitive to subtle effects due to inhomogeneous media. VP/VS and AI (acoustic impedance) cubes are the most informative. The analysis of the inversion results is carried out using the VP/VS -AI cross-plot, on which the distinction between facies of different composition looks most pronounced. From the model well trends, three lithotypes are usually determined, clay, water-saturated sandstone, and hydrocarbon-saturated sandstone, which are decisive in creating probabilistic lithotype cubes. To perform synchronous inversion in the “density-VP/VS ratio” parameterization, a minimum of 5–7 informative partial sums is required. In this case, the seismic data on the area should be presented evenly, and the offset ranges for the first three and last two stacks are revised. Accurate determination of VP/VS for rocks of different lithologies is important, since it is the deviations of experimental seismic data from lithological, “background” ratios that can help prediction [32, 45, 54]. For most sedimentary rocks, the VP/VS ratio is between 1.6 and 2.5. It takes into account that the velocities VP and VS in rocks and their ratio VP/VS depend on lithology, porosity, thermodynamic conditions (pressure, temperature), and hydrocarbon saturation. It is worth noting that the ratio VP/VS increases/decreases in accordance with the increase/ decrease in Poisson’s ratio. At high VP values, rocks of different lithologies differ better in VP/VS ratio. At high velocity, the difference in VP/VS between gas- and water-saturated rocks is relatively small. For rocks with a low velocity, the lithological resolution is also not large; however, the difference between gas- and watersaturated rocks will be relatively large. As a result, AVO studies will be more stable in media with low-wave velocity [32, 45, 54]. When preparing seismic data for synchronous inversion, normalization and alignment procedures are used. These procedures are necessary to eliminate the effect of stretching a seismic pulse at large offsets, not related to geology. The quality control of synchronous inversion is performed in various ways: in particular, by comparing the pseudo-elastic parameters obtained at the well location points filtered in the seismic frequency range; by analysis of the obtained sections of elastic properties; by comparing the real seismic field and reconstructed as a result of

3.2 Inversion Technologies to Refine the Seismic-Geological Model

63

inversion; by analyzing their differences; and by analyzing automatically calculated quality maps. The results of synchronous inversion in the form of independent elastic parameter cubes make it possible to perform a more accurate, compared to acoustic inversion, quantitative prediction of FCP and a qualitative prediction of lithotype distribution, determined from well data (e.g., the reservoir lithotype). The thickness of promising objects distinguished by seismic data depends largely on the quality of these data and the properties of the reservoir and enclosing rocks. The objective complexity here is the determination of the interval velocity in the reservoir for translating time thicknesses into deep ones. The geostatistical inversion technology allows to increase the reliability of the FCP prediction of the target formation, primarily effective thicknesses.

3.2.3

Geostatistical Inversion Technology

Geostatistical inversion, as is known, is the most “hard” type of seismic trends applied when using stochastic algorithms in geological modeling [29, 36, 38, 39, 62, 64, 65]. At the same time, geostatistical modeling is based on well data and a priori ideas about the sedimentation model of the reservoir. To describe the volumetric distribution of productive formation reservoir properties, synchronous seismic inversion allows to combine seismic and well data, but the vertical resolution of the result obtained here is limited to the seismic frequency band, while the volume model of the reservoir should ideally have well data resolution. The geostatistical inversion algorithm allows you to get as close as possible to solving this problem. For this, the geological-statistical model is built on a stratigraphic grid using geostatistical modeling methods, and seismic data are some additional source of information. The method of geostatistical inversion (GI), proposed by A. Haas and O. Durbul [29, 36, 38, 39, 62, 64, 65], is based on the calculation for each implementation of the wave field corresponding to the geological model obtained in the process of statistical modeling. In this case, only those facies (lithotypes) are restored; information about which is contained in the well data; and they can be described in terms of the probability density function (PDF), transforming them into a normal Gaussian distribution, usually using a power transformation. Describing all sources of information about the reservoir as a probability density function (PDF), using the algorithm for modeling and selecting solution versions (Markov-Monte Carlo chains), we obtain many models of properties (elastic and discrete) that are consistent with seismic data [64]. It should be marked that geostatistical inversion algorithms operate on a stratigraphic grid and the calculation of elastic parameter cubes (VP, VS, and density) and petrophysical (lithotypes) reservoir properties is carried out simultaneously [59, 60]. At the same time, the preparation of data for GI of partialmultiple stacks includes the choice of cell size; the creation of a frame stratigraphic model; description of statistical models (PDF) of discrete properties (lithotypes) in

64

3 Methods for Solving Inverse Dynamic Seismic Problems

the space of continuous properties (elastic parameters); and definition of vertical and horizontal variograms for discrete and continuous properties. Preliminary comparison of the model wave field with the observed one allows rejection of realizations that do not satisfy the values of the residuals. It should be mentioned that there are certain differences between the deterministic and geostatistical inversions [59]. These differences manifest themselves in the creation of an a priori geoacoustic model. For the first inversion, this is only a low-frequency model of acoustic properties. For the second approach, it is necessary to use different information about the structure of the reservoir, which is formalized in terms of the probability distribution, in particular, such as spatial consistency of properties in the form of 3D variograms (equivalent to spectral density); about the range of parameter values; and the extent to which they vary relative to other properties—multidimensional probability distribution functions characterizing different rock lithotypes. Practically, a GI produces a significant number of realizations in the form of acoustic impedance cubes, for example, 100 [36]. Processing all these implementations is a serious task. For such huge amounts of information, an approach can be applied, consisting in calculating at each point the mean value and standard deviation of the realizations. However, the GI algorithm in each grid cell is supposed to count the number of realizations in which the impedance value is above a certain threshold. The resulting number is then converted into a probability—this can provide useful information in cases where high or low impedance values can directly indicate the presence or absence of a collector. Then, various approaches proposed by various authors (developers) can be implemented, based, in particular, on more general nonstationary fractal models; decomposition of the covariance matrices inversion; evaluation functions using the normalized sum of squares between the synthetic and calibrated real seismic traces; an analytical approach to solving the problem, which formalizes the problem of geostatistical inversion in the framework of Bayesian and Gaussian representations; etc. [36]. To this we can add that in the process of gradual complication of inversion transformations from acoustic inversion of a full-fold cube to geostatistical algorithms, one should take care of the ever-increasing demand for the quality and completeness of the input seismic, logging, and geological data [64]. In the transition to geostatistical inversion, one should have a formed understanding of the reservoir geological structure under investigation. As noted in [43], a priori geological information about the reservoir (lithological regularities, levels of hydrocarbon contacts, etc.) can be taken into account in the solution in the form of trends. In this case, the probability density functions PDF, describing the available information about the reservoir, are combined using the Bayesian strategy so that each simulated parameter obtains a posteriori distribution consistent with a priori one. As one of the examples of inversion transforms in Fig. 3.1, a sequential set of seismic sections in different wave and inversion representations, formed from 3D cubes, initial and transformed due to different inversions [62], are presented. The results of inversions are a set of equiprobable realizations of the distribution of lithotypes and FCP, obtained from acoustic impedance based on the relations

Fig. 3.1 An example of a seismic section in different wave representations: (a) section of wave amplitudes, (b) P-impedance as a result of acoustic inversion, (c) P-impedance as a result of synchronized inversion, (d) P-impedance, and (e) collector-non-collector lithotype distribution using results of synchronous inversion (brown, collector (reservoir); blue, non-collector (non-reservoir)) (according to Ref. [62])

3.2 Inversion Technologies to Refine the Seismic-Geological Model 65

66

3 Methods for Solving Inverse Dynamic Seismic Problems

established at the petro-elastic modeling stage, which are consistent with both well survey and seismic data. In another example, the unstacked seismic records were previously divided into seven angular sums, each of which was related to reflections of the 7-degree sector of dip angles, taking into account the data of acoustic and density logging [29]. The performed three-parameter AVO inversion gave an estimate of the reflection coefficients of longitudinal and transverse waves and the density contrast (Fig. 3.2). The

Fig. 3.2 Results of three-parameter AVO inversion: (a) P wave reflection coefficient, (b) P wave acoustic impedance, (c) the product of rock density on shear modulus, (d) S wave reflection coefficient, (e) S wave acoustic impedance, (f) VP/VS ratio, (g) density contrast, (h) density, (i) λ/ μ ratio (according to [29])

3.2 Inversion Technologies to Refine the Seismic-Geological Model

67

volume distributions of these data underwent inversion with respect to the longitudinal and transverse wave impedance and density, for which the volume distributions μρ, VP/VS, and λ/μ were determined. Schlumberger carried out the inversion of multicomponent seismic data from the gas-condensate field in the North Sea [29]. Its main goal was to determine the elastic properties (impedance for longitudinal waves, VP/VS, and density) from data sets for use as input parameters in the calculations of geomechanical properties at the field. Knowledge of these properties could help build a 3D model of the mechanical properties of the geological medium (mechanical earth model, abbreviated MEM). To assess the significance of the PS data, a comparison was made between the results of the simultaneous inversion of the PZ data and the combined PZ and PS data (Fig. 3.3). Acoustic impedance and density, determined from the amplitudes of the PZ and PS reflections, are characterized by better resolution and better match the logging values compared to the values calculated only from the PZ inputs. A comparison was also made of the acoustic impedances represented by inversion with their values measured in wells whose data were not used to calibrate the inversion. The log acoustic impedances of ten layers of MEM at the same time almost completely coincided with the impedances after the seismic data inversion [29]. Correlation with models built using traditional methods for obtaining geomechanical properties, i.e., not based on their calculation from seismic data, was characterized in several layers by large errors. It is worth noting that since the geostatistical inversion widely uses statistical methods for estimating source, intermediate data, and processing results, the GI has the same drawbacks as for statistical methods, especially in that part where properties of thin-layer objects (layers) are predicted. Among them, some distance from physical conditioning (the nature of the wave propagation process in inhomogeneous media), fuzzy validity of the characteristic relationships with the wave fields, in conditions of nonuniqueness, solutions are generated and selected randomly (you can skip the optimal variant); sometimes the results do not correspond to the theoretical principles of information conversion, for example, the resulting detail may exceed the discretization step of the original field seismic survey outside the wells.

3.2.4

Neural Networks in the Dynamic Interpretation of Seismic Data

Algorithms based on the use of the neural network method, which work without the classical seismic record inversion procedure, are called genetic inversion algorithms [47, 51, 52, 54, 58, 66]. It does not require knowledge of the seismic pulse due to the fact that the inversion operator is nonlinear and cannot be represented explicitly. These algorithms differ from traditional methods of solving inverse problems, including acoustic, synchronous elastic, and geostatistical inversions. The nonlinear

68

3 Methods for Solving Inverse Dynamic Seismic Problems

Fig. 3.3 Single- and multicomponent seismic data inversion comparison (inversion of P wave Z-components (a) and PS exchange waves (b)) (according to [29])

3.2 Inversion Technologies to Refine the Seismic-Geological Model

69

genetic inversion (NGI), implemented in the software package Petrel (Schlumberger), is conditionally related to deterministic inversion [51]. The NGI algorithm is based on the principle of neural network learning on acoustic data from well and a seismic cube data. Based on the found dependencies, the cube of the acoustic property is calculated, which is used in network training. This can be either impedance or other properties that are related to the acoustic characteristics of rocks. The minimum and maximum sizes of elementary offset data in the calculation of attributes determine the minimum and maximum frequencies involved in the inversion [54]. As noted by the authors of [52], the main idea of multi-trace genetic inversion is to perform seismic inversion without using a priori information in the first stage, and then in the second one, the reservoir modeling itself is already performed, using the results of the seismic inversion as a volume trend. This allows you to determine where the details of the effective model are obtained objectively from instrumental observations and where from a priori information used in the simulation. Moreover, it is emphasized that the acoustic impedance resulting from seismic inversion is the most informative feature tying seismic and borehole data. And the connection of impedance with the most important petrophysical characteristics, such as density and porosity, allows it to be used as a trend in modeling reservoir characteristics. Unlike standard neural networks, which use the algorithm of the error back propagation as the learning algorithm, in the genetic method under consideration, a slightly different learning algorithm is used. Here the gradient descent technique is used to modify the weights, which is reduced to the numerical optimization problem. Since the surface of the error function of network objective function has a complex structure, the solution can fall into the local minimum of this function when there is a global minimum. This way of training does not allow to get out of the local minimum. The genetic learning algorithm allows the neural network to detect the global minimum of the error function and thereby find the optimal solution [52]. A certain problem of neural network algorithms is the effect of retraining, when the algorithm works well with examples from the training set, but rather poorly with examples that did not participate in the training. To overcome this problem in genetic inversion, the operator is stabilized with the help of a smoothing functional that does not allow the operator to use the uncorrelated part of the seismic signal. In addition, the use of a multi-trace operator allows one to take into account changes in the wave field between the traces, which also stabilizes the solution. The quality control of genetic inversion is determined by the reproducibility of well data based on the use of seismic data, and for wells that did not participate in the training, the integral accuracy of the inversion is determined. Another estimate is the comparison of the measured seismic wave field with the results of solving a direct problem by the result of the inversion. Neural networks, as is known, are nonlinear and provide great opportunities for complicating the dependency model (increasing the number of input attributes, the number of neurons) [45]. In this regard, there is a danger of overcomplicating the model, accompanied by a loss of prediction quality at a distance from the well points.

70

3 Methods for Solving Inverse Dynamic Seismic Problems

In this case, careful monitoring of the prediction reliability in the interwell space is necessary. This control is carried out using cross-validation methods. The most widely used are neural networks such as multilayer perceptron and networks of radial basis functions. On their basis, it is possible to recalculate with the use of a constructed neural network of seismic attribute cubes into a cube of predicted values of counting parameters (KP, Hef, etc.). The disadvantages of neural network algorithms are [37, 45] (1) the uncertainty in the choice of the neural network type (topology) to solve a specific problem (the number of hidden layers, the number of neurons in hidden layers, the type of activation function); (2) the difficulty of obtaining accuracy estimates of the approximation constructed by the neural network; and (3) the possible instability of the prediction, expressed in large variations of the approximating function values when changing the parameters of training and the type (topology) of the neural network at points that are not included in the training and control set. In this case, the forecast for the 2D profile system is carried out using neural networks of the multilayer perceptron type to the results of elastic inversion and seismic attributes. Here, a set of inversion results and seismic attributes are pre-divided into seismically homogeneous areas using Kohonen’s neurocomputer classification [45]. When solving classification problems in Kohonen networks, the so-called access threshold is used. Due to the fact that in such a network, the activation level of a neuron is the distance from it to the input example, the access threshold plays the role of the maximum distance at which recognition takes place. If the activation level of the winning neuron exceeds this threshold value, then the network is considered to have not made any decision. Therefore, when all neurons are marked, and the thresholds are set at the right level, the Kohonen network can serve as a detector of new phenomena. When training, neural networks can tune to the uncorrelated, high-frequency part of the seismic signal [51]. This may result in a good correlation of acoustic impedance, calculated from seismic data with measurements in wells involved in the training. It is impossible to use such a neural network for prediction, since it is based on the uncorrelated part of the seismic measurements. In the genetic inversion to overcome this problem, the operator is stabilized using standard methods for solving non-correct Tikhonov problems [13]. As noted in [51], the deterministic inversion process in general form can be reduced to the construction of a linear operator using a single seismic trace. Genetic inversion has a much greater degree of freedom to find the optimal solution due to the nonlinearity of the operator and the use of several traces to construct the operator. At the same time, genetic inversion is not tied to either the impulse form, or to the a priori structural model. It is also worth noting that the rapidly developing applied field of mathematics, specializing in the creation of artificial neural networks, of which many types in the well-known package Hampson-Russell, implemented the following modules [67]: a multilayered neural network; neural network with a probabilistic approach (PNN); discriminant analysis; and radial functional analysis. The most widely used of these is the neural network with a probabilistic approach (PNN), as it gives the most optimal and stable results compared to other types of neural networks.

3.2 Inversion Technologies to Refine the Seismic-Geological Model

71

The PNN network is a parallel implementation of well-known statistical methods, when samples are classified based on estimates of their proximity to adjacent samples. The distance to neighboring samples is an important factor when classifying a new sample, but the distribution of neighboring samples is also important. The PNN classification is based on the use of Bayesian statistics methods. The idea is that for each sample you can make a decision based on the choice of the most likely class of those to which this sample could belong. Such a solution requires an estimate of the probability density function for each class, which is obtained by considering the training data. The formal rule is that the class with the most dense distribution in the area of an unknown instance will have an advantage over other classes. Another approach to estimating the probability density used in the PNN network is based on nuclear estimates [67–69]. The reasoning here is constructed as follows: the fact that the observation is located at a given point in space indicates that there is a certain probability density at this point. Near observation, there is more confidence in the level of density, and as you move away from it, trust decreases and tends to zero. In the nuclear estimation method, a certain simple function is placed at the point corresponding to each observation; then all of them are added together, and the result is an estimate for the total probability density. Often bell-like Gaussian functions are used as nuclear functions. If the training sample is large enough, then this method gives a fairly good approximation to the true probability density [67–70]. In one of the papers [34], the issues of minimizing nonsmooth functions using the genetic algorithm and the Direct Search Toolbox are discussed. In particular, examples are given on the application of optimization methods from the Optimization Toolbox; the variants are given for applying a genetic algorithm as a method for solving optimization problems based on the use of natural selection by analogy with the processes occurring in biological evolution. The following applications are considered: (a) a genetic algorithm; (b) a genetic algorithm in combination with an algorithm based on the analysis of derivatives according to the Optimization Toolbox; and (c) a pattern search algorithm. In the genetic algorithm, there is a permanent modification of the initial solutions family. At each step, the method of random sampling selects some individualized object from the current generation, called the parent, and is used further to obtain the next generation, called the child. By successive creation of generations, there is an “evolution” of progress toward some optimal solution. The genetic algorithm does not use any kinds of derivatives in order to determine the trajectory of the fastest descent, which represents certain advantages for tasks with a non-differentiable function. Since the search is performed using a genetic algorithm, this is often useful when finding a global minimum. From the point of view of artificial information processing systems, genetic search is a specific method for finding a solution to an optimization problem. Such an iterative search is adaptable to the features of the objective function: the chains that are born in the process of crossing test increasingly wide areas of feature space and are mainly located in the optimum area. Relatively rare mutations prevent the

72

3 Methods for Solving Inverse Dynamic Seismic Problems

degeneration of the gene pool, which is equivalent to a rare, but not ceasing search for optimum in all other areas of the feature space. Obviously, in the genetic inversion methods, where statistical approaches predominate and the less justified physical nature of measurements is used, their inherent shortcomings also remain.

3.2.5

The Classification of Seismic Facies is One of the Important Seismic Data Interpretation Directions

An important direction of seismic prospecting is the establishment of a change in the lithofacial setting of sedimentation (when seismic sections do not reflect changes in either morphological or textural features). As an alternative to the abovementioned inversions, a system similar to the Stratimagic software package (a Paradigm product) [45, 71] can be used to identify seismic facies. At the same time, the classification of reflections along the horizon by type seismic correlations associated with the types of waveforms, which is performed in detail across all cube traces, can be interpreted as changes in lithology. Each trace model identified as a model of a sandy layer is compared with the analyzed reflections along the layer, and in the case of reaching the maximum correlation, the color indicates the belonging of the current trace to this class of facies. As a result, a map of seismic facies is formed—seismic fades map. Such changes may characterize the lithology, porosity, or thickness of the reservoir, but for this purpose, it is required to perform the previously described petrophysical signal calibration for the wells. The analysis of the reservoir properties assumes its visualization in the form of cube sections (traditional method) or in bulk form when the interval of the reservoir extracted from the cube is represented as color-coded values of the reservoir properties (impedance, velocity, porosity, etc.) and the geometry of the reservoir surface is simultaneously shown. Under the conditions of a small number of wells in the area, one of the most popular programs in our country and abroad is the Stratimagic software package from Paradigm. In this complex, the most effective is the classification of trace sections according to their form using three main methods: self-organizing neural networks (NNT), hybrid (Hybrid), and hierarchical with a randomly selected sample of observations. The results obtained in this case are a sequence of model facies traces that are color-graded and reflect the heterogeneity of seismic data. Based on this data, seismic facies maps are generated. Summarizing the above about the features and results of the dynamic interpretation of seismic observations based on the use of inversion transformation methodologies, the following should be noted: 1. The reliability of the obtained results of wave field inversions is largely determined by the quality and completeness of the original borehole and a priori

3.2 Inversion Technologies to Refine the Seismic-Geological Model

2.

3.

4.

5.

6.

7.

73

geological materials and the preparedness of the studied productive and promising objects in the wave field to such transformations. The sequential complication of the transformations performed from the simplest acoustic inversion and ending with more complex geostatistical inversion can be applied in well-studied seismic and geological conditions with the necessary minimum of these well logging methods. The results comparison of various methods of seismic data inversion transformations shows that in the end it is possible to obtain significantly different results of predicting promising objects, indicating quite different possibilities of such transformations. The lack of a unified approach to comparative expert estimates of the information content for these inversion methods of each single and in the common and unified analysis does not allow to finally solve the issue with the ambiguity and reliability of the results. Such a feature of their use is inherent, as is well known, to inverse dynamic seismic problems. Acoustic deterministic inversion is the simplest and most quickly implemented of the entire set of inversion technologies. It allows you to significantly clarify the stratigraphic model, and, in favorable cases, to perform a rapid analysis of areas with improved reservoir properties of terrigenous reservoirs, but with a high degree of ambiguity in the allocation of reservoirs in the space of one elastic parameter—acoustic impedance. A quantitative prediction of the FCP is usually significantly difficult. Synchronous deterministic inversion makes it possible to obtain cubes of three independent elastic parameters and more confidently predict the presence of reservoirs. The vertical resolution of elastic parameter cubes is limited to the frequency range of seismic data. In favorable conditions, a quantitative prediction of the FCP is possible, and the prediction error is estimated only using well data. When predicting areas of improved reservoir properties, in some cases synchronous geostatistical inversion is the most effective, allowing one to go beyond the registered frequency range of seismic data and obtain detailed high-resolution models as close as possible to the detail of GBS data. However, such an inversion is the most demanding both in terms of the quantity and quality of the input data; its execution requires considerable computational and time resources. The set of equally probable realizations of geostatistical inversion allows estimating the uncertainty of the prediction. Using the estimated uncertainty of predicting the effective thickness and volume of reservoirs for a variety of geostatistical inversion realizations makes it possible to perform a probabilistic assessment of hydrocarbon reserves and resources by quantile: P10, P50, and P90 (probabilities of the reservoirs presence in the studied interval are more than 10, 50, and 90%). However, even in this case, it is impossible to do without a previously formed idea of the geological structure of the reservoir under study (i.e., the result of the inverse problem here is not free from the ambiguity of the solution). In addition, as an approach based on the evaluation of statistical characteristics, the method has a drawback regarding the construction of a physically based search for a detailed model (in practice, examples are given of

74

3 Methods for Solving Inverse Dynamic Seismic Problems

obtaining models with a resolution that exceeds the sampling step of the original seismic survey). 8. Despite the obvious usefulness and widespread use of neural network technologies as possible expert systems, any of them are not perfect and have several disadvantages. One of the main drawbacks of such systems is the difficulty of recognizing the limits of their capabilities and demonstrating the unreliable functioning near the limits of applicability. It should be noted that the design of the parameters of neural network technologies causes certain difficulties and limitations. They are poorly adapted to learning at the level of new concepts and new rules, are not effective, and of little use in those cases when it is necessary to take into account the complexity of real and nonstandard tasks. The main disadvantage of neural networks is also the difficulty in interpreting the results, which leads to a decrease in the value of the results obtained by the network. A trained NN is a “smart black box” whose work is difficult to explain with conventional means. If we talk about specific NN models, then the disadvantage of, for example, multilayer neural networks is the inability to guarantee the best training for a specific time interval. It is also difficult to implement a reasonable choice of network parameters, the number of hidden layers and the number of neurons in these layers, and the selection of weights. Given the above disadvantages, it can be assumed that the sharing of data from different technologies will, in principle, provide a hybrid model. Since each of the technologies is able to more or less indicate the confidence level of each decision, the solution can be transferred to the expert system to make possible concrete and logical selection. But they may also need to gather additional facts to get a final conclusion. In this case, you can add other methods and algorithms that are associated with the definition of informative features. In other words, you can build a combination of systems that would be more powerful than each of the systems separately, i.e., apply a system approach with respect to inversion methods.

3.3

Hydrocarbons Indication in the Dynamic Analysis of Seismic Data

Chapter 1 provides information on the justification of the possibilities for isolating the visual effects of oil and gas deposits in the observed geophysical fields. We are talking about factors that influence the change in the entire spectrum of the rocks’ physical properties, which are reflected in the geophysical fields in the form of quite significant anomalies. These anomalies, the dynamic expressiveness of which often turns out to be insignificant, in practical situations are always complicated by anomalies of a different geological nature, the intensity many times higher than the first. In relation to seismic anomalies associated with hydrocarbon deposits, it should be marked that such complications significantly hamper the selection of a useful but

3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data

75

most often weak intensity signal, determined by the characteristic contribution from the hydrocarbon reservoir (in the form of appropriate geological indicators). This feature is discussed in Sect. 2.2 when quantifying the contribution to the seismic wave field parameters of the geological section. In fairly complex geological and geophysical conditions for the selection of target but rather weak seismic responses from the seismic record dynamics (one way or another associated with oil and gas saturation), the results for the selection of the desired useful anomalies directly related to hydrocarbon deposits, in most cases, are ambiguous. Such features of the dynamics and conditions of signal extraction are considered in a number of well-known monographs [72–75]. In this sense, the hydrocarbon indication topic based on the use of seismic dynamic parameters remains relevant to the present, but at the same time, it has not been fully studied, and its wide application is not noted in practice. This topic also turns out to be very extensive, and the authors do not set themselves the task of examining all its aspects and features. In this case, the authors considered it expedient to dwell on the features of this topic, which are touched upon in a number of works [72, 76–81]. This refers to some features of the use of seismic recording dynamics in estimating and predicting oil and gas saturation based on the use of well-known relationships between the velocity of elastic seismic waves and porosity, focusing mainly on the materials and results presented in these works. The problem of direct predicting of oil and gas potential itself, as is well known, remains rather complicated, despite the large amount of versatile scientific and methodological developments in this field [73–75]. Note that the saturation of reservoir rocks with hydrocarbons in structural and lithological traps changes the elastic, density parameters, and absorbing properties of a real medium. At the same time, the presence of hydrocarbons in the oil and gas traps (as compared with the reservoir rocks formation water saturation) leads to changes in the wave velocity and density of rocks. Due to this effect, acoustic impedance (the product of velocity and density) decreases. This, in turn, leads to a noticeable change in the distribution of reflection coefficients in a real subsurface environment. On the sections of the wave fields, such changes in the physical properties of rocks of the studied objects appear as changes in the amplitudes, phases, frequency composition, and other parameters of the seismic record. If a phase transition in the form of water-oil, gas-oil, or gas-water contacts is formed in a perspective object, then additional horizontal or subhorizontal reflections may appear on the wave fields due to changes in acoustic properties at the phase transition interface. The identification of such features on seismic sections and their mapping by area provides a qualitative basis for the indication of hydrocarbons in promising traps. The extraction of quantitative characteristics from the peculiarities of such effects is the process of predicting and evaluating the oil prospects of such objects. Characteristics such as lithology type, porosity, degree, nature, etc. of fluid saturation are commonly used to describe the objects under study. Thus, the possibility of using seismic data dynamics to directly search for oil and gas using the average time and wave field amplitudes was considered in [76]. The method consists in the direct use of the average time equation, which describes the dependence of the

76

3 Methods for Solving Inverse Dynamic Seismic Problems

elastic wave velocity in sedimentary rocks on porosity, wave velocities in the fluid and solid phase, and amplitudes of reflected waves on the reservoir. The formulation of the prediction problem is set as follows. These are the input data for solving the problem: (1) the wave velocities in the solid phase (sandstone, clay) and in the fluid (gas, oil, and formation water) are considered known; (2) within the studied object, the parameters of the solid phase practically do not change (or change only slightly within the error limits); (3) the porosity of the reservoir rocks is constant (or varies only slightly within the error range); (4) formation rock densities are related to porosity or velocity by known ratios; and (5) the amplitudes of the seismic record are proportional to the values of the reflection coefficients. It is necessary to determine the type of fluid. The nature of the fluid can be associated with the ratios of reflection coefficients, determining the type of fluid and the volume of the reservoir.

3.3.1

Velocity Determination

First, the section is interpreted: the top and bottom reflectors and tectonic faults of the prospective formation are correlated (Fig. 3.4). The average and interval velocities are determined in various ways, for example, with anticlinal and syncline structures according to the Dix formula, while thinning structures according to the Setlegger method; etc. vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !ffi u u 2 P n n 1 P u V e 2n1  Δt j  V Δt j u en  t j¼1 j¼1 Vn ¼ , Δt n e n is the average velocity up to where Vn is the interval velocity in the n-th layer, V the n-th layer, and Δtj is the travel time in layer j.

Fig. 3.4 Schematic models of oil and gas structures: (a) the section of the anticlinal and synclinal structures, (b) anticlinal structure complicated by faults, (c) wedging structure (according to [76])

3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data

3.3.2

77

Porosity Determination

The well-known average time equation proposed by Willie M. [81] for determining the porosity of rocks by wave velocity or interval time is as follows: 1 φ 1φ þ , ¼ V Vf Vm where φ is porosity, Vf is the velocity in the fluid, Vm is the velocity in the solid phase, V is the interval velocity in the reservoir. (Remarks: It is worth noting here some restrictions concerning the use of the average time equation. It is valid only for depths > 2000 m, since at these depths the velocity variations due to pressure increase are relatively minor. In [79], in particular, it is noted that it is valid for well-cemented rocks saturated with water or oil at depths of 2.5–3.5 km. For non-cemented sedimentary rocks (e.g., for sands), the cubic dependence of velocity on porosity is more valid. There are doubts about the adequacy of its use for rocks with low porosity values. For more information about the features of its use, see [30, 72, 80, 82, 83]. We only note that this empirical model does not take into account the influence of clay content, oil and gas saturation, and the degree of the rock compaction on the dependence of interval time (velocity) on porosity. It is also worth remembering that there are several modifications of the average time formula proposed for clay rocks with different distribution of clay material whose influence is taken into consideration as an additive function [80]. The equation does not take into account the types of porosity in complex reservoirs. With fairly complex types of reservoirs and nontrivial conditions for their occurrence, additional studies are needed to determine the conditions for the applicability of this equation. For example, it may be necessary to take into account friction between the fluid and the matrix, the flow of fluid from one type of pore into another, the influence of the spectral composition of the seismic pulse on the fluid behavior in a complex porous medium, the geological features of the reservoir, etc.) From the average time equation, the porosity can be defined as [76]: φ¼

3.3.3

Vm  V   V  VV mf  1

Terrigenous Rock Density Determination

With a known porosity and density dependence on the porosity, the density can be calculated, for example, you can use an analogue of the “average time equation” for the density [76]: ρ ¼ 1:00  φ þ 2:68  ð1  φÞ

78

3 Methods for Solving Inverse Dynamic Seismic Problems

3.3.4

Reflection Coefficient Ratio Determination

The reflection coefficient for a normal beam is associated with the seismic characteristics of the reservoir according to the well-known formula [30, 82, 84]: R¼

ρ 2 V 2  ρ1 V 1 ρ 2 V 2 þ ρ1 V 1

In this case, the ratio of reflection coefficients (RB/RA) at different points of the reservoir (at points A and B) laterally is determined as the ratio of the amplitudes of the seismic wave field at these points (AmpB/AmpA) (Fig. 3.4). According to the formulation of the problem noted in paragraph 5, we have: RB/RA ¼ AmpB/AmpA. In the case of a thin-layer section, the thin layer modeling is used.

3.3.5

Fluid Nature Determination

Acoustic impedance AI of the reservoir as the product of rock density and wave velocity in the reservoir is determined by the ratio of the reflection coefficients at point A according to the formula [76]: AI ¼ ρ2 V 2 ¼

ρ1 V 1 ðRВ =RА þ RВ Þ ðRВ =RА  RB Þ

Fluid density can be calculated and set the type of fluid by the formula: ρ2f ¼ ρf φ2 + 2.68(1  φ2) Using the average time expression, find the fluid velocity at point X: V fx ¼

V 2  V m2  φ2 V m2  V 2  ð1  φ2 Þ

The found Vfx values are compared with the known ones and the fluid nature is evaluated. Figure 3.5 shows the curves of acoustic measurements and the P wave travel time and the results of the interpretation of logging curves in the depth interval of 1340–1400 m. In Fig. 3.6 the results of the fluid type prediction on the studied area are presented. On the amplitude map in the area of the deposit, a decrease in amplitudes is observed, which is associated with a decrease in the reflection coefficient from the top of the reservoir (Fig. 3.6a). The map of the velocity distribution in the fluid also

3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data

79

Fig. 3.5 Diagram of continuous acoustic logging (according to [76])

Fig. 3.6 Maps of contours: (a) reflection amplitude along top of the layer III, (b) the velocity in the fluid (marked the boundary waters) (according to [76])

highlights the reservoir along the OWC contour within the contour of 1341 m/s (Fig. 3.6b). The method can be used in various seismic and geological conditions: with a complex configuration of promising objects, inclined OWCs, complex formation faults, and facies changes in the sediments under study. The necessary requirements for the source data are high-quality field survey, processing with maximum resolution and preserving the true amplitude ratios (in particular, considering the dependence of the wave amplitudes on the morphology of the reservoir surface), and high-quality a priori information on well data. When performing seismic inversion, as the source data, the results of seismic survey processing in the form of wave field sections, acoustic and density measurements in wells in the form of velocity and density distributions are used. At well

80

3 Methods for Solving Inverse Dynamic Seismic Problems

points, seismic data is transformed to well data, considering the well data to be more informative, which is not always true in the general case. However, as shown in [77], this kind of approach to a priori information (regarding absolute confidence in SN data) even with qualitative logging can lead to significant errors, since ultrasonic (US) measurements and seismic surveys in wells (VSP) differ both in the parameters of the waves used and in the technology and conditions of the measurements. With US and VSP measurements, the waves used cover a different volume of rocks, therefore, represent a different amount and quality of information and do not always provide the expected complementarity. Ultrasonic measurements are known to be influenced by many factors—the presence of a casing string, a cement ring, a mudcake, cavities, disturbed zones, mud penetration zones, etc. The high-frequency composition of the waves and the small size of the downhole probe limit the possibility of studying the more distant parts of the prospective objects from the well. In this sense, the VSP survey can provide more reliable results. The borehole seismic survey, in addition, allows performing detailed parametrization of the section (stratification of the section by velocities and densities; determination of the boundary occurrence elements, fracturing, types of faults, etc.). Impulse characteristics from the VSP data allow us to describe geological objects and classify them. However, the VSP data has its drawbacks (waves to the wellbore can be approached from different directions, which determines the limited spatial observations), from which measurements to a certain extent are free using landbased seismic surveys. Based on the above, it should be noted that the weight of the latter should be higher when solving inverse problems based on the combination of well sonic (SN) and seismic data. In case of doubtful SN data, they should not be used, since these data will significantly limit the possibility of extracting information from seismic data, thereby distorting the structure of the wave field in the process of matching with logging data. As one of the examples in [77], the results of using reflection coefficients for the hydrocarbon indication in the Triassic deposits of the Gevet gas field (North Sea) are given. Velocities in gas- and water-saturated sandstones were calculated using the Willie average time equation. Moreover, to determine the velocity of gas saturated sandstones Bunter (B), we have: k p  ð1  k w Þ 1  k p 1 k ¼ kp  w þ þ , V Bg Vw V Bm Vg where kp is the reservoir porosity, kw is the reservoir water saturation, VBg is the velocity in gas-saturated sandstones, VBw is the velocity in water-saturated sandstones, VBm is the velocity in the solid phase, Vw is the velocity in formation water, and Vg is the velocity in gas.

3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data

81

The relevant parameters for the reservoir and the overburden are defined as follows: For the water-saturated sandstones Bunther: kp 1  kp 1 ¼ þ : V Bw V w V Bm Density of gas sandstones Bunther:  ρBg ¼ kp  kw  ρw þ kp  ð1  k w Þ  ρg þ 1  kp  ρBm , where ρBg is the density in gas-saturated sandstones, ρBw is the density in watersaturated sandstones, ρBm is the density in the matrix, ρw is the density in the formation water, and ρg is the density in the gas. Density of water-saturated sandstones Bunther:  ρBw ¼ kw  ρw þ 1  k p  ρBm : The reflectivity from the top of the gas reservoir: RBg ¼

ρBg  V Bg  ρcl  V cl , ρBg  V Bg þ ρcl  V cl

where ρcl is the density of the overlying shales and Vcl is the velocity of the overlying shales. The reflectivity of the gas-water contact (GWC): RBgw ¼

ρBw  V Bw  ρBg  V Bg : ρBw  V Bw þ ρBg  V Bg

The reflectivity from top of the deposits in the water-saturated part: RBw ¼

ρBw  V Bw  ρcl  V cl : ρBw  V Bw þ ρcl  V cl

The calculated reflectivity makes it possible to explain the manifested effects on the wave field section (Fig. 3.7), namely, to associate the horizontal axis of synphase with the boundary of the GWC; negative reflectivity as an indicator of gas presence; a slight jump in acoustic impedance on the top of the water-saturated part of the reservoir causes the appearance of a dark spot effect; and with equal and opposite reflection coefficients, the top of the gas deposit and the boundary of the GWC can form a bright spot on the wave field section.

82

3 Methods for Solving Inverse Dynamic Seismic Problems

Fig. 3.7 Seismic section as result of research at the Gevet oilfield (1974) (according to “Phillips petroleum exploration” [77])

Fig. 3.8 The relationship between the reflection and transmission coefficients of longitudinal and transverse waves which were determined according to the system of Zoeppritz’s equations in matrix form for the model of two half-spaces (according to [85])

In the general case, the reflection and transmission coefficients of seismic waves for acoustically heterogeneous media, in addition to the elastic properties of the medium, depend on the incidence angle θ of the longitudinal waves on the interface between the media. This dependence, as is known, is described by nonlinear Zoeppritz’s formula [30, 84, 86, 87]. Accounting for this additional parameter (θ) makes it possible to rearrange seismic data in such a way that the set of seismic traces will represent the function of the incidence angle. In this case, the amplitudes of the reflections will depend on the approach angle of the wave, and, therefore, it becomes possible to use the Zoeppritz’s equations to solve the inverse problem—finding the coefficients of these equations, represented through elastic characteristics of the medium. The Zoeppritz’s equation system in the matrix form for the model of two halfspaces (Fig. 3.8) is represented as follows:

3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data 2

cos ðθ1 Þ  sin ðδ1 Þ 6 sin ðθ1 Þ cos ðδ1 Þ 6 6 V S1  sin ð2δ1 Þ 6 6 cos ð2δ1 Þ V P1 6 6 4 V P1  cos ð2δ1 Þ sin ð2θ1 Þ V S1

cos ðθ2 Þ  sin ðθ2 Þ ρ2  V P2  cos ð2δ2 Þ ρ1 V P1 ρ2  V P1  V 2S2  sin ð2θ2 Þ ρ1  V P2  V 2S1

sin ðδ2 Þ

83 3

3 2 3 2 7 ARP cos ðθ1 Þ 7 7 6 A 7 6  sin ðθ Þ 7 1 7 7 6 RS 7 6 76 7 ¼6 7, 7 4 ATP 5 4  cos ð2δ1 Þ 5 7 5 ρ2  V P1  V S2  cos ð2δ2 Þ ATS sin ð2θ1 Þ ρ1  V 2S1 cos ðδ2 Þ ρ2  V S2  sin ð2δ2 Þ ρ1 V P1

where VP, VS, and ρ are the velocities of the longitudinal, transverse waves and the density of the media, respectively, and ARP, ARS, ATP, and ATS are the reflection and transmission coefficients of the longitudinal and transverse waves, respectively. Zoeppritz’s formulas are complex, and their various approximations are used for practical application. In practice, binomial approximation is widely used. For more complex environments, three- and more-membered approximations are used. The prediction purpose is to find and interpret the coefficients of the equations that are associated with the distribution of subsurface parameters. Here we also note that for successful application of AVO (amplitude variation with offset) analysis, it is necessary to make an assumption that the amplitudes of the interference seismic wave field (Amp (θ)) are proportional to the values of the reflection coefficients, i.e., R(θ) ~ Amp(θ). With a three-term approximation, the equation (Aki-Richards) after rearrangement of terms (by Shuey R. T., 1985) looks like this [32, 82, 86, 88]: RðθÞ ¼ R0 þ G  sin 2 ðθÞ þ C 

sin 4 ðθÞ , cos 2 ðθÞ



 V2 1 ΔV P Δρ 1 ΔV P ΔV S 1 Δρ ,G ¼  , þ  4  2S  þ  R0 ¼  2 ρ 2 VP 2 ρ VP VS VP 1 ΔV P V þ V P2 C¼  , V P ¼ P1 , ΔV P ¼ V P2  V P1 , 2 VP 2 ρ þ ρ2 V þ V S2 V S ¼ S1 , ΔV S ¼ V S2  V S1 , ρ ¼ 1 , Δρ ¼ ρ2  ρ1 , 2 2 where R0 is the reflection coefficient at θ = 0, depending on the velocity distribution longitudinal waves and medium density; G is the AVO gradient, depending on the distribution of the longitudinal and shear wave velocities and density; and C is the AVO curvature, depending on the velocity distribution of longitudinal waves. In the values of the final reflection coefficient R(θ), each of its components (R0, G, C) characterizes the corresponding contributions of the R0, G, and C for a certain incidence angles range of longitudinal waves. Based on the approximation type and the unknown variable number, an equation system is formed. The solution of this system gives the AVO attributes, which can be given physical and/or geometric meanings:

84

3 Methods for Solving Inverse Dynamic Seismic Problems

R0 is reflectivity at normal incidence (θ ¼ 0). G is the tangent of the slope angle of the linear part of the function R(θ), characterized at angle drops of 0 –30 . С is AVO curvature, determines the behavior of R(θ) at high inclination angles (>30 ). AVP ¼ ΔVP/VP characterizes the ability of the horizon to reflect P waves. AVS ¼ ΔVS/VS characterizes the ability of the horizon to reflect S waves. ν ¼ ΔVP/VP – ΔVS/VS is Poisson’s ratio. ΔF ¼ ΔVP/VP – q(VS/VP)(ΔVS/VS) is the fluid factor. To assess the effect of hydrocarbons on the function R(θ), simulations are performed, specifying the velocity distribution VP, VS, and density ρ for theoretical calculations. When interpreting the AVO analysis results, a graphical representation of the relationship between two AVO processing attributes (in the cross-plot form) is used, for example, to determine reservoir saturation (such as porosity or lithology) and represent the dependence of the attributes G and R0. Trend lines are built on the plane of the cross-plot with constant VP/VS ratios for the G ¼ f(R0) dependence (Fig. 3.9a). The points (R0, G) for different angles θ are plotted on the cross-plot. When changing the type of fluid or lithology, these points deviate from the background trend line in either direction, depending on what reflection is being examined (from the top or the bottom of the reservoir). When the formation water is replaced by hydrocarbons, points on the cross-plot deviate from the trend line (Fig. 3.9b). The deviation of points (R0, G) for the reservoir reflectors with gas is greater than the deviations for the ones with oil. An analysis of the distribution areas of the cross-plot points provides a basis for the fluid type prediction and its classification.

Fig. 3.9 Dependencies between AVO attributes G and R0 for (a) the trend lines with constant VP/VS ratio of dependencies G ¼f (R0) and (b) deviations from the trend line of points (R0, G) for the reservoir with gas and oil

3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data

85

Errors in the application of the described approach to predicting hydrocarbons are due to a number of factors. This is the use of interference amplitudes instead of the reflectivity estimates; for thin-layer objects, it is not always possible to distinguish the top and the bottom of the collector—the lack of reliable data on shear wave velocity; perspective objects are heterogeneous in porosity and lithology; often, it is not possible to correctly construct a background (or lithological) trend: the presence of background random and regular noises (low signal-to-noise ratio) on the seismic gathers and a number of others. S. Chopra, J. Castagna, and Y. Xu [89] give examples of studying oil and gas objects using data after applying wave field inversion procedures. As a result of solving the inverse dynamic problem, estimates of the reflectivities for a thin-layer medium are obtained. The authors of [89] demonstrate the possibility, under favorable seismic and geological conditions, of a more detailed study of the thin-layer medium. The described approach differs from other types of inversions in that geological assumptions are made, rather than formal mathematical ones. It uses the method of spectral decomposition. The authors argue that the inversion in the spectral domain described in the article represents a new way of excluding the wave pulse, probing medium, from the seismic data, and extracting the reflection coefficients on this basis. It is based on the assumption that the distance between the spectral maxima and minima for limited by a window a portion of the seismic trace is a function of the layer thickness in the time domain. In the case when the wave pulse is known in the noise absence in the field of seismic reflections from one layer, it is possible to determine the layer thickness in a unique way without limiting the resolution at a relatively narrow frequency band (when the polarity and magnitude of the reflection coefficients from the top and layer bottom are unknown) provided there is an even component in the response of the reflected wave [83]. Under such circumstances, as soon as the thickness of the layer is found, the reflection coefficients from the top and the bottom can be determined. In practice, the thickness and reflection coefficients can be found as detailed as the magnitude of the noise allows. In the case of thin layer set, the result of the inversion becomes not the only one. Moreover, if we a priori accept the reasonable assumption that reflections do not dominate for all layers, but only for a few (there is no complicated interference pattern), then we can consistently resolve the superposition of sinusoidal responses from the layers in the frequency domain. Thus, the result is much more detailed than the original wave field. An example of the spectral decomposition application under favorable conditions (high signal-to-noise ratio) is shown in Fig. 3.10a. The presence of noise in the source data at the same time worsens the inversion of the reflection coefficients outside the seismic recording band, but the method still steadily restores the medium within the frequency band without a significant increase in noise, as it would have if using the usual deconvolution procedure. This phenomenon is due to the fact that the reflection coefficients (RC) during the inversion are determined in the frequency band of the original interference field, where the signal-to-noise ratio is higher, and outside this frequency band, the RC values are significantly attenuated. The result of

86

3 Methods for Solving Inverse Dynamic Seismic Problems

Fig. 3.10 Example of wave field inversion into reflection coefficients with data of low-level noise within the frequency band: (a) the section of the original wave field, (b) the result of seismic trace inversion in to reflection coefficients (according to [83, 87])

deconvolution is obtained by spectral division, including in the frequency band, in which where the useful signal is weak, the noise increases sharply. But at the same time, the result of the inversion is distinguished by high resolution compared to the original seismic section (Fig. 3.10b) and allows for more confident interpretation of fine details and the internal structure of oil and gas perspective objects. Figure 3.11 shows the result of applying the described inversion approach when identifying a carbonate reef 50 m thick and with a lateral dimension of 600 m, which lies on the carbonate base. In the time section, the prospective object under consideration practically does not differ from the underlying formation (Fig. 3.11a). On a fragment of the relative acoustic impedance (Fig. 3.11b), the gas-saturated reef and the enclosing upper and lower strata are uniquely differentiated. At the same time, the internal structures of the perspective object and adjacent layers are relatively easily and reliably interpreted. In Fig. 3.12 is an example of studying the internal structure of gas-saturated sandstone. The fragment of the reflection coefficients (Fig. 3.12a) shows the internal structure of a complexly constructed reservoir. The collector is represented by a series of two sandstone layers with the correlation of the top and the bottom of each of them. In the first layer of sandstone, gas-oil contact (GOC) is traced; in the second layer of sandstone, a water-oil contact (OWC) is clearly traced.

3.3 Hydrocarbons Indication in the Dynamic Analysis of Seismic Data

87

Fig. 3.11 Result of seismic inversion in the identification of the carbonate reef: (a) fragment of the original time section, (b) section relative acoustic impedance(according to [83, 87])

In Fig. 3.12b, a fragment of the section with the prediction of clay content, obtained from a conventional seismic section, is superimposed on the result of the reflection coefficients (RC) interpretation. In this section, the complex structure of the reservoir is not visible. A reservoir from two layers looks like one object; gas-oil and oil-water contacts do not appear. Thus, the results obtained above using various approaches to the interpretation of seismic data substantiate the presence of a certain parameter contribution of the real medium to the kinematic and dynamic characteristics of the seismic record. However, such approaches have their own peculiarities and drawbacks, which make the results of the prediction ambiguous. One of the main disadvantages of this is the use, as the initial data for solving the inverse problem, of interference wave fields. As shown in Chap. 4, the exclusion from the seismic data of such a distorting factor as wave interference leads to a significant increase in the reliability and detail of the geological parameter prediction when prospecting for oil and gas deposits.

88

3 Methods for Solving Inverse Dynamic Seismic Problems

Fig. 3.12 Example of studying the internal structure of gas-oil-saturated sandstone: (a) section fragment of the reflection coefficients, (b) interpretation of reflection coefficients (RC) superimposed predicted of clayiness (according to [83, 87])

3.4

Summary

The features of the theory and practice of the inverse mathematical problems study, which are laid down and developed in the fundamental works of many domestic and foreign researchers, are discussed. The solution of the inverse dynamic seismic problem in the classical formulation is justified. The conditions for the existence, uniqueness, and stability of such a solution are determined. Iteratively testing the model with different parameters determines the domain of solution existence. If the instability of the solution is detected during the inversion, the algorithm is configured to obtain an approximate solution by regularization. Of the considered seismic inversions, the main ones are acoustic deterministic; synchronous (elastic) AVO/AVA; geostatistical; and nonlinear genetic (NGI), based on the principle of neural network learning related on acoustic data from wells and seismic cube data. The simplest of these is the acoustic inversion, and the most complex is the geostatistical one. As an alternative to the abovementioned

References

89

inversions, a system similar to the Stratimagic software package from Paradigm can be used to detect seismic facies. In this complex, the most effective way is to classify trace intervals by their shape using three main methods: self-organizing neural networks (NNT), hybrid (Hybrid), and hierarchical with a randomly selected sample of observations. The results obtained in this case are a sequence of color-classified model facies traces that reflect the heterogeneity of the seismic data. On the basis of these data, maps of seismic facies are formed. An original method of indicating hydrocarbons is considered based on the use of dynamic parameters of seismic recording using the mean time equation, describing the dependence of the velocity of elastic waves in sedimentary rocks on porosity, on the wave velocities in the fluid and solid phase, and using the amplitudes of reflected waves from the reservoir.

References 1. Lavrent’yev, M. M. (1967). Matematicheskiye zadachi interpretatsii geofizicheskikh nablyudeniy (Mathematical problems of geophysical observations interpretation). In Nekotoryye metody i algoritmy interpretatsii geofizicheskikh dannykh (Some methods and algorithms for the interpretation of geophysical data) (pp. 3–8). M., Nauka. 2. Alekseyev, A. S., & Mikhaylenko, B. G. (2004). Nekotoryye voprosy matematicheskogo modelirovaniya v geofizike (Some questions of mathematical modeling in geophysics). Novosibirsk, Izd. IVMiMG SO RAN. Plenarnyy doklad (Plenary lecture). pp. 20–45. 3. Vakhromeyev, G. S., & Davydenko Yu, A. (1987). Modelirovaniye v razvedochnoy geofizike (Modeling in exploration geophysics) (p. 192). M., Nedra. 4. Verbytskyy, T. Z., Pochynayko, R. S., Starodub Yu, P., & Fedoryshyn, A. S. (1985). Matematycheskoe modelyrovanye v seysmorazvedke (Mathematical modeling in seismic exploration) (p. 276). Kyev, Naukova dumka. 5. Vychislitel’nyye matematika i tekhnika v razvedochnoy geofizike (Computational Mathematics and Technique in Exploration Geophysics). (1990). In: V. I. Dmitriyeva (Ed.), Spravochnik geofizika (Geophysicist handbook) (2nd ed.). M., Nedra, p. 498. 6. Koryagin, V. V., & Sakharov Yu, P. (1988). Matematicheskoye modelirovaniye v seysmorazvedke (Mathematical modeling in seismic exploration) (p. 160). M., Nauka. 7. Oblogina, T. I., & Piyp, V. B. (1966). Issledovaniya kinematicheskikh osobennostey voln v neodnorodnykh sredakh (Investigations of the waves kinematic features in heterogeneous media). Izv. AN SSSR, Fizika Zemli (Physics of the Earth), № 3. 8. Petrashen, G. I. (1978). Osnovy matematicheskoy teorii rasprostraneniya uprugikh voln (Foundations of the mathematical theory of elastic wave propagation). Voprosy dinamicheskoy teorii rasprostraneniya seysmicheskikh voln (Questions of the dynamic theory of seismic wave propagation). Release XVIII, L., Nauka, p. 248. 9. Petrashen, G. I. (1980). Rasprostraneniye voln v anizotropnykh uprugikh sredakh (Wave propagation in anisotropic elastic media). L., Nauka. p. 280. 10. Alekseyev, A. S. (1967). Obratnyye dinamicheskiye zadachi seysmiki (Inverse dynamic seismic problems). Book: Nekotoryye metody i algoritmy interpretatsii geofizicheskikh dannykh (Some methods and algorithms for the interpretation of geophysical data). M., Nauka, pp. 9–84. 11. Alekseyev, A. S., & Lavrent’yev, M. M. (1982). Matematicheskiye modeli v geofizike (Mathematical models in geophysics). In Collection: Aktual’nyye problemy prikladnoy matematiki i matematicheskogo modelirovaniya (Actual problems of applied mathematics and mathematical modeling) (pp. 42–50). Nauka.

90

3 Methods for Solving Inverse Dynamic Seismic Problems

12. Romanov, V. G. (1984). Obratnyye zadachi matematicheskoy fiziki (Inverse problems of mathematical physics). M., Nauka, p. 264. 13. Tikhonov, A. N., & Arsenin Ya, V. (1974). Metody resheniya nekorrektnykh zadach (Methods for solving incorrect problems). M., p. 223. 14. Yagola, A. G. et al. (2014). Obratnyye zadachi i metody ikh resheniya (Inverse problems and methods for their solution). Prilozheniya k geofizike (Applications to geophysics). Second Edition. M., BINOM. Laboratoriya znaniy (Knowledge laboratory). p, 216. 15. Yanovskaya, T. B., & Prokhorova, L. N. (2004). Obratnyye zadachi geofiziki (Inverse problems of geophysics). Uchebnoye posobiye (Tutorial). Izd. S.-Peterburgskogo universiteta. p. 214. 16. Kreyn, M. G. (1954). Ob odnom metode effektivnogo resheniya obratnoy krayevoy zadachi (On a method for the effective solution of an inverse boundary value problem). Dokl. AN SSSR (USSR Academy of Sciences Report), 94(6), 767–770. 17. Gel’fand, I. M., & Levitan, B. M. (1951). Ob opredelenii differentsial’nogo uravneniya po yego spektral’noy funktsii (On the determination of a differential equation from its spectral function). Izv. AN SSSR. Ser. Matem. (Bulletin of the USSR Academy of Sciences, Series of Mathematics), 15(4), 309–360. 18. Marchenko, V. A. (1952). Nekotoryye voprosy lineynykh operatorov vtorogo poryadka (Some questions of the second order linear operators). Trudy Mosk. mat. obshchestva (Proceedings of the Moscow Mathematical Society). T. 1. 19. Babenko, I. A., Fedotov, S. L., Nekrasova, T. V., Yevdokimova, M. L., & Krylova, M. V. (2012). Osobennosti ispol’zovaniya inversionnykh tekhnologiy dlya prognoza kollektorov na shel’fe Okhotskogo moray (Features of the inversion technologies use for predicting reservoirs on the Okhotsk Sea shelf). Nauchno-tekhn. Vestnik OAO «Rosneft’» (Scientific-technical bulletin of OJSC “Rosneft”). Geologiya i geofizika (Geology and Geophysics), 28(3), 12–15. 20. Gadyl’shin, K. G., Cheverda, V. A., & Neklyudov, D. A. (2014). Vliyaniye svobodnoy poverkhnosti na kachestvo resheniya obratnoy dinamicheskoy zadachi seysmiki (The free surface influence on the quality of the inverse dynamic seismics problem solution). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 43–50. 21. Mikhaylenko, B. G., & Fat’yanov, A. G. (2014). Chislenno-analiticheskoye modelirovaniye volnovykh poley dlya sred slozhnogo stroyeniya i struktury (Numerical and analytical modeling of wave fields for media of complex structure and construction). Sibirskiy zhurnal vychislitel’noy matematiki (Siberian Journal of Computational Mathematics), 17(2), 163–176. 22. Romanov, V. G. (1973). Obratnyye zadachi dlya differentsial’nykh uravneniy (Inverse problems for differential equations). Novosibirsk, Russia. 23. Fat’yanov, A. G. (2005). Chislenno-analiticheskoye modelirovaniye volnovykh poley v neodnorodnykh sredakh (Numerical and analytical modeling of wave fields in inhomogeneous media). Avtoref. disser. na soiskaniye uch. stepeni d.f.-m.n. (Ph.D. thesis in Physics and Mathematics). Novosibirsk. p. 33. 24. Cheverda, V. A. (2009). Vosstanovleniye skorostnogo stroyeniya neodnorodnykh sred metodom polnogo obrashcheniya volnovykh seysmicheskikh poley (Reconstruction of the velocity structure of heterogeneous media by the method of full reversal of wave seismic fields). Avtoref. disser. na soiskaniye uch. stepeni d.f.-m.n. (Ph.D. thesis in Physics and Mathematics). Novosibirsk. p. 32. 25. Zhdanov, M. S. (2007). Teoriya obratnykh zadach i regulyarizatsii v geofizike (The theory of inverse problems and regularization in geophysics). M., Nauchnyy mir (Scientific world). p. 712. 26. Kabanikhin, S. I. (2009). Obratnyye i nekorrektnyye zadachi (Inverse and incorrect problems). Novosibirsk. Sib. nauch. izd-vo (Siberian Scientific Publishing House). p. 457. 27. Yakhno, V. G. (1990). Obratnyye zadachi dlya differentsial’nykh uravneniy teorii uprugosti (Inverse problems for differential equations of the elasticity theory). Avtoref. disser. na soiskaniye uch. stepeni d.f.-m.n. (Ph.D. thesis in Physics and Mathematics). Novosibirsk. p. 30.

References

91

28. Ampilov Yu, P., Barkov Yu, A., Yakovlev, I. V., Filippova Ye, K., & Priyezzhev, I. I. (2009). Pochti vse o seysmicheskoy inversii (Almost everything about seismic inversion), CH.1. Tekhnologii seysmorazvedki (Seismic Technologies), 4, 3–16. 29. Barklay, F., Bruun, A., Rasmussen, K. B., Al’faro, K. H., Kuk, E., Kuk, D., Solter, D., Godfri, R., Louden, D., Makkh’yugo, S., Ozdemir, K. H., Pikering, S., Pineda, F. G., Vol’terrani, S., Murineddu, A., Rasmussen, A., & Roberts, R. (2008). Seysmicheskaya inversiya: chitaya mezhdu strok (Seismic inversion: reading between the lines). Neftegazovoye obozreniye (Oil and Gas Review). Vesna. pp. 50–75. 30. Boganik, G. N., & Gurvich, I. I. (2006). Seysmorazvedka (Seismic exploration). Izd. AIS, p. 744. 31. Buzlukov, V. V. (2007) Obratnaya dinamicheskaya zadacha po mnogovolnovym AVOdannym dlya tonkosloistykh sred (Inverse dynamic problem on multiwave AVO data for thin-layered media). Mezhdunar. konf. geofizikov i geologov (International Conference of Geophysicists and Geologists). Russia. Tyumen, 4-7 dek. 32. Voskresenskiy Yu, N. (2001) Izucheniye izmeneniy amplitud seysmicheskikh otrazheniy dlya poiskov i razvedki zalezhey uglevodorodov (Study of changes in the seismic reflections amplitudes for prospecting and exploration of hydrocarbon deposits). Uchebnoye posobiye dlya studentov spetsial’nostey (Study guide for students of specialties) 650200, 650100, 553200. Ministry of Education. RF. Gubkin University, M., p. 68. 33. Geneticheskiye Algoritmy. (2017). 1. Vvedeniye. 2. Osnovnyye ponyatiya. 3. Klassicheskiy geneticheskiy algoritm. (Genetic algorithms. 1. Introduction. 2. Basic concepts. 3. Classical genetic algorithm). Retrieved July 24, from https://coderlessons.com/ 34. Doyetri, D., Friman, M. E., & Kumar, R (2017). Optimizatsiya s ispol’zovaniyem MATLAB i Genetic Algorithm and Direct Search Toolbox (Optimization using MATLAB and Genetic Algorithm and Direct Search Toolbox). Stat’i, materialy po prakticheskim prilozheniyam (Articles, materials on practical applications). Retrieved July 24 from https://codetown.ru, http://zdamson.ru 35. Dan’ko, D. A. (2016). Sravneniye metodov deterministicheskoy akusticheskoy inversii dlya vydeleniya akusticheski kontrastnykh obyektov po seysmicheskim dannym (Comparison of deterministic acoustic inversion methods for identifying acoustically contrasting objects from seismic data). Geofizika (Geophysics), 1, 2–11. 36. Dyubryul, O. (2006). Ispol’zovaniye geostatiki dlya vklyucheniya v geologicheskuyu model’ seysmicheskikh dannykh (Using geostatics to include seismic data in a geological model). SEG EAGE. p. 296 37. Yefremov, V. A., & Grivko, I. L. (2007). Sravnitel’nyy analiz dvukh nelineynykh metodov preobrazovaniya seysmicheskikh dannykh v parametrakh sredy (Comparative analysis of two nonlinear methods for transforming seismic data into medium parameters). Mezhdunar. konf. geofizikov i geologov (International Conference of Geophysicists and Geologists). Russia. Tyumen, 4-7 dek. 38. Zadorina Ye, A., Markelova, L. S., Grigorenko, I. V., & Krylova, M. V. (2014). Razrabotka modeli stroyeniya terrigennogo rezervuara i otsenka neopredelennostey s ispol’zovaniyem geostatisticheskoy inversii (Development of the terrigenous reservoir structure model and assessment of uncertainties using geostatistical inversion). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 84–91. 39. Zadorina Ye, A. (2015). Issledovaniye parametrov geostatisticheskoy inversii dlya prognoza kollektorskikh svoystv po dannym seysmorazvedki (Study of geostatistical inversion parameters for predicting reservoir properties based on seismic data). Dissertatsiya na soiskaniye uchen. step. kand. tekhn. nauk (Thesis for Ph.D. of Technical Sciences). M., Lomonosov Moscow University. p. 117. 40. Kozhenkov Yu, A., Ponomarenko, P. G., Shevchuk, O. L., Gazaryan, Z. I., & Neudachin Yu, D. Prognoz raspredeleniya slozhno-postroyennykh karbonatnykh kollektorov nizhnego devona s ispol’zovaniyem tekhnologii geostatisticheskoy seysmicheskoy inversii na primere Kolvinskogo mestorozhdeniya (Prediction of the complex-built carbonate reservoirs

92

3 Methods for Solving Inverse Dynamic Seismic Problems

distribution of the Lower Devonian using the technology of geostatistical seismic inversion on the Kolvinskoye oilfield example). Fugro-Jason, «Pechoraneft’». pp. 1–4. 41. Kondrat’yev, I. K., Ryzhkov, V. I., Kissin Yu, M., & Shubin, A. V. (2011). Sposoby realizatsii i otsenka effektivnosti seysmicheskoy inversii (Implementation methods and evaluation of the seismic inversion efficiency). Uchebnoye posobiye (Tutorial). Gubkin University. M. p. 62. 42. Konovalov, A. O., & Bychkov, A. V. (2009). Seysmicheskaya inversiya (Seismic inversion). Kursovaya rabota (Course work). Tomskiy politekhnicheskiy universitet (Tomsk Polytechnic University). Kafedra geofiziki (Department of geophysics). 43. Kubyshta, I. I., Pavlovskiy Yu, V., & Yemel’yanov, P. P. (2016). Effektivnost’ tekhnologiy inversii dannykh seysmorazvedki 3D kak osnova postroyeniya i utochneniya seysmogeologicheskoy modeli vendskikh otlozheniy mestorozhdeniya Vostochnoy Sibiri (Efficiency of 3D seismic data inversion technologies as a basis for constructing and refining a seismic-geological model of the Vendian deposits of the Eastern Siberia oilfield). Zhurnal «PROneft’» (Magazine “About Oil”). Nauchno-tekhnicheskiy tsentr «Gazprom Neft’», November 14. Sankt-Peterburg. 44. Loginov, A. K. (2013). Issledovaniye vozmozhnosti nestatsionarnogo i kvaziperiodicheskogo kharaktera vertikal’nogo raspredeleniya parametrov geologicheskoy sredy v zadache seysmicheskoy inversii (Investigation of the possibility of non-stationary and quasiperiodic nature of the geological medium parameters vertical distribution in the seismic inversion problem). Avtoref. dis. na soisk. uch. step. k.f.-m.n. (Ph.D. thesis in Physics and Mathematics). M. p. 25. 45. Metodicheskiye rekomendatsii po ispol’zovaniyu dannykh seysmorazvedki dlya podscheta zapasov uglevodorodov v usloviyakh karbonatnykh porod s poristost’yu treshchinnokavernoznogo tipa (Methodological recommendations on the use of seismic data for calculating hydrocarbon reserves in carbonate rocks with fractured-cavernous porosity). (2010). Pod redaktsiyey V.B.Levyanta (Edited by V.B.Levyant). M., OAO «TSGE». 46. Mnogovolnovyye seysmicheskiye issledovaniya (Multiwave seismic research). (1987). Novosibirsk, Izd. Nauka. Institut geologii i geofiziki (Institute of Geology and Geophysics). N. Puzyrev. p. 213. 47. Neyronnyye seti. 1. Vvedeniye. 2. Paralleli iz biologii. 3. Bazovaya iskusstvennaya model’. 4. Primeneniye neyronnykh setey. 5. Sbor dannykh dlya neyronnoy seti. ... 9. Veroyatnostnaya neyronnaya set’. 10. Obobshchenno-regressionnaya neyronnaya set’. 11. Lineynaya set’.12. Set’ Kokhonena. etc. (2016). (Neural networks. 1. Introduction. 2. Parallels from biology. 3. Basic artificial model. 4. Application of neural networks. 5. Data collection for a neural network. ... 9. Probabilistic neural network. 10. Generalized regression neural network. 11. Linear network. 12. Kohonen network, etc.). Retrieved on October 12 from http://statsoft.ru, http://neuralnet.info. 48. Nefodkina, T. V., Ayzenberg, A. M., Rakshayeva Ye, Z. H., Vylegzhanin, R. I., & Lykhin, P. A. (2015). Ispol’zovaniye effektivnykh koeffitsiyentov otrazheniya v AVO-inversii PPotrazheniy na bol’shikh udaleniyakh ot istochnika (Use of effective reflection coefficients in AVO-inversion of PP-reflections at large distances from the source). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 5–9. 49. Osovskiy, S. (2002). Neyronnyye seti dlya obrabotki informatsii (Neural networks for information processing). Per. s pol’skogo I.D.Rudnitskogo. M., Finansy i statistika (Finance and statistics). p. 344. 50. Popova, P. F., Deliya, S. V., & Bulayeva, N. V. Geologicheskoye modelirovaniye rezervuarov po seysmicheskim dannym s ispol’zovaniyem geneticheskoy inversii, realizovannoy v komplekse Petrel (Geological modeling of reservoirs from seismic data using genetic inversion implemented in the Petrel complex). OOO «LUKOYL-VolgogradNIPImorneft’», Volgograd. pp. 1–4. 51. Priyezzhev, I. I., Shmar’yan Ye, L., & Solokha Ye, V. (2009). Metodika seysmicheskoy inversii s pomoshch’yu geneticheskogo algoritma s posleduyushchim ispol’zovaniyem rezul’tatov inversii pri modelirovanii kollektorskikh svoystv rezervuara (Seismic inversion technique

References

93

using a genetic algorithm with subsequent use of inversion results in modeling reservoir properties). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 18–23. 52. Priyezzhev, I. I., & Solokha Ye, V. (2009). Metodika seysmicheskoy inversii s pomoshch’yu geneticheskogo algoritma s posleduyushchim modelirovaniyem kollektorskikh svoystv rezervuara (Seismic inversion technique using a genetic algorithm followed by reservoir properties modeling). «Geomodel’-2009» - 11-aya mezhdunar. nauchno-prak. konf. po problemam kompleksnoy interpretatsii geologogeofizicheskikh dannykh pri geolog. modelirovanii mestorozhdeniy uglevodorodov (XI International Scientific and Practical Conference on the Problems of Complex Interpretation). Russia. Gelendzhik, 7-10 September. 53. Resheniya Hampson - Russel (Hampson - Russel solutions). (2013). Retrieved on June 6 from http://cgg-geosoftware.ru/index.php/hampsonrussellresheniya/strata-new. 54. Safonov, A. S., Kondrat’yeva, O. O., & Fedotova, O. V. (2011). Poisk neantiklinal’nykh lovushek uglevodorodov metodami seysmorazvedki (Search for non-anticlinal hydrocarbon traps using seismic exploration methods). M., Nauchnyy mir (Scientific world), p. 512. 55. Seysmicheskaya inversiya (Seismic inversion). (2018). Retrieved on January 18 from https:// dic.academic.ru/dic.nsf/ruwiki/, http://petroportal.ru/. 56. Seysmicheskaya inversiya (Seismic inversion). (2012). Fugro-Jason. Retrieved on August 12 from https://cgg-geosoftware.ru, http://docplayer.ru 57. Smirnov, V. N. (2011). Ispol’zovaniye peremennogo impul’sa dlya akusticheskikh inversionnykh preobrazovaniy. Modelirovaniye bez ucheta fazovogo povorota (Using a variable pulse for acoustic inversion conversions. Modeling without the phase rotation). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 79–85. 58. Terekhov, S. A. Lektsii po teorii i prilozheniyam iskusstvennykh neyronnykh setey (Lectures on the theory and applications of artificial neural networks). Laboratoriya Iskusst. Neyronnykh Setey NTO-2 (Artificial Neural Networks Laboratory). VNIITF. Snezhinsk. 59. Filippova Ye, K., Ponomarenko, P. G., Kozhenkov Yu, A., Shevchuk, O. A., & Alabushin, A. A. (2011). Postroyeniye obyemnykh modeley karbonatnykh rezervuarov s ispol’zovaniyem razlichnykh algoritmov inversii volnovogo polya na primere mestorozhdeniya TimanoPechorskoy provintsii (Construction of carbonate reservoirs volumetric models using various algorithms of wave field inversion on the example of the Timan-Pechora province). Tekhnologii seysmorazvedki (Seismic Technologies), 1, 34–46. 60. Filippova Ye K., Gazaryan, Z. I., Kozhenkov Yu, A., Ponomarenko, P. G., & Alabushin, A. A. Ispol’zovaniye geostatisticheskoy inversii chastichno-kratnykh summ dlya postroyeniya ob”yemnoy modeli produktivnykh rifogennykh otlozheniy devona (The use of geostatistical inversion of partial-fold sums to build a volumetric model of productive reef deposits of the Devonian). Fugro-Jason, LUKOYL-Komi. pp. 1–4. 61. Tsybul’kina, I. S., Serzhanovich, I. A., Shevchuk, O. A., & Chaynikov Yu, G. Postroyeniye ob”yemnoy modeli terrigennykh rezervuarov s ispol’zovaniyem razlichnykh tekhnologiy seysmicheskoy inversii na primere Sosnovskogo mestorozhdeniya Timano-Pechorskoy provintsii (Construction of a terrigenous reservoirs volumetric model using various technologies of seismic inversion on the example of the Sosnovskoye field of the Timan-Pechora province). Fugro-Jason, OOO “TSNPSEI”. pp. 1–4. 62. Shestakova, G. M., Zakharova, O. A., & Volkov, G. V. (2013). Vydeleniye kollektorov po dannym stokhasticheskoy i deterministicheskoy inversiy v neokomskikh otlozheniyakh Zapadnoy Sibiri (Reservoirs Identification based on stochastic and deterministic inversions in the Neocomian sediments of Western Siberia). Neftyanoye khozyaystvo (Oil Industry), 12, 28– 29. 63. Yakovlev I.V., Filippova Ye, K., & Barkov Yu, A.. Primeneniye geostatisticheskoy inversii dlya vydeleniya malomoshchnykh pronitsayemykh zon v gazonasyshchennykh plastakh (Application of geostatistical inversion to identify thin permeable zones in gas-saturated reservoirs). OOO «Gazprom VNIIGAZ», Fugro-Jason. pp. 1–4.

94

3 Methods for Solving Inverse Dynamic Seismic Problems

64. Yakovlev, I. V., Ampilov Yu, P., & Filippova Ye, K. (2011). Pochti vse o seysmicheskoy inversii (Almost everything is about seismic inversion). CH.2. Tekhnologii seysmorazvedki (Seismic Technologies), № 1. pp. 5–15. 65. Haas, A., & Dubrule, O. (1994). Geostatistical inversion—a sequential method of stochastic reservoir modeling constrained by seismic data. First Break, 12(11), 561–569. 66. Pustarnakova Yu, A., & Akhmetova, E. R. (2002). Iskusstvennaya neyronnaya set’ kak instrument prognozirovaniya geologicheskikh parametrov po seysmicheskim atributam i dannym bureniya (Artificial neural network as a tool for predicting geological parameters from seismic attributes and drilling data). Geofizika, spets. vypusk «Tekhnologii seysmorazvedki-I» (Geophysucs, special edition of “Seismic Technologies”), pp. 117–121. 67. Yanevits, R.B., Bespechnaya Yu, L., & Bespechnyy, V. N. Postroyeniye seysmogeologicheskikh modeley osadochnykh otlozheniy na osnovanii dannykh seysmorazvedki i PGIS (Construction of seismicgeological models of sedimentary deposits based on seismic and well field survey data). OAO «Sibneftegeofizika», Novosibirsk. pp. 1–12. 68. Specht, D. F. (1990). Probabilistic neural networks/neural networks (Vol. 3, pp. 109–118). 69. Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568–576. 70. Masters, T. (1995). Advanced algorithms for neural networks. Wiley. 71. Molyarova, T. N. (2007). Seysmofatsial’nyy analiz kak universal’noye sredstvo ponimaniya stroyeniya rezervuara (Seismic facies analysis as a universal means of understanding the reservoir structure). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 79–87. 72. Averbukh, A. G. (1982). Izucheniye sostava i svoystv gornykh porod pri seysmorazvedke (Study of the composition and properties of rocks during seismic exploration). M., Nedra, p. 232. 73. Berezkin, V. M., Kirichek, M. A., & Kunarev, A. A. (1978) Primeneniye geofizicheskikh metodov razvedki dlya pryamykh poiskov mestorozhdeniy nefti i gaza (Application of geophysical exploration methods for direct exploration of oil and gas fields). M., Nedra, p. 224. 74. Kuznetsov, O. L., Petukhov, A. V., Zor’kin, L. M., Zubayrayev, S. L., Kirichek, M. A., & Popsuy-Shapko, G. P. (1986). Fiziko-khimicheskiye osnovy pryamykh poiskov zalezhey nefti i gaza (Physical and chemical foundations of direct prospecting for oil and gas deposits). M., Nedra. p. 336. 75. Mandel’baum, M. M., Puzyrev, N. N., Rykhlinskiy, N. I., Surkov, V. S., & Trofimuk, A. A. (1988). Pryamoy poisk uglevodorodov geofizicheskimi metodami (Direct search for hydrocarbons by geophysical methods). Akademicheskiye chteniya AN SSSR (Academic readings of the USSR Academy of Sciences ). M., Nauka, 1988. p. 160. 76. Gorodnichev Ye, N. (1970). Pryamyye poiski nefti i gaza metodom otrazhennykh voln (Direct prospecting for oil and gas by the method of reflected waves). Novosti zarubezh. literatury. Region., razved. i promysl. geofizika. Vyp. 8, 1971 g. (referaty 2325). M.: VIEMS, S. 1-9. (Poh-Hsi Pan. De Bremaecker J. Cl. Direct location of oil and gas by the seismic reflection method. “Geophys. Prosp.” 18, Suppl., pp. 712-729 (angl.)). 77. Mak-Kuillin, R., Bekon, M., & Barklay, U. (1985). Vvedeniye v seysmicheskuyu interpretatsiyu (Introduction to seismic interpretation). M., Nedra. p. 308. 78. Rudnitskiy, V. P. (1988). Vozmozhnosti detal’noy parametrizatsii geologicheskogo razreza s pomoshch’yu seysmicheskikh nablyudeniy v burovykh skvazhinakh (Possibilities of detailed parameterization of a geological section using seismic observations in boreholes.). Geofiz zhurnal (Geophysical Journal), 10(1), 67–78. 79. Fizicheskiye svoystva gornykh porod i poleznykh iskopayemykh (petrofizika). (1984). (Physical properties of rocks and minerals (petrophysics)). Spravochnik geofizika. Pod red. N.B. Dortman (Geophysics handbook edited by N.B.Dortman). Second Edition. M., Nedra. p. 455. 80. Ellanskiy, M. M., & Yenikeyev, B. N. (1991) Ispol’zovaniye mnogomernykh svyazey v neftegazovoy geologii (The use of multidimensional relationships in oil and gas geology). M., Nedra, p. 205.

References

95

81. Wyllie, M., Gregory, A., & Gardner, G. (1956). Elastic wave velocities in heterogeneous and porous media. Journal of Geophysical Research, 21(1), 41–70. 82. Kozlov Ye, A. (2006). Modeli sredy v razvedochnoy seysmologii (Medium models in exploration seismology). Tver’: GERS. p. 480. 83. Chopra, S., Castagna, J. P., & Portniagune, О. (2006). Seismic resolution and thin-bed reflectivity inversion. CSEG Recorder, 31(1), 19–25. 84. Sheriff R., & Geldart L. (1987). Seysmorazvedka (Seismic exploration). Tom 1 (448 pages), Tom 2 (400 pages). M., Mir. 85. Zhukov, I. M. (1980). Ispol’zovaniye neftegazokontroliruyushchey roli nekotorykh tolshch v kachestve osnovy povysheniya effektivnosti poiskov nefti i gaza (Using the oil and gas control role of certain strata as the basis for increasing the efficiency of oil and gas exploration). Geologiya nefti i gaza (Oil and Gas Geology), 10, 42–45. 86. Urupov, A. K. (2004). Osnovy trekhmernoy seysmorazvedki (Fundamentals of 3D seismic prospecting). M., “Neft’ i gaz” (“Oil and Gas”). p. 584. 87. Zoeppritz, K. (1919). Uber reflexion und durchgang seismischer Wellen durch Unstetigkerlsfläschen: Berlin, Uber Erdbebenwellen VII B, Nachrichten der Königlichen Gesellschaft der Wissenschaften zu Göttingen. Math-Phys., K1, 57–84. 88. Aki, K., & Richards, P. (1983). Kolichestvennaya seysmologiya (Quantitative seismology). T.1, M., Mir, p. 519. 89. Chopra, S., Castagna, J., & Yong, X. (2009). Thin-bed reflectivity inversion and some application. First Break, 27, 55–62.

Chapter 4

Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Abstract The main features of the method developed by the authors and the methodology of its application, made in the form of the HRS-Geo technology, are considered. The solution of the IDSP is found by the optimization method, which consists of the selection of the AI and RC models to a given structure of the wave field (WF) according to the known formulas for solving the direct problem for calculating the seismic wave field. In this case, the convolutional model algorithm is used, in which it is possible to take into account the noise level, the residual background of multiple waves, and the regularization factor. In general, the study of real geological environments is focused on the use of the seismic record dynamic features and the implementation of its maximum possible resolution, namely, the construction of 2D sections and 3D cubes of effective acoustic impedance (AI) and reflection coefficients (RC), which have a vertical resolution equal to the sampling step of the seismic record in time. One of the main advantages of the technology over modern inversion methods is that it is not tied to borehole data. The detail of the section study, i.e., the vertical resolution of the inversion results, in the complete absence of borehole data, is the sampling step of the seismic survey (along the time 1 or 2 ms, which corresponds to a depth of 3–6 m). A general scheme for solving the inverse dynamic problem of seismics and interpreting the results in the HRS-Geo technology is presented. The assessment of the chosen approach correctness to the solution of inverse dynamic seismic problem (IDSP) is given, and the reliability and accuracy of the acoustic model restoration results are determined. Examples of solving the inverse dynamic problem of seismics on test and real materials are given. It is shown which factors have a significant impact on the reconstruction results of a detailed thin-layer medium model and the methods of their computing. A new type of noises (wave interference) in the structure of the seismic wave field, which limits the maximum detail and information content of the studied environment, is introduced in the solution of thin-layer geological problems using the HRSGeo technology. The chapter considers the features of seismic data preprocessing using the HRS-Geo technology, which ensures the maximum possible preservation of primary seismic waves against the background of various non-useful regular waves and random noises.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_4

97

98

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

To study real thin-layer media, the authors developed a numerical method for solving the inverse dynamic seismic problem (IDSP) [1–3]. Program-methodical realization of this method and the technique of its application are made in the form of HRS-Geo technology. The solution to the IDSP is found by an optimization method, which consists in selecting models of the AI and RC to a given structure of the wave field (WF) using the known formulas for solving a direct problem for calculating the seismic wave field. In this case, the algorithm of the convolutional model is used, in which it is possible to take into account the level of noise, the residual background of multiple waves, and the regularization factor. In general, the study of real subsurface media is focused on the use of dynamic seismic record features and the implementation of its maximum possible resolution, namely, the construction of 2D sections and 3D cubes of effective acoustic impedance (AI) and effective reflection coefficients (RC) with vertical resolution equal to the sampling step seismic time recording [4–6]. Let us take a closer look at the features of this development and the method of its application on seismic observation materials, starting with some well-known general theoretical principles of the seismic method of research and development of the corresponding software and methodological support by the authors.

4.1

Physical Basis for Finding the Properties of a Real Medium and Searching the Optimal Solution to the Inverse Problem

One of the main objectives of seismic research methods, as is known, is to find the properties of matter in a real geological environment through the properties of artificially generated and received seismic waves. Elastic waves are repetitive in space and in time the motions of the medium particles. The elastic properties of the medium in this case determine the nature of the particles motion, thereby forming a spatial wave field. The characteristics of the wave field observed in a limited area (on a surface or in a borehole) are directly related to the elastic properties in the deep parts of the subsurface medium. Finding the parameters of the medium and their distribution in space is the essence of the inverse dynamic seismic problem solution, in which the measured dynamic characteristics of the wave field of the space-time process of seismic oscillations are given parametrized properties of the medium— the spatial distribution of the real subsurface medium parameters. The wave propagation physics in the environment is as follows. The forces acting on the medium particles, in the general case, lead them to the motion with acceleration. Small volumes of medium particles that are moving create periodically changing deformations of the real environment, creating time-varying internal stresses (pressure). In accordance with this, there is a relation between stresses and deformations of the medium. In a static state (equilibrium), such a relationship is

4.1 Physical Basis for Finding the Properties of a Real Medium and. . .

99

described by the state equation. In the general anisotropic case and in the case of small deformations, the relationship between stress and strain is linear [7–11]: σ ij ¼

3 X 3 X

Cijkl εkl ,

k¼1 l¼1

where i, j ¼ 1, 2, 3; C is the elastic modulus tensor describing the properties of the medium; σ ij is the stress components; and εkl is the deformation components. In the matrix form, taking into consideration the symmetry of stresses (σ ij ¼ σ ji), the state equation takes the form 2 6 6 6 6 6 6 6 6 6 4

σ xx σ yy σ zz σ xy σ xz σ yz

3

2

7 6 7 6 7 6 7 6 7 6 7¼6 7 6 7 6 7 4 5

c11 c12 c13

c14

c15

c16

c21 c31

c22 c32

c23 c33

c24 c34

c25 c35

c26 c36

c41

c42

c43

c44

c45

c46

c51 c61

c52 c62

c53 c63

c54 c64

c55 c65

c56 c66

3 2ε xx 7 6 ε yy 7 6 7 6 εzz 7 6 76 7 6 ε xy 7 6 7 6 6 5 4 εxz

3 7 7 7 7 7 7 : 7 7 7 5

εyz

For an isotropic medium, expressing the elements of the matrix C in terms of the Lamé coefficients (considering symmetry), we obtain the following equation: 3 2 σ xx λ þ 2μ λ λ 0 6σ 7 6 6 yy 7 6 λ λ þ 2μ λ 0 7 6 6 σ zz 7 6 6 λ λ λ þ 2μ 0 7 6 6 σ xy 7 ¼ 6 6 7 6 0 6 0 0 μ 7 6 6 σ xz 7 6 0 0 0 5 4 0 4 0 0 0 0 σ yz 2

3 2 0 6 07 7 6 7 6 0 07 6 76 6 0 07 7 6 7 6 μ 05 6 4 0 μ 0 0

3 εxx εyy 7 7 7 εzz 7 7 , εxy 7 7 7 εxz 7 5 εyz

where λ ¼ с33 - 2с44 and μ ¼ с44. The elastic characteristics of the medium, together with the density of matter, are knownqtoffiffiffiffiffiffiffiffi be related to the wave propagation qffiffivelocity: for a longitudinal wave VP ¼

λþ2μ ρ ;

for a transverse wave V S ¼

μ ρ,

where ρ is the density of the

substance and VP and VS are the velocity of the longitudinal and transverse waves, respectively. Under the influence of the time-varying force of the oscillations source, the system is taken out of equilibrium—the changing stresses create variable deformations that propagate in space in the form of elastic waves. This process is described by the known motion equations (or by introducing bias potential, wave equations). For the 3D case, the equation of motion in the matrix form looks like this:

100

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

2

2

∂ ux 6 2 6 ∂x 6 2 6 ∂ uy 6 6 ∂y2 6 6 2 4 ∂ uz ∂z2

2

2

2

2

2

2

∂ ux ∂ u x þ 2 ∂y2 ∂z ∂ uy ∂ u y þ 2 ∂x2 ∂z ∂ uz ∂ u z þ ∂x2 ∂y2

3 2 2 3 2 2 ∂ uy ∂ uz ∂ ux þ 7 7 ∂x∂y ∂x∂z 7 2 2 3 6 6 ∂t 2 7 VP 7 7 6 2 2 6 2 7 6 ∂ 2 uy 7 ∂ ux ∂ uz 7 7  ¼ V 7 , 6 4 5 þ S 6 ∂t 2 7 ∂x∂y ∂y∂z 7 7 2 7 6 VP 7 4 ∂2 u 5 2 2 z ∂ ux ∂ uy 5 þ ∂t 2 ∂x∂z ∂y∂z

where ux, uy, and uz are the projections of particle displacement during wave propagation. The matrix of derivatives with respect to spatial coordinates in the left part of the system describes the behavior of particles at points in space; the column vector of the right part describes the behavior of particles in time by means of acceleration projections (second derivatives with respect to time). The column vector on the left side of the equation, whose elements represent the squares of the velocities of the waves VP and VS, relates the particle motion distribution in space over time. In the simple case, an analogue of such a connection can be the uniform motion of a point, described as x ¼ Vt, which also relates the spatial and time space values. In general, the geological environment under consideration was formed during sedimentation and is a complex thin-layer heterogeneous system of sediments. The method of studying the properties of medium matter using reflected seismic waves is based on the study of the ability of medium inhomogeneities to form and reflect elastic waves. In this case, the main characteristic of the medium for the seismic method is the distribution of the elastic wave reflectivities. In practice, for the medium parameterization and the solution of the inverse dynamic seismic problem, it is convenient to express the reflection coefficients not through elastic modules, but through the wave velocity and the rock density of the substance and acoustic impedance of the medium: R¼

ρi V i  ρi1 V i1 AI i  AI i1 ¼ , ρi V i þ ρi1 V i1 AI i þ AI i1

where V and ρ are the wave velocity and density, respectively; AI is the acoustic impedance of the medium; i is the media indexes; and R is the interface reflectivity between media at normal wave incidence. Traditionally, inverse problems are formulated within the framework of certain models. For the models accepted, direct problems are solved—the calculated wave fields are defined. The wave fields measured and processed accordingly are compared with the calculated ones. For a quantitative characteristic comparison of two or several wave fields, measures of their difference are formulated in the form of objective functions (metrics or response functions). The expression of the objective function includes the desired parameters of the environment in accordance with the accepted model under study. The complexity of the model is determined depending on the structure of the source data from which the desired parameters about the

4.1 Physical Basis for Finding the Properties of a Real Medium and. . .

101

geological environment are to be extracted. For example, if during processing it was not possible to suppress multiple waves, one should study their characteristics and synthesize them in a model for a direct problem. Also, if there are irregular noises in the structure of the wave field, it is necessary to investigate their nature and simulate the nature and statistical parameters of such noises when modeling the wave process. In the initial wave field data, it may turn out that absorbing effects are not taken into account or undercompensated—to correctly solve the inverse problem of seismic, in such a case, it is necessary to complicate the model of the medium by including characteristics describing the absorbing properties of the studied medium. In order to study how complex the model of the medium should be in order to obtain a reliable and maximally credible solution of the inverse problem, it is necessary to consistently determine the contribution of each of the effects to the wave field or to the results of its transformation, as shown above in Sect. 2.2 [1, 3, 12]. Based on the analysis of each factor influence, the most significant of them are selected, and thus the complexity of the model is determined. In the same way, the type of model space is chosen, within which the search for the optimal model is to be performed (1-, 2-, or 3D reservoir model; piecewise linear or piecewise constant of the reservoir model; the parameter determining the degree of thin-layering; the features of using the preprocessing graph procedures and the optimal search process; etc.). A solution component of the inverse dynamic seismic problem is the effective search for the optimal solution, i.e., a solution which contains the most amount of reliable information about the subsurface environment, and at the same time, the search obtained with the least cost of iterative calculations. This is due to the fact that solving an inverse dynamic seismic problem requires an extremely large volume of computation, since in the process of finding a solution, it is necessary to repeatedly calculate and compare synthetic wave fields for a large amount of input seismic data. In addition, finding the optimal solution of the inverse problem is connected with the search for the function extremum of several variables. For practice, various computational schemes have been developed to search for the extremum of such functions. Effective methods for a particular model of the object being studied are selected depending on the initial data set, the complexity of the accepted model, and applied restrictions to it. Consider the most used computational optimization methods in brief. Let an n-dimensional objective function f ðxÞ be given, where x ¼ ðx1 , x2 , . . . , xn Þ is an n-dimensional vector. The inverse problems to ensure the uniqueness and stability of the solution usually set the constraints (conditions) of the form. c1i ðxÞ ¼ 0,

c2i ðxÞ  0,

i ¼ 1, . . . , n;

a  x  b,

a ¼ ða1 , . . . , an Þ,

b ¼ ðb1 , . . . , bn Þ: It is necessary to investigate the function and find the vector xopt, which determines the minimum of the objective function f ðxÞ ) min .

102

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.1 Residual objective functions between real and calculated seismic traces characterizing nonuniqueness of the inverse problems solution: (a) an example of the objective function having several local minima, (b) an example of the objective function slightly changing in the vicinity of the global minimum point (according to Ref. [18])

As is well known, inverse problems are ambiguous [13–17]. The objective functions representing the discrepancies between real and computed seismic traces in such tasks, as a rule, have several local minima (the task is largely ambiguous) xlc1, xlc2, and xlc3 (Fig. 4.1a). A reliable model will be represented by a solution that corresponds to the global minimum of the objective function. To determine the unique solution, it is very important to find such a global minimum xopt (Fig. 4.1). Another problem that arises when solving the inverse seismic problem is the instability of the solution. This feature of the problem solution is that with small changes in the source data, the desired solution can change abruptly, taking unreal values, while the solution is not continuous with respect to the source data. If the inverse problem is presented in the form of a system of equations, then the instability manifests itself as a poor conditionality of the solved system (with a slight perturbation of the system right side B, the solution X varies greatly: A*X¼B, where A is coefficients matrix of the system). If an objective function is formulated for solving the inverse problem, then a sign of instability is the presence of an argument domain in which the value of the objective function is constant or the objective function changes slightly in the vicinity of the global minimum point, as shown, for example, in Fig. 4.1b. At the same time, the stability of the solution is ensured by applying various constraints to the sought model (or to the model space, in which the optimal model is searched by narrowing the search area until the problem is stable), smoothing procedures to the model (during parameterization) and selecting regularizing factors (by replacements of the original problem—another, close, but more stable). The solution of inverse dynamic seismic problems is directly related with the use of optimization methods. In practice, direct optimization methods can be used [18, 19]. Their essence consists in constructing a sequence of solutions R1,. . .,Rn, for which the value of the objective function consistently decreases

4.1 Physical Basis for Finding the Properties of a Real Medium and. . .

103

f(R1) > f(R2)>,. . .,>f(Rn). The starting point of the solution search process is selected based on a study of the source data and the behavior of the objective function itself. For successful optimization, the starting point is to be chosen closer to that of the global minimum. In this case, the algorithm for constructing a solution consists of two stages: the choice of the movement direction to the next point, where the value of the objective function decreases, and the movement step determination. Methods for finding a point with a minimum of the objective function can be divided into deterministic and stochastic. In the deterministic search method, the direction and/or size of the descent step to the minimum point is determined uniquely, based on the analysis of the objective function of previous points. In the case of a stochastic method, random variables are used to go from one point (model) to another—the search represents a stochastic process. Deterministic and stochastic methods are also used to classify methods for solving inverse seismic problems. In the case of using deterministic methods, the minimization process is fully defined and depends on the choice of source data and initial parameters—here the disadvantage is that you can skip the global minimum. With stochastic methods, the solution at each step is chosen randomly—in this case, a large number of models must be analyzed. In these methods, as is well known, random variables are described by the probability density function characteristics of a random variable and are used directly in the objective function creation. Currently, a combination of these two approaches, implemented in different algorithms, is often used to take advantage of each of these two optimization methods. Thereby, the scope of each of these applied methods is narrowed or expanded.

4.1.1

Optimization Method for Solving the Inverse Problem in the HRS-Geo Technology

In the HRS-Geo technology, an analogue of the direct search method [19] is used to search for a solution. This method refers to methods of zero order (the Hooke-Jeeves method) [18]. The method was chosen because there is not necessary to calculate the derivatives of the objective function to be determined. The advantages of this method are that the objective function can be quite complex and it can also have discontinuous derivatives. Also, the objective function may not have an explicit representation, but is given, for example, in the form of an equation system. The disadvantages of the method include relatively slow convergence to the optimal solution compared to higher-order methods, where the derivatives of the objective function are used and, therefore, limit their use. The optimization of the direct search method used is as follows. The initial approximation R0 is set. The objective function is investigated in a neighborhood of the point R0. The range of directions from the point R0 is selected. From a set of directions, one is chosen in which the objective function decreases more than in

104

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

other directions. Along this line, the minimum point of R1 is searched, in which f(R1) < f(R0). If in this direction it is not possible to determine the minimum point, the search step is reduced. If the minimum point along the selected line is not determined, then searching to another direction is moved. With the transition to a new point, the cycle repeats. Calculations are stopped when a given level of the objective function f(Rn) fend is reached or it is impossible to find a subsequent minimum point. As a solution, the last found minimum point is taken. This one will correspond to the solution of the inverse problem. It should be added that the solution of inverse dynamic seismic problems is complicated by the fact that it is necessary to achieve two mutually opposite criteria: to achieve maximum resolution of the solution and its stability in the process of finding the decision. To overcome this contradiction, the HRS-Geo technology uses multicriteria optimization [20, 21]. The essence of this optimization is that when finding a solution, not one objective function is used, but a set of objective functions—that is, vector objective function F(x) = [f1(x), . . ., fm(x)]. To ensure the solution correctness of the inverse problem (uniqueness and stability), conditions can be set for each objective function, and regularization can be applied. Thus, in the process of multicriteria optimization, the vector of objective functions is minimized under the constraints of the form: C1i ðRÞ ¼ 0,

C2i ðRÞ  0,

i ¼ 1, . . . , n;

Ra  R  Rb ,

where Ra and Rb are areas of application of equality and/or inequality type constraints on the model. In general, speaking about the seismic data resolution, a question arises: to what detail can the subsurface environment be restored? Considering the components of seismic recording in the spectral domain, it can be noted that the real environment, represented by a reflectivity sequence, has a broadband spectrum that occupies a frequency range up to the Nyquist frequency. Seismic impulse, probing medium, is finite in time. It should also be considered as a broadband signal, despite the fact that most of its energy is concentrated in a narrow frequency band. Consequently, the seismic trace, obtained as a result of filtering a reflectivity sequence (their pulse set) by a probing wave impulse, also has a wide spectral range due to the presence of components that are weak in energy. In any case, the signals occupy a wide range of frequencies, remaining even weak in certain intervals of the frequency band. Therefore, the potential possibility of their extraction exists. At the same time, the limitation in detail is due to the structure and noise level in the source data and the possibility of their selection and suppression (this is a problem mainly in the preprocessing of the original seismic data). Answering the above question (to what detail is it possible to restore the subsurface environment?), we can say: to the extent that there are broadband weak signals in the source data. In practical survey to study perspective objects with a discretization step of seismic data of 1–2 ms at depths of 3000–4000 m, this possibility still remains with strictly correct execution of all seismic exploration and prospecting stages from field survey, data processing to interpretation, and model construction.

4.1 Physical Basis for Finding the Properties of a Real Medium and. . .

105

Fig. 4.2 An example of the initial wave impulse of the reflected waves described in the time and spectral domains

In the process of solving the inverse dynamic seismic problem, an important role is assigned to the stage of determining the shape of the original seismic signal. This is all the more important because a seismic wave pulse is involved in solving a direct dynamic problem based on the application of a convolutional model, which must first be found. The seismic trace inversion procedures in the reflectivity distributions and acoustic impedance curves are most often applied to the stack wave field traces obtained during the preliminary processing of field survey data. It is obvious that the probing medium elementary wave pulse undergoes significant changes, and, therefore, to simulate the traces, it is necessary to use the initial pulse of reflected waves corresponding to the stack time field. The wave impulse is usually a function of time. However, it can be defined and described both in the time and spectral domains (Fig. 4.2). In the frequency domain, it is convenient to describe the wave pulse through the amplitude and phase spectra. Moreover, if s(t) is a seismic pulse, then S(jw) is the R/ Fourier transform, and SðjwÞ ¼ sðt Þ  ejw dt; then S(jw) = |S(jw)|  ejP(w), where /

S(jw) is the complex spectrum, |S(jw)| is the amplitude frequency spectrum, P(w) is the phase spectrum, w is the cyclic frequency, and j is the imaginary unit. To determine the wave impulse, there are many ways and methods. In one study [22], in particular, three main classical methods for extracting a wave impulse were described: signal evaluation for a minimum-phase model, signal evaluation of an arbitrary shape using homomorphic filtering methods, and signal evaluation using borehole data. We only note some features of the application of these methods in brief, bearing in mind their detailed description in [22]. When evaluating a signal in the framework of the minimum-phase model, the property of the minimum-phase function is used: its amplitude spectrum is uniquely related to the phase one. Determining the amplitude spectrum of the signal unambiguously, they also find the phase spectrum, thereby restoring the entire wave pulse. There are different ways to estimate the amplitude spectrum of a signal. You can use the property of seismic trace autocorrelation function: its proportionality to the wave pulse autocorrelation function. Then the amplitude (energy) spectrum of the signal is determined by the direct Fourier transform of the autocorrelation function. In this

106

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

method, it is assumed that the autocorrelation function of the pulse trace is proportional to a unit impulse, i.e., r(t)~δ(t). Extracting the original signal of arbitrary shape allows the method of homomorphic filtering. In this method, the signal is transformed into a spectral domain; the logarithm of the signal spectrum is calculated; and the inverse Fourier transform is performed, passing into the cepstral domain [22–25]. In this domain, the wave impulse and the pulse trace are additively presented. In this case, the signal is in the lower range of the cepstrum argument, and the impulse trace is in the higher part of the band. Performing low-frequency filtering (cutting in the cepstral domain) and performing the inverse transformations of the signal into the time domain, the desired wave pulse is found. Here, in the cepstral domain, a significant difference of the signal from the medium pulses is assumed. In practice, the use of homomorphic filtering for pulse estimation is a rather ambiguous process, and it is associated with the separation of the wave impulse phase from the complex structure of the seismic trace one. Determination of the original wave impulse using the data of SN, GGL, and VSP on the wells consists in calculating the pulse seismogram. The selection of a wave pulse from a convolution model can be performed, for example, by calculating the cross-correlation function of the pulse and seismic traces. Here, the assumptions about the uncorrelated noise and the pulse trace and the randomness of the reflection coefficients are used. This method is used for stratigraphic deconvolution, as well as for the transformation of the wave field to the well data in different seismic inversion software packages (see Sect. 3.2). The most stable with respect to methods of homomorphic filtering and calculation using pulse trace is the evaluation of the waveform from the autocorrelation function. It is also worth noting that in order to ensure the stability and reliability of these methods, various methods of effect accumulation, averaging, regularization, filtration, etc. in assessing the wave impulse are used. Within the framework of the HRS-Geo technology, various methods for extracting a seismic wave impulse were tested. However, in the practice of using the technology, in most cases, a modified method of pulse calculation by the autocorrelation function with the possibility of phase correction is used as the simplest to implement and reliable in stability. Wave field traces are often the result of preprocessing, which are converted to a zero-phase signal format. If at the same time during the processing no significant phase transformations of the signal were applied, then the phase characteristic of the signal remains close to the minimum-phase structure, although it differs from the initial one in frequency composition. If, however, there are differences in the waveform from the minimum phase signal, then it is possible to choose a phase correction to the phase characteristic of the calculated minimum phase signal during the testing of the inversion process. Mixed-phase signal estimates are necessary when linking seismic data to well data. At the same time, as noted in the Sect. 3.3, there is a significant difference between such data (the transformation of seismic data under the well may cause significant distortions and loss of useful information).

4.1 Physical Basis for Finding the Properties of a Real Medium and. . .

107

Fig. 4.3 To the solution of the IDSP for thin-layer model on the fragments of: PAL trace (a) and seismic trace (b) as smooth functions; distribution of reflection coefficients (c), and acoustic impedance (d) as discontinuous functions

In the HRS-Geo technology packages, the seismic impulse is extracted from single trace, i.e., for each separately inversable trace, its own wave impulse is estimated, which in turn is used in the inversion procedure. This method avoids the effects of smoothing the result over the entire volume of seismic data. Smoothing effects lead to the loss of detail and reliability of the estimated model of the medium (distributions of acoustic impedance and reflectivity). The peculiarity of the search for a solution to the inverse dynamic seismic problem for a thin layer medium also consists in the fact that the processed seismic traces with a limited frequency band represent some continuous (smooth) functions (Fig. 4.3b). Of these, it is necessary to obtain a solution in the form of discontinuous functions, while thin-layer AI and RC have a wide frequency spectrum (Fig. 4.3c, d), in contrast to the PAL traces, where a smooth function is displayed in a smooth one (Fig. 4.3a). In other words, the input data and the desired results of the transformation are in different classes of functions—a class of continuous (smooth) functions and a class of discontinuous, respectively. In this sense, obtaining a solution is a mapping of finite-dimensional functions from one class to another using the solution operator of the inverse problem. With this approach to solving inverse problems, direct and inverse problems are solved iteratively and sequentially; direct and inverse transformation operators are built and applied to the data. In this case, as a rule, direct operators are stable, and the inverse is always unstable [17]. The reason for the manifestation of such operator properties can be explained (justified) as follows: during the formation of a seismic wave field (geophysical), as in other physical processes, there is a law of energy (and information) conservation. The energy of the wave field after its passage through the medium cannot be greater than the energy of the original probe wave pulse (unless the medium additionally releases it in the measurement process, there are no other

108

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

wave sources in the medium during seismic surveys). In this sense, the actual process of the wave field formation is always “stable.” Why, in practice, direct and inverse operators, numerically simulating the wave process and procedures for extracting data about the medium, have different properties with respect to stability or are not always stable? One of the reasons for the relative stability of the direct problem operator is its integral nature. The values of the calculated characteristics of the field are the sum of the influence (contribution) of many factors. The direct operator contains mainly the operations of addition, multiplication, and integration. It may be unstable if not all factors of influence (contribution) in the wave field are taken into account (e.g., with a simplified approximation of a real wave process when building a model). The reason for the instability of the inverse operator is differential in nature when applied to real or model data. When constructing the inverse operator, the main actions are the operations of differentiation, finding the value difference, dividing it into small numbers, i.e., actions opposite to integral procedures, the application of which can lead to large, poorly controlled changes and the results can vary greatly and even in small changes in input data—for example, the presence of small magnitude noise (even rounding, discretization, and quantization errors in analog to digital converter (ADC), etc. could have an effect). In the HRS-Geo technology, the solution to the stability problem is given special attention. The solution of the inverse dynamic seismic problem (IDSP) in the HRS-Geo technology is found by the optimization method, which consists in the selection of models of AI and RC to the given structure of the wave field according to the known formulas of the direct problem solution for the seismic wave field calculation [2]. At the same time, as already noted above, the convolution model algorithm for solving the direct dynamic seismic problem is used, in which it is possible to take into account the noise level, the residual background of multiple waves, and the regularization factor. To optimize the solution, a system of objective functions (vector of objective functions) has been developed, in which various types of residuals between real and model data are iteratively calculated. A feature of this system is that it separately calculates and analyzes the residuals in energy and in the form of signals and makes a decision on the whole model optimization. This separates the consideration of the wave field characteristics for two reasons: – Amplitude (energy) residuals allow you to control the stability of the convergence iterative process of the original model to the desired solution and analyze the sign for optimal dynamic regularization of the finding a solution process; – Phase residuals allow you to more successfully manage the solution of the problem, selecting the optimal from a variety of solutions. The process of searching for the AI and RC model is completed when the vector of the objective functions system is stabilized and/or when they reach specified limit level. There is another reason for the separate estimation of residuals (metric vector). The phase characteristics of the wave field are more sensitive to changes in the seismic properties of the medium and contain much more information about the

4.1 Physical Basis for Finding the Properties of a Real Medium and. . .

109

subsurface medium than the energy ones. In turn, the use of energy factors give stability to the inversion system. The combined use of the described characteristics allows you to extract the distribution of the medium properties with maximum detail in the process of the stable wave field data inversion. It is obvious that the response function system (vector of functions) with restrictions can be represented as:   8 λ1 ðRÞ ¼ E f ðRÞ  E m ðRÞ þ α1 ðiÞ  Ω1 ðRÞ ) min , > >   > < λ2 ðRÞ ¼ P f ðRÞ  Pm ðRÞ þ α2 ðiÞ  Ω2 ðRÞ ) min , > SðRÞ  c, R ) fix, > > : SðRÞ < c, R ) var: where λ1 and λ2 are the objective functions (functionals); Ef(R) and Em(R) are the energy of real and calculated traces, respectively; Pf(R) and Pm(R) are the phase characteristics of real and calculated traces corresponding to the form of the reflected signal, respectively; Ω1 and Ω2 are stabilizing functionals; α1(i) and α2(i) are the regularization parameters of the corresponding functionals, dependent on iteration number i; S(R)  c, S(R) < c are the constraints in the form of inequalities that determine fixed and variable parameters; с is a constant that determines the reliability of the solution obtained; R is the inverse dynamic problem solution. In the process of performing the task, the solution is discarded from consideration if one of the criteria (objective functions) does not achieve optimality or find optimal, but contradictory models for each objective function. Such a situation is possible with complex reliefs of objective functions, for example, the models found correspond to different minima in multiextremal objective functions. Optimal decisions are made when criteria for all objective functions are reached. The stability of the solution of the inverse problem is also ensured by applying the variable parameters of the regularization α1(i) and α2(i) in the process of iterative data processing. The process of finding a solution R to the above system stops when the threshold values of the functionals are reached or the solution stabilizes.

4.1.2

The General Scheme for Solving an Inverse Dynamic Problem and Interpreting Results in the HRS-Geo Technology

The scheme for implementing the inverse dynamic problem solution and the interpretation of the results include the following: – Specialized preliminary processing of field seismic survey data with preservation of true amplitude ratios, maximum attenuation of regular and random noise, minimal seismic signal distortion, and data migration (Sect. 4.3);

110

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

– Determination of the elementary wave pulse for the considered seismic section interval; – Well data binding to a seismic section reflectors; – The inversion of the wave field into thin-layer sections and cubes of the AI and RC; – Binding of thin-layer formations on sections and cubes of AI and RC to well data; – Detailed interpretation of thin-layer sections and AI and RC cubes (correlation of reflectors, layers, and tectonic faults); – Finding and inclusion of the low-frequency component of the AI in sections and cubes of the AI (if necessary); – Building a deep seismic model; – Construction of the structural framework for the target layers; – The calculation of the parameters of the wave field (WF), acoustic impedance (AI), and reflection coefficients (RC) and their classification; – Tying of the parameters for the WF, AI, and RC parameters to the well data and interpretation of seismic facies; – Preparation of wells, sections, and cubes of the AI and RC for predicting geological and geophysical parameters; – Prediction of geological and geophysical parameters by area (type of lithology; reservoir properties; fluid saturation analysis; parameters of FCP, OWC, GWC, and GOC; etc.); – Construction and analysis of maps of prediction parameters, clay contours, OWC, etc. The results of applying the described technology of HRS-Geo are given below in Sect. 4.2 on test and real materials and in Chaps. 8 and 9.

4.2

Examples of Solving the Inverse Dynamic Problem on Test and Real Data

The high-resolution seismic technology HRS-Geo, as noted above, was developed to solve petroleum geology problems—extracting information about detailed thin-layer structure of the real environment from the dynamics of seismic recording, quantifying the composition and properties of porous facies and detecting on this basis possible hydrocarbon traps of various geneses and sizes. In this subsection, the correctness of the chosen approach to the solution of the inverse dynamic seismic problem (IDSP) is evaluated, and the reliability and credibility of the medium acoustic model restoration results are determined.

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

4.2.1

111

Model Study of the Trace Inversion Algorithm

To test the trace inversion algorithm, the following task was set: (1) the field model of longitudinal P waves on the basis of acoustic measurements in the well is formed; (2) the field of transverse S waves is absent; (3) the seismic impulse characteristics probing medium are not known; (4) information about the nature and type of the medium model is not known (the stratification, the morphology of the reflectors, the presence of geological objects in the section, the properties of reservoirs and sealing formation, etc. are not known); (5) the presence or absence of random noise and the background of regular (multiple) waves is not known; (6) effects associated with wave divergence, absorption, and dispersion are absent; and (7) a priori constraints are given in the form: a well model is known in a certain limited part of the medium (section). It is required (1) to find a probe wave impulse; (2) to determine the initial distribution of model acoustic impedance with maximum detail; and (3) to investigate the noise immunity of the medium recovery algorithm. The synthetic wave field was previously constructed without the participation of the authors in this process, being one of the conditions for evaluating the operation of the wave field inversion algorithm at CGE [2]. In particular, it was necessary to evaluate the possibilities of this algorithm for reconstructing an acoustic model with previously unknown characteristics: a medium model and a seismic wave pulse “probing” this test medium model. About the initial model of the medium, it was known only that for its construction acoustic measurements were used in a real well located in one of the oil and gas areas of Western Siberia. The distribution of acoustic velocities VSN in the depth interval 2100–2480 m was used as the input data, the values of which were transformed into a thin-layer equitime VSNR model with an approximation step of 2 ms (Fig. 4.4). Laterally from the well location, the velocity model was formed by changing the velocity values of thin-layer interlayers having a thickness proportional to a time step of 2 ms (this time interval is known to be most common in seismic sampling practice over the time). The model of the wave field generated in this way (as a result of the seismic pulse and the acoustic model convolution) together with the amplitude-frequency spectrum are presented in Fig. 4.5. Such a model of the wave field was the input (source) for the algorithm for restoring the environment model, and the main result of the experiment should be a reconstructed acoustic model, which, when properly solved the task, is adequate to a 2D original (previously unknown) equitime acoustic model. A preliminary qualitative visual analytical description of the wave field in the form of a time section showed in general that the wave field (formed from 51 synthetic traces) is symmetrical about the central point (central vertical section of the field) (Fig. 4.5a). Reflections, which represent synclinal in its upper part and anticlinal structures in its lower part, are confidently distinguished in it. Within these structures, there are also reflections with varying interference amplitudes, which characterize some features of the internal structure of the section.

112

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.4 An element of acoustic data used to form a 2D acoustic model: sonic velocity curve along the well (VSN); equitime approximation of sonic velocity model (VSNR)

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

113

Fig. 4.5 Wave field model to test the procedure for restoring the acoustic model of the section: (a) synthetic wave field, (b) amplitude-frequency characteristics of synthetic wave field traces

The analysis of the synthetic wave field shows that horizontal reflections at times of 560 and 1000 ms with unchanged visual amplitudes above and below the structures are observed. In the middle part of the section in the time interval of 730–830 ms, a series of horizontal reflectors is observed, which most likely characterizes the model of the medium with horizontal stratification of the expected deposits. Below the upper structure at a time of 660 ms in the vicinity of the point pk25, you can see an anomaly with increased amplitudes, interpreted as a “bright spot” effect (Fig. 4.5a). To quantify the wave field, its spectral characteristic and autocorrelation function were calculated. The amplitude spectra of the presented wave field traces show the frequency distribution of the reflection energy (Fig. 4.5b). From this figure, in particular, it is clear that the frequency band representing the reflected waves is in the range of 2–80 Hz. The maximum amplitudes of the spectral components fall

114

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

within the frequency range of 20–55 Hz. From about 60 Hz and above, there is a sharp weakening of the spectrum components. It is possible to make a preliminary conclusion about the frequency composition of the seismic pulse used in the simulation. Presumably, its frequency band should cover the interval of 0–70 Hz with an approximate maximum at a frequency of 35 Hz. Further, in the process of solving the inverse dynamic problem, the wave pulses were extracted from the time section traces using the autocorrelation function under the assumption that there is no noise in the original seismic records. The amplitude spectrum for each of the traces was determined from the autocorrelation function (Fig. 4.6b). The initial waveform was taken to the minimum phase. Then, taking into account the presence in the section of the data fragment for the well (Fig. 4.4), the correction to the phase characteristic of the minimum phase signal was calculated. Thus, the shape of the seismic signal for each trace was sequentially refined. The extracted wave pulses “probing” the synthetic model of a real medium for each of the traces are shown in Fig. 4.6a. From the amplitude spectrum of the pulse, it can be seen that the frequency band is in the range from 0 to ~100 Hz, with the main signal energy concentrated in the interval of 0–60 Hz (Fig. 4.6b). The signal contains a constant component, since the beginning of the spectrum has a nonzero value. In its form, the specified signal turned out to be quite complex, close to the

Fig. 4.6 Form of the seismic impulse as a result of the solution the inverse dynamic seismic problem: (a) elementary seismic impulses for each trace, (b) amplitude-frequency characteristics of seismic impulses

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

115

maximum phase one. The oscillation energy of such a signal increases with increasing time, and the instantaneous frequency of the signal decreases in time (i.e., when solving a direct problem, a signal with spectral components changing in the frontal and rear parts was used), which testifies first of all to the difficult experimental conditions. As a result of solving the inverse dynamic problem, the desired acoustic model was obtained throughout the section (Fig. 4.7a). It, with all the details and features of the section structure up to a discretization step of 2 ms, fully complies with the original acoustic model, which was formed for a numerical experiment and is a thinlayer medium. It should be noted that in the recovery process, nonrigid regularization parameters were used, and the desired model was restored in a stable way, i.e., this indicates that the wave field modeling was carried out without taking into consideration random noise or their level was not significant for the field inversion system. At the input time section (Fig. 4.5a), as noted above, in analyzing the wave field, the synclinal and anticlinal structures emerged. However, the internal acoustic heterogeneity of these structures, obtained from a preliminary analysis of the wave field, was not confirmed—the structures turned out to be almost uniform with the values of the acoustic impedance varying linearly from trace to trace. It can be seen from the figure that the “bright spot” effect is not due to a change in the acoustic impedance of a substance inside the synclinal structure, but to a change in the impedance of thin layers at 650 ms, located below the structure (Fig. 4.7a). Such structural features of the wave pattern in this case are due to interference effects of the wave field, and their interpretation only within the original wave field (Fig. 4.5a) leads to ambiguous and inadequate results. The amplitude spectrum of the reconstructed model covers the entire frequency range up to 100 Hz, which is a confirmation of the model’s thin-layer nature (Fig. 4.7b). One of the important aspects in testing the system performance is to check the acoustic model restoration in the presence of noise. For this kind of test, random noise was generated programmatically with a broadband Gaussian distribution with a sufficiently strong intensity reaching the seismic signal level in places. The content of the noise used in the time and frequency domains is shown in Fig. 4.8a–b. It is present on almost all amplitude samples of traces (Fig. 4.8a). The amplitude spectrum of the noise covers a wide range of frequencies (Fig. 4.8b). This noise is additive to the time section (Fig. 4.5a); as a result, a complicated wave field was obtained (Fig. 4.9a) with a strong distortion of the signal. The traces are a discontinuous function with breaks (breaks of the first and second types) (Fig. 4.9a). The use of the above methods of impulse extraction give unstable and generally distorted results (Fig. 4.9b–c). The effect of strong random noise has a rather significant destructive effect on both the extracted form of the seismic signal and the desired result—the model of acoustic impedance. However, the most general kinematic and dynamic regularities of the restored model still remain (this indicates the noise immunity of the developed recovery algorithm). At the same time, it can be seen that in some cases, the signal-to-noise ratio was rather unfavorable for recovery, resulting in a sharp decrease in the intensity of the required acoustic impedance on

116

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.7 The results of inverse dynamic problem solution: (a) the sought acoustic model, recovered from the synthetic wave field, presented in Fig. 4.5a, (b) amplitude-frequency characteristics of the restored acoustic model traces

certain traces. On separate traces (with numbers 2, 8, 25, 27, 47) the restoration was almost impossible—here the stability of the solution is lost, and on other vertical sections, the resulting trace is distorted only partially (numbers 4, 6, 9, 42, 50) (Fig. 4.9c). To reduce the adverse effect of strong random noise on the desired result of the inversion to the seismic wave field traces (Fig. 4.9a), the standard procedure of optimal filtering was applied. As a result, the original model of the seismic wave field with superimposed strong random noise was obtained but, to a certain extent, regularized by optimal filtering (Fig. 4.10a). As a result of the IDSP solution, “probing” seismic signals (Fig. 4.10b) was predetermined for each trace, and a reconstructed model of acoustic impedance was obtained (Fig. 4.10c). These results were quite stable. At the same time, the

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

117

Fig. 4.8 Distribution of random noise generated before entering into the model of the initial wave field: (a) random noise with a Gaussian distribution function, (b) amplitude-frequency characteristics of random noise

resolution (vertical and horizontal) of the acoustic model has slightly deteriorated compared to the result of the inversion without noise, which is explained by the presence of some residual background noise, which still distorts the original wave field. However, the found acoustic model of the medium as a whole retains a thinlayer structure, and almost all geological elements are confidently identified and in a sense can be satisfactorily interpreted. Thus, according to the results of the experiment, detailed seismic modeling using well data, testing the procedure for inverting frequency-limited records of reflected waves, and restoring on this basis a detailed model of the acoustic impedance structure, we can draw the following conclusions.

118

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.9 The result of acoustic model restoration with the random noise introduced into the wave field model: (a) the model of the synthetic wave field with random noise, (b) elementary seismic impulses as a result of the IDSP solution, (c) the restored sought acoustic model

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

119

Fig. 4.10 The results of acoustic model restoration with the random noise introduced into the wave field model and with the prior application the optimal filtration procedure for random noise suppression: (a) the model of the synthetic wave field with random noise after the optimal filtration procedure, (b) elementary seismic impulses as a result of the IDSP solution, (c) the restored acoustic sought model

120

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

1. The procedure developed by the authors for solving the inverse dynamic seismic problem makes it possible to reliably reconstruct the section model of effective RC and AI with the accuracy of the seismic sampling step over time. 2. The results of the AI model restoration can be influenced by strong noise (commensurate in intensity with useful reflected waves); their impact on the desired results can be significantly reduced by using additional procedures for processing the original seismic records (e.g., optimal filtering procedures). In general, the process of restoring the subsurface medium model is characterized by a fairly high noise immunity. 3. Obviously, at the stage of preliminary seismic data processing, it is necessary to implement a special seismic data processing graph, which, on the one hand, provides an increase in the signal-to-noise ratio (it is quite possible to do this within the traditional standard processing graph) and, on the other hand, minimally distorts seismic dynamics, i.e., to the maximum extent, it stores identification information on geological indicators contained in the seismic data of the field survey.

4.2.2

Application of the Inversion Algorithm for the Real Seismic Data

In addition to testing the algorithm of seismic data inversion on a synthetic model, to assess the reliability of the reconstruction algorithm, studies were carried out on real materials previously obtained by different companies using their own seismic data preprocessing graphs in each of them. At the same time, the field observation materials for all participants of the experiment were the same. Materials used were the CDP and GBS data on one of the seismic profiles, worked out within the Krasnoleninsk arch of Western Siberia. First, we briefly discuss some of the seismic method application features when performing a comparative analysis of the various processing results obtained through the use of different processing graphs used in different companies and their subsequent inversion to the acoustic model of the real subsurface medium obtained using HRS-Geo technology procedures. The seismic method of subsurface exploration provides for multistage work, starting with the design of field survey to the interpretation of the digital data processing results, followed by the definition—the calculation of hydrocarbon resources or reserves. In essence, the IDSP solution is a key step directly related to obtaining a real distribution of the medium elastic characteristics, on the basis of which various geological indicators are predicted within the target objects under study. The successful solution of the inverse dynamic problem requires the correct execution of all digital procedures at each of the processing stages with the solution of the main task—to minimize irreversible losses of identification information about the medium properties (its detail and reliability) with various transformations of the original, intermediate, and final seismic records.

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

121

Preprocessing always precedes the solution of the inverse problem, and the IDSP solution result depends largely on the preprocessing quality. It is known that the processing, as well as the interpretation result, is ambiguous. This is explained by the fact that the applied data processing graph can be combined in different ways (using different procedures, in a slightly different sequence of their execution and combinations, associated in one way or another with certain research objects in specific geological conditions). Parameters that are used in processing procedures have options (choices). In addition, it is often a priori necessary to pre-define unknown models (or to have some idea of their geological structure in advance), within which certain transformations of seismic data will be carried out most efficiently. At the same time, it turns out that as a result of applying different graphs and preprocessing stages, one has to deal with the source data of different optimality for solving the IDSP. As one example of such a situation, you can refer to the materials placed in one of the articles where, when considering the topic of processing and reprocessing seismic data, the issue of applying new software and algorithmic approaches in the preprocessing is discussed [26]. As a reliable and most credible method for evaluating the data processing efficiency, the inversion technology of wave fields with the direct extraction of geological information about the environment is used. In particular, the authors of this article point out that “. . .repeated processing of seismic data is not a formal procedure and the closest attention should be paid to the choice of the graph. It is necessary to analyze the results of previous works, to identify their shortcomings and to formulate requirements for new output data. In the final formation of the optimal graph of works, a technical competition among various performers can help. . .”. Inversion with the use of HRS-Geo technology programs allows to assess the quality of the previous work performed on the data recovery results and iteratively select an efficient processing graph, determine its most significant stages, and establish the most optimal parameters set of its component procedures. Below are the results of an application comparative analysis of the procedure for inverting to the medium acoustic models of seismic wave fields, independently obtained in computer centers of various leading industrial and scientific organizations. The quality of digital data processing are analyzed, and conclusions in details are made. A total of 12 variants of the data preprocessing results from the same seismic survey profile were tested. These results to qualitative (visual) and quantitative assessments were subjected. The evaluation criteria were the kinematic and dynamic characteristics (features) of seismic records and traces of acoustic impedance and reflectivity. Particular attention to the detail of the results obtained and the reaction of such transformations to the presence of interference was paid. The potential of each processing version and the results of subsequent inversion were also evaluated. It is noted that the quantitative assessment of the seismic data transformation results is ambiguous. However, there are several approaches to this problem, and some results (e.g., for detailed RC sections) require the development of effective (or improvement of those existing in practice) methods of quantitative estimates.

122

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

The detail of the method is determined by what sizes and at what distances two or more heterogeneities with the same properties in the field under study (wave, RC, AI, etc.) can be considered as separate objects. At the same time, the detail of the seismic method depends on many factors: the nature of the medium acoustic heterogeneities, the lithological composition of the formations, the tectonic conditions, the applied field observation system, the noise type and its level, the graph and processing algorithms, the interpretation technology, subjective factors (depending on the interpreters experience), etc. Seismic shooting detail in space depends on the study direction (in different directions, it is different—detail anisotropy). Distinguishing the detail in the vertical and horizontal directions, respectively, quantitatively, it is measured by vertical and horizontal resolutions. Seismic waves are almost always complicated by various types of noise. The ability to detect useful waves against noise background is determined by the amplitude resolution, i.e., it depends on the ratio of the useful wave and noise energy. As is well known, the quantitative criterion of amplitude resolution is the signal-to-noise (S/N) ratio, i.e., how much the signal exceeds the noise level. Usually, the noise level is reduced either by suppression in the frequency domain, or by processing multichannel filters by mixing data. This leads to the stretching of the useful signal in space (additional interference is introduced), and this in turn reduces the spatial resolution. This is where the conflicting nature of the criteria appears. The optimal graph is to choose a compromise between the amplitude and spatial resolutions, since an increase in amplitude resolution leads to a decrease in vertical resolution and vice versa. Quantitatively, the detail of the initial data and the results of the inversion was estimated using the calculation of vertical and horizontal resolutions. Vertical resolution was estimated by analyzing the behavior of the autocorrelation function (ACF). In the specified trace time window, the normalized autocorrelation function is calculated. Based on the ACF property, the vertical resolution is calculated using the formula [27]: m1 P

f R ðk Þ ¼

1 T ACF



ACF i

i¼0 m4 P

,

ð4:1Þ

ACF i

i¼m1

where fR(k) is the vertical resolution of the k-th trace in Hz; TACF is the width of the main half cycle ACF in s; m1 is the number of discretes in the main half period of ACF; m4 is the number of samples defining the 4TACF interval; and ACFi is the i-th value of the normalized autocorrelation function in the seismic trace window, calculated as: ACF i ¼

X S jS j

ji =

X S2j , j

where Sj is the trace amplitude at the j-th discrete.

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

123

In the interval of traces, the mean value of resolution is calculated: fR ¼

N X

f R ðk Þ=N,

k¼1

where N is the number of traces. The horizontal resolution of seismic data is calculated using various approaches [28, 29]. The method for calculating the resolution in the horizontal direction using the theory of Fresnel zones is given in [28]. In this case, the wave front is divided into annular surface areas (Fresnel zones), the dimensions of which are determined by the distance from the source and the length of the generated wave. Areas with distance increase due to the surface divergence of the wave front. It is noted that the contribution of reflected waves from surface area heterogeneities corresponding to the first Fresnel zone is the largest at the observation point (the influence within this zone is also different). From the other zones, the influence is much smaller due to the multi-phase summation at the observation point. The radius of the first Fresnel zone [28] is taken as a measure of horizontal resolution: RF1 

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi 0:5  λ  h0 ¼ 0:5  V  t=f ,

ð4:2Þ

where RF1 is the first Fresnel zone radius in m; h0 is the depth to the reflecting horizon in m; λ is the wavelength in m; V is the average velocity to the reflecting boundary in m/s; t is the time of the wave path to the reflecting boundary; f is the frequency in Hz. In [29], several methods are presented for the practical assessment of lateral resolution in planning the parameters of 3D seismic surveys. Some of them were used to calculate horizontal resolution. It is proposed to estimate the calculation of lateral resolution via vertical one using the expression [30]: RH ¼ RV = sin ðθÞ,

ð4:3Þ

where RH is the horizontal resolution in m; RV is the vertical resolution in m; θ is the maximum radial angle used in the migrrepresented by a convolutionation procedure. The dependence of the horizontal resolution on the wave velocity, the maximum frequency, and the inclination angle of the boundary is estimated in [31] as: dX ¼

0:3  V , f max  sin ðθÞ

ð4:4Þ

where dX is the horizontal resolution in m; V is the velocity in m/s; fmax is the maximum frequency in the spectrum; θ is the angle of inclination of the boundary.

124

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Klaerbout J. F. suggests calculating the resolution as half of the effective wavelength RL  λ/2 [32]. Horizontal and vertical resolution evaluation through wave velocity and bandwidth is also given in [33]: RL ¼ RV ¼

V , 2B

ð4:5Þ

where RL is the horizontal resolution in m; RV is the vertical resolution in m; V is the average velocity to the reflecting horizon in m/s; B is the bandwidth in Hz. The lateral resolution can also be estimated through the wavelength [33]: RL ¼

λ min , 4  sin ðθÞ

ð4:6Þ

where RL is the horizontal resolution in m; λ is the minimum wavelength in m; θ is the arctan (L/2Z); L/2 is the half the length of the line in m; Z is the target depth in m. In formulas (4.3), (4.4), (4.5), and (4.6) the resolutions RL , dX, RV correspond to the calculated values. They are achievable with adequate measurement parameters of recording equipment and parameters of seismic receivers (geophones) spread and are used in the design of field systems for 2D/3D observations. Obviously, for planning observation systems for seismic surveys, one should focus not only on the characteristics of the recorded wave fields but also on the parameters of the data obtained as a result of the preprocessing of field materials and, especially, on the characteristics of the solution to the inverse dynamic seismic problem, since in the process of implementing the IDSP on the AI and RC sections, it is possible to significantly (several times) improve the amplitude and spatial resolutions of the seismic records and the detail of the displayed geological bodies. In particular, to more accurately determine the horizons of target geological objects and small-sized oil and gas deposits, it is advisable to switch to a smaller time discretization step and to a smaller interval between CDP traces for 2D surveys and a smaller bin size for 3D surveys. For the original wave field, the traces of which represent the graphs of a smooth (continuous) function, the signal-to-noise ratio is usually calculated based on a comparison of the model trace maximum amplitudes with the residual trace maximum amplitude between the model and estimated. For each point, the model trace is formed by stacking the correlated adjacent traces (summation based on the analysis of the cross-correlation function (CCF)). In this way, the correlated recording component is separated from the random component (i.e., signal from noise). For the reflectivity trace, free from the impulse that probe the medium, such an approach to estimating the signal-to-noise ratio is not efficient enough in practice and can lead to significant errors. This is due to two reasons. The seismic signal correlation in a seismic section is created by a probe impulse and the reflectivity distribution in space (vertically and laterally). After the inversion of the wave field in the reflectivity section, there is no seismic pulse, which, due to its energy in the wave

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

125

field, significantly increases the correlation (acts as an energy-enhanced marker of the useful signal). The reflection coefficients are almost not correlated in the vertical component. They are often considered as the realization of a random process, since they acquire statistical properties, especially with the distribution of small values (hypothesis of reflectivity randomness) [34]. In such conditions, there are also some difficulties with the estimation of the signal correlation in the lateral direction. As a result, the correlation of the signal decreases, and, therefore, there is a sharp decrease in the absolute value of the S/N ratio calculated by the usual method. However, this does not mean that noise has dramatically increased in the distribution of RC values. This means that we extracted only a less correlated useful component from the wave field—the distribution of the reflectivities, whose characteristics are close to statistical, and the traditional S/N estimation algorithms consider the resulting RC values as uncorrelated signal, i.e., as noise. In this regard, traditional methods of the signalto-noise ratio estimating do not allow stably obtaining reliable signal/noise values for the reflectivity distribution [35]. To calculate the signal-to-noise ratio of a reflectivity thin-layer section representing graphs of discontinuous functions, the authors developed and applied a method for estimating the signal-to-noise ratio based on the change in signal energy in the power spectrum and the value of the regularization parameters when solving the IDSP with respect to the S/N value of the initial field. The signal-to-noise ratio is estimated to section the reflectivity distribution of the tested seismic data processing versions using the following formula:   ΔE WR RSNR ¼ RSNW  1 þ  nðαÞ, ER

ð4:7Þ

where RSNR is the signal-to-noise ratio in the trace of the reflection coefficients; RSNW is the signal-to-noise ratio in the trace of the original wave field; ΔEWR ¼ ER – EW is the change in the energy of the useful signal when converting the wave field into the reflectivity one; ЕW is the signal energy in the wave field; ЕR is the signal energy in the reflectivity; n(α) is the coefficient determining the change in the noise level during extraction reflectivity of the wave field, depending on the noise level in source data and being a function of the regularization parameters α at solving an inverse dynamic problem. When comparing 12 different processing and inverting seismic data variants, a section of the wave field obtained during special processing using HRS-Geo technology and the results of its inversion was taken as a reference. This is due to the fact that the full range of such work on processing, interpreting, and predicting geological and geophysical parameters of objects using the entire set of available seismic (profiles) and well data is carried out on the area of the considered seismic line location, and a geological model is known that is close to real. There are three wells on the profile line. To compare the quality of the kinematic and static corrections, the boundaries of the Bazhenov formation deposits (top) were traced across all the versions, which are summarized in one graph (Fig. 4.11). It is seen that only in the

126

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.11 Kinematic and static corrections quality comparison for different data processing versions, performed in different processing centers, based on tracing the top of Bazhenov suite deposits, imposed to one graph

vertical section of the well section where there is a minimal variation. Outside the well, processing errors increase in proportion to the distance from the well. The red horizon on the graph is the reference based on the results of the HRS-Geo technology. All other horizon versions oscillate with respect to it, creating processing errors (which is primarily responsible for the kinematic of the processing). For a comparative analysis of the informativity and detailed assessment for all 12 versions in the correlating time window for the deposits of the Jurassic complex, the energy spectra of the initial data (Fig. 4.12a), the inversion results (Fig. 4.12b, c), the vertical and horizontal resolutions, and a number of other parameters, are summarized in Tables 4.1–4.5. Next, we present the results of the analysis for each of the preprocessing versions and the subsequent restoration of the real subsurface environment model in the form of RC and AI sections and their corresponding estimates.

4.2.3

Version 1: Special Processing Using HRS-Geo Technology

As a result of special processing, a time section was obtained—the initial wave field (WF) for the inversion (Fig. 4.13a), and as a result of the WF traces inversion, sections of the relative acoustic impedance (AI) and reflection coefficients (RC) were obtained (Fig. 4.13b, c). Let us consider the main estimated characteristics (according to a certain formalized scheme) for productive and potentially productive sediments of the Jurassic

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

127

Fig. 4.12 Energy (power) spectra of the Jurassic complex section fragment for the 12 processing versions in comparison with the of reference processing version by the HRS-Geo technology: (a) spectra of source (input) wave field, (b) spectra of the inversion results—sections of the reflection coefficients

128

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.12 (continued)

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

Fig. 4.12 (continued)

129

Description Input seismic data processing version Signal energy of input data (WF traces) Signal energy of output data (RC traces) Ratio of signal energies (informative variation) Input data (WF) vertical resolution at frequency Output data (RC-inversion results) vertical resolution at frequency Input data (WF) vertical resolution along the time coordinate Output data (RC) vertical resolution along the time coordinate Input data (WF) vertical resolution along the depth coordinate Output data (RC) vertical resolution along the depth coordinate Input to output vertical resolution ratio (WF to RC)

Vertical resolution fRw, Hz fRr, Hz 38.9 241.5 28.9 215.8 27.2 100.1 34.8 196.7 28.8 145.4 36.7 217.4 40.7 195.3 33.4 127.5 37.6 170.7 34.3 129.9 25.5 173.6 39.0 205.6

Index Version no Psw, c.u. Psr, c.u. dSinf, % fRw, Hz fRr, Hz ΔtRw, ms ΔtRr, ms hRw, m hRr, m hRw/r

(ΔPs/Psr)100 dSinf, % 81.3 79.5 76.5 70.7 71.9 71.0 81.6 76.6 72.2 70.5 78.8 81.7

Service center HRS-geo Gdata JDow KMGph MU Prdgm PtrAlnce SvMGph SbNGph Svginf TNGph YtGph

Version no. 1 2 3 4 5 6 7 8 9 10 11 12

Signal energy Psw, c.u. Psr, c.u. 28.09 149.96 24.41 118.84 18.54 79.04 18.12 61.75 26.14 93.13 20.06 69.09 26.97 146.87 17.67 75.61 20.81 74.95 24.76 84.02 11.79 55.48 27.09 148.16

Table 4.1 Seismic data vertical resolution and informativity ΔtRw, ms 12.9 17.3 18.4 14.3 17.4 13.6 12.3 15.0 13.3 14.6 19.6 12.8

ΔtRr, ms 2.1 2.3 5.0 2.5 3.4 2.3 2.6 3.9 2.9 3.8 2.9 2.4 hRw, m 14.8 19.9 21.2 16.5 20.0 15.6 14.1 17.2 15.3 16.8 22.5 14.7

hRr, m 2.4 2.7 5.7 2.9 4 2.6 2.9 4.5 3.4 4.4 3.3 2.8

hRw/r ¼ hRw/hRr 6.2 7.4 3.7 5.7 5.0 6.0 4.9 3.8 4.5 3.8 6.8 5.3

130 4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Index Version no RSNW RSNRi RSNR RF1W, m RF1R, m Xw, m Xr, m Rhw, m Rhr, m RF1W/R Xw/r Rhw/r

Version no 1 2 3 4 5 6 7 8 9 10 11 12

RSNW 5.25 2.23 5.28 5.49 5.40 3.27 3.74 4.82 4.66 4.30 5.15 5.20

RSNRi 1.34 0.90 1.30 1.47 1.38 1.23 0.87 1.45 1.44 1.41 1.45 1.34

S/N ratio

RSNR 9.04 3.79 8.86 8.90 8.82 5.30 4.75 8.09 7.62 6.97 8.74 8.99

Lateral resolution RF1W, RF1R, m m 193.0 78.0 224.0 82.0 231.0 121.0 204.0 86.0 225.0 100.0 199.0 82.0 189.0 86.0 209.0 107.0 197.0 92.0 206.0 106.0 239.0 92.0 193.0 84.0 Xw, m 25.4 34.1 36.3 28.3 34.2 26.8 24.2 29.5 26.2 28.7 38.7 25.3

Xr, m 4.1 4.6 9.8 5.0 6.8 4.5 5.0 7.7 5.8 7.6 5.7 4.8

Description Input seismic data processing version Input data signal-to-noise ratio (WF traces) Output data signal-to-noise ratio (RC traces) Output data signal-to-noise ratio (RC traces, using formula (4.7)) Input data (WF) lateral resolution (radius of the first Fresnel zone) Output data (RC) lateral resolution (radius of the first Fresnel zone) Input data (WF) lateral resolution (using formula (4.4)) Output data (RC-inversion results) lateral resolution (formula (4.4)) Input data (WF) lateral resolution (using formula (4.3)) Output data (RC-inversion results) lateral resolution (formula (4.3)) Input to output lateral resolution ratio (radii of the first Fresnel zone) Input (WF) to output (RC) lateral resolution ratio (formula (4.4)) Input (WF) to output (RC) lateral resolution ratio (formula (4.3))

Service center HRS-geo Gdata JDow KMGph MU Prdgm PtrAlnce SvMGph SbNGph Svginf TNGph YtGph

Table 4.2 Seismic data lateral resolution and signal-to-noise ratio Rhw, m 21.1 28.4 30.2 23.6 28.5 22.4 20.2 24.6 21.8 23.9 32.2 21.0

Rhr, m 3.4 3.8 8.2 4.2 5.6 3.8 4.2 6.4 4.8 6.3 4.7 4.0

RF1W/R ¼ RF1W/ RF1R 2.5 2.7 1.9 2.4 2.3 2.4 2.2 2.0 2.1 1.9 2.6 2.3

Xw/r ¼ Xw/ Xr 6.2 7.4 3.7 5.7 5.0 6.0 4.8 3.8 4.5 3.8 6.8 5.3

Rhw/r ¼ Rhw/ Rhr 6.2 7.5 3.7 5.6 5.1 5.9 4.8 3.8 4.5 3.8 6.9 5.3

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data 131

150.0 118.8 79.0 61.8 93.1 69.1 146.9 75.6 75.0 84.0 55.5 148.16

100.0 79.2 52.7 41.2 62.1 46.1 97.9 50.4 50.0 56.0 37.0 98.8

dSinfr1, % 5.25 2.23 5.28 5.49 5.40 3.27 3.74 4.82 4.66 4.30 5.15 5.20

RSNW 100.0 42.5 100.6 104.6 102.9 62.3 71.2 91.8 88.8 81.9 98.1 99.0

dRSNW1, % 9.04 3.79 8.86 8.90 8.82 5.30 4.75 8.09 7.62 6.97 8.74 8.99

RSNR 100.0 41.9 98.0 98.5 97.6 58.6 52.6 89.5 84.3 77.1 96.7 99.4

dRSNR1, % 14.8 19.9 21.2 16.5 20.0 15.6 14.1 17.2 15.3 16.8 22.5 14.7

hRw, m 100.0 74.4 69.8 89.7 74.0 94.9 105.0 86.0 96.7 88.1 65.8 100.7

dhRw1, % 2.4 2.7 5.7 2.9 4.0 2.6 2.9 4.5 3.4 4.4 3.3 2.8

hRr, m 100.0 88.9 42.1 82.8 60.0 92.3 82.8 53.3 70.6 54.5 72.7 85.7

dhRr1, % 21.1 28.4 30.2 23.6 28.5 22.4 20.2 24.6 21.8 23.9 32.2 21.0

Rhw, m

Input seismic data processing version Signal energy of input data (WF); input data (WF) energy informative with respect to processing version 1 Signal energy of output data (RC); output data (RC-inversion results) energy informative with respect to processing version 1 Input data (WF) signal-to-noise ratio; input data (WF) signal-to-noise variation in relation to processing version 1 Output data (RC-inversion) signal-to-noise ratio; output data (RC) signal-to-noise variation in relation to processing version 1 Input data (WF) vertical resolution; input data (WF) vertical resolution variation in relation to processing version 1 Output data (RC) vertical resolution; output data (RC) vertical resolution variation in relation to processing version 1 Input data (WF) lateral resolution; input data (WF) lateral resolution variation in relation to processing version 1 Output data (RC) lateral resolution; output data (RC) lateral resolution variation in relation to processing version 1

100.0 86.9 66.0 64.5 93.1 71.4 96.0 62.9 74.1 88.1 42.0 92.9

Psr, c. u.

Description

28.1 24.4 18.5 18.1 26.1 20.1 27.0 17.7 20.8 24.8 11.8 26.1

dSinfw1, %

Version No Psw; dSinfw1 Psr; dSinfr1 RSNW; dRSNW1 RSNR; dRSNR1 hRw; dhRw1 hRr; dhRr1 Rhw; dRhw1 Rhr; dRhr1

HRS-geo Gdata JDow KMGph MU Prdgm PtrAlnce SvMGph SbNGph Svginf TNGph YtGph

1 2 3 4 5 6 7 8 9 10 11 12

Psw, c.u.

Index

Processing center

Version No.

Table 4.3 Signal-to-noise ratio and resolution and informativity comparison of the processing variants

100.0 74.3 69.9 89.4 74.0 94.2 104.5 85.8 96.8 88.3 65.5 100.5

dRhw1, % 3.4 3.8 8.2 4.2 5.6 3.8 4.2 6.4 4.8 6.3 4.7 4.0

Rhr, m

100.0 89.5 41.5 81.0 60.7 89.5 81.0 53.1 70.8 54.0 72.3 85.0

dRhr1, %

Gdata

JDow

KMGph

MU

Prdgm

PtrAlnce

SvMGph

SbNGph

Svginf

TNGph

YtGph

2

3

4

5

6

7

8

9

10

11

12

92.9

42.0

88.1

74.1

62.9

96.0

71.4

93.1

64.5

66.0

86.9

100.0

98.5 97.6

4.6 2.9

53.3

Output data signal-to-noise ratio (RC-inversion results) in relation to version 1; addition to the optimum

Input data vertical resolution (WF) in relation to version 1; addition to the optimum

Output data vertical resolution (RC-inversion results) in relation to version 1; addition to the optimum

Input data lateral resolution (WF) in relation to version 1; addition to the optimum

Output data lateral resolution (RC-inversion results) in relation to version 1; addition to the optimum

dhRr1; ΔhRr1

dRhw1; ΔRhw1

dRhr1; ΔRhr1

85.7

0.7

dhRw1; ΔhRw1

72.7

54.5

70.6

34.2

11.9

3.3

14.0

82.8

5.0

Input data signal-to-noise ratio (WF) in relation to version 1; addition to the optimum

100.7

65.8

88.1

96.7

86.0

105.0

92.3

60.0

82.8

42.1

88.9

100.0

dhRr1, %

5.1

26.0

10.3

30.2

25.6

0.0

ΔhRw1, %

dRSNR1; ΔRSNR1

3.1

3.3

22.9

15.7

10.5

47.4

94.9

74.0

89.7

69.8

74.4

100.0

dhRw1, %

Output data (RC-inversion results) energy informative with respect to processing version 1; addition to the optimum

96.9

96.7

77.1

84.3

89.5

52.6

41.4

2.4

1.5

2.0

58.1

0.0

ΔRSNR1, %

dRSNW1; ΔRSNW1

1.0

1.9

18.1

11.2

8.2

28.8

58.6

98.0

0.6

37.7

41.9

100.0

dRSNR1, %

57.5

0.0

ΔRSNW1, %

dSinfr1; ΔSinfr1

99.0

98.1

81.9

88.8

91.8

71.2

62.3

102.9

104.6

100.6

42.5

100.0

dRSNW1, %

Input seismic data processing version

1.2

63.0

44.0

50.0

49.6

2.1

53.9

37.9

58.8

47.3

20.8

0.0

ΔSinfr1, %

Input data (WF) energy informative with respect to processing version 1; addition to the optimum

98.8

37.0

56.0

50.0

50.4

97.9

46.1

62.1

41.2

52.7

79.2

100.0

dSinfr1, %

dSinfw1; ΔSinfw1

7.1

58.0

11.9

25.9

37.1

4.0

28.6

6.9

35.5

34.0

13.1

0.0

ΔSinfw1, %

Version no

Description

HRS-geo

1

dSinfw1, %

Index

Processing center

Version no

Table 4.4 Potential assessment of the various seismic data processing and data inversion versions

14.3

27.3

45.5

29.4

46.7

17.2

7.7

40.0

17.2

57.9

11.1

0.0

ΔhRr1, %

100.5

65.5

88.3

96.8

85.8

104.5

94.2

74.0

89.4

69.9

74.3

100.0

dRhw1, %

0.5

34.5

11.7

3.2

14.2

4.5

5.8

26.0

10.6

30.1

25.7

0.0

ΔRhw1, %

85.0

72.3

54.0

70.8

53.1

81.0

89.5

60.7

81.0

41.5

89.5

100.0

dRhr1, %

15.0

27.7

46.0

29.2

46.9

19.0

10.5

39.3

19.0

58.5

10.5

0.0

ΔRhr1, %

134

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Table 4.5 Seismic data processing and inversion efficiency Version no 1 2 3 4 5 6 7 8 9 10 11 12 Index Version no dEw1, % ΔEw1, % dEr1, % ΔEr1, %

Service center HRS-geo Gdata JDow KMGph MU Prdgm PtrAlnce SvMGph SbNGph Svginf TNGph YtGph

dEw1, % 100.0 69.5 76.6 87.0 86.0 80.7 94.2 81.6 89.1 86.6 67.8 98.3

ΔEw1, % 0.0 30.5 23.4 13.0 14.0 19.3 5.8 18.4 10.9 13.4 32.2 1.7

dEr1, % 100.0 74.9 58.6 75.8 70.1 71.6 78.6 61.6 68.9 60.4 69.7 91.6

ΔEr1, % 0.0 25.1 41.4 24.2 29.9 28.4 21.4 38.4 31.1 39.6 30.3 8.4

Description Input seismic data processing version The seismic data processing efficiency for the set of parameters: dSinfw1, dRSNW1, dhRw1, dRhw1 The potential opportunities to the optimum data processing for the set of parameters: ΔSinfw1, ΔRSNW1, ΔhRw1, ΔRhw1 The seismic data inversion efficiency for the set of parameters: dSinfr1, dRSNR1, dhRr1, dRhr1 The value of potential opportunities to the optimum data inversion for the set of parameters: ΔSinfr1, ΔRSNR1, ΔhRr1, ΔRhr1

complex based on the results of seismic data conversion in the form of WF, AI, and RC sections. In this case, the main characteristics are signal-to-noise ratio, vertical and horizontal resolutions, the power spectrum at the frequencies used, and the results of a comparative analysis of these characteristics. The reflection signals of the wave field original section are concentrated in the frequency band of 10–70 Hz (Fig. 4.12a; version 1), the signal energy in this band is Psw ¼ 28.09 c.u. (Table 4.1), the average energy value is E10–70 ¼ 0.4 c.u., the standard deviation is σ 10–70 ¼ 0.3 c.u., the signal-to-noise ratio is RSNW ¼ 5.25 (Table 4.2), the vertical resolution is ΔtRw ¼ 12.9 ms ( fRw ¼ 38.9 Hz in frequency units and hRw ¼ 14.8 m in depth) (Table 4.1), the first Fresnel zone radius according to formula (4.2) is RF1W ¼ 193.0 m, and the horizontal resolution according to expression (4.3) is Rhw ¼ 21.1 m (Table 4.2). The reflectivity section is characterized by the following quantitative parameters: the signal energy covers the frequency band of 10–250 Hz (Fig. 4.12b; version 1), the signal energy in this frequency band is Psr ¼ 149.96 c.u. (Table 4.1), signal-tonoise ratio through comparing model and difference traces (conventional method) is RSNRi ¼ 1.34 (Table 4.2), the signal-to-noise ratio using the proposed formula (4.7) is RSNR ¼ 9.04 (Table 4.2), the vertical resolution is ΔtRr ¼ 2.1 ms ( fRr ¼ 241.5 Hz and

Fig. 4.13 The results of special data processing using the HRS-Geo technology (version 1): (a) the input time section for the inversion of the wave field (WF); (b) the result of inversion, the section of acoustic impedance (AI); (c) the result of inversion, the section of the reflection coefficients (RC)

136

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.13 (continued)

hRr ¼ 2.4 m) (Table 4.1), the first Fresnel zone radius from expression (4.2) is RF1R ¼ 78.0 m (Table 4.2), and the horizontal resolution according to the formula (4.3) is equal to Rhr ¼ 3.4 m (Table 4.2). Such horizontal resolution is achievable when using the appropriate field observation system in the process of seismic measurements. In the power spectrum, the frequencies are divided into four ranges (sections): (1) 10–70 Hz, (2) 70–100 Hz, (3) 100–125 Hz, and (4) 125–250 Hz (Fig. 4.12b; version 1). The first part of the spectrum is characterized by the average value of Е1 ¼ 0.52 c.u., the amplitudes are variable and change from 0.5 to 1.0, and the standard deviation is σ 1 ¼ 0.22 c.u. At the second section of 70–100 Hz, a decrease in the energy of reflections is observed, and the amplitudes are almost constant and equal to Е2 ¼ 0.12 c.u., σ 2 ¼ 0.05 c.u. The section of 100–125 Hz is gradient: the amplitudes vary from 0.12 to 0.65 c.u., E3 ¼ 0.42 c.u., with a spread of σ 3 ¼ 0.13 c.u. The amplitudes of the fourth range (125–250 Hz) oscillate around the value of E4 ¼ 0.79 c.u. with a deviation of σ 4 ¼ 0.08 c.u. In the sections the signal energy is distributed evenly over the frequencies. The main part of the signal energy from the reflection coefficients is within the frequency bands of the first and fourth sections of

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

137

10–70 Hz and 125–250 Hz with energies of Psr ¼ 37.7 and Psr ¼ 98.2 c.u. (25.1% and 65.5%), respectively. Its characteristic is that 65% of the signal energy falls on the fourth section, which characterizes a thin-layer medium—this indicates the almost complete elimination of interference phenomena within the discretization step. Comparing the characteristics of the original wave field (WF) and reflectivity (RC) sections obtained after the inversion, you can see the information content of the dSinf signal energy increased to 81.3% Table 4.1), the vertical resolution hRr improved by hRw/r ¼ 6.2 times (from 14.8 m to 2.4 m) (Table 4.1), the lateral resolution of Rh improved in Rhw/r ¼ 6.2 times (from 21.1 m to 3.4 m) (Table 4.2), and the first Fresnel radius RF1 zone decreased in RF1W/R ¼ 2.5 times (i.e., from 193.0 m to 78.0 m) (Table 4.2). The wave field contains reflectors from all layers of the Jurassic complex and the surface of the pre-Jurassic basement with a fairly low noise level (Fig. 4.13a). The reconstructed model of the medium (the AI and RC sections) well reflects the geological features of the thin-layer structure with the accuracy of the seismic data sampling step Δt ¼ 2 ms (Fig. 4.13b, c). With the thin-layer nature of the sought model for the sections in the RC, the noises did not increase as compared with the input wave field (Fig. 4.13a); they practically do not create difficulties for the process of geological and geophysical interpretation of the model. At the same time, the internal structure of target objects can be traced quite confidently: the relationship between the constituent objects under study—layers and interlayers. According to drilling and well logging data in well 631, using its lithologic-stratigraphic column, the geological horizons of thin layers in the borehole were linked to the corresponding reflecting horizons of the AI and RC (Fig. 4.14a, b). In the process of interpreting the thin-layer horizons on the AI and RC sections along the lateral fairly confidently traced, almost all target objects of the Jurassic complex, starting from the top of the Bazhenov formation (suite) (Ю0) and ending with the boundary, are associated with the surface of the pre-Jurassic basement— crystalline basement (A) and tectonic faults. Figure 4.14a and b presents the results of the performed interpretation of the Jurassic sediment layers. Relationships of traps and seal formations for the reservoir have been determined. Morphological features of the structure of the Jurassic and pre-Jurassic basement sediments are revealed. Figure 4.15 shows an example of the internal structure of the reservoir on the acoustic impedance and reflectivity. In these sections, the structure adjacent to the tectonic fault in the sediments of the Ю6 reservoir stands out with confidence. This structure is interpreted as a trap for hydrocarbons, since it overlaps with a reliable clay-sealing formation with low AI values. In the internal structure of the object, there is a horizontal boundary (reflector), above which the values of the AI have smaller values than in the lower space of this interface (the oil and gas AI is smaller than the formation water AI). At this phase transition, a positive jump in acoustic impedance appears, which creates an insignificant reflection coefficient—and in the wave field, a weak reflected signal that is masked by interference effect of strong reflectors. Such a boundary in the RC section indicates the presence of hydrocarbon

138

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.14 Jurassic complex reflectors, tied to the geological horizons of the well section: (a) on the acoustic impedance (AI) section; (b) on the reflection coefficient (RC) section

signs. This boundary is interpreted as the boundary between hydrocarbons and formation water, i.e., as a water-oil contact (OWC). On the initial section of the WF, complicated by interference effects, such geological phenomena, are not recognized due to the low level of the reflected signal (Fig. 4.13a).

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

139

Fig. 4.15 An example of studying the reservoir internal structure on the fragmentsof reflection coefficient and acoustic impedance sections

The crystalline basement surface on the AI section is also more clearly distinguished than on the initial WF one. We emphasize once again that the results obtained for the first variant of processing and subsequent data conversion into the acoustic model are taken by the authors conventionally as reference ones (which are used for the subsequent comparative analysis of the results for all the seismic materials participating in the experiment).

4.2.4

Version 2: Gdata

The result of the second version of the seismic data preprocessing is presented in Fig. 4.16a. In the process of solving the IDSP on the basis of applying the HRS-Geo technology programs, the corresponding sections of the AI and RC were obtained (Fig. 4.16b, c).

140

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.16 The results of the second version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

141

Fig. 4.16 (continued)

In the wave field kinematics, there are significant deviations compared with version 1, which is clearly seen in the results of comparing the time lines t0(x) associated with the top of the Bazhenov formation deposits (Fig. 4.11). The maximum deviation of the reflection times along this reflector reaches 50 ms at the point farthest from the well (Fig. 4.11). In general, these materials indicate problems at the stage of seismic data processing, especially in the application of kinematic and static corrections, as a result of which significant errors may be made. The reflection signals of the original wave field are concentrated in the frequency band of 10–60 Hz (Fig. 4.12a; version 2), the signal energy in this band is Psw ¼ 24.41 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 2.23 (Table 4.2), and the vertical resolution is ΔtRw ¼ 17.3 ms (28.9 Hz and hRw ¼ 19.9 m) (Table 4.1). The first Fresnel zone radius is RF1W ¼ 224.0 m, and the horizontal resolution according to expression (4.3) is Rhw ¼ 28.4 m (Table 4.2). The reflectivity section is characterized by the following quantitative parameters: the signal energy covers the frequency band of 10–250 Hz (Fig. 4.12b; version 2), the signal energy in this band is Psr ¼ 118.84 c.u. (Table 4.1), the signal-to-noise ratio through comparing model and difference traces (conventional method) is RSNRi ¼ 0.90 (Table 4.2), the signal-to-noise ratio using the proposed formula

142

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

(4.7) is RSNR ¼ 3.79 (Table 4.2), the vertical resolution is ΔtRr ¼ 2.3 ms ( fRr ¼ 215.8 Hz and hRr ¼ 2.7 m) (Table 4.1), the first Fresnel zone radius according to (4.2) is RF1R ¼ 82.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 3.8 m (Table 4.2). Comparing the characteristics of the original WF section with the RC one after the inversion procedure, the following can be noticed: the informative value of the dSinf signal energy increased to 79.5%, the vertical resolution hRr improved in hRw/r ¼ 7.4 times (from 19.9 m to 2.7 m) (Table 4.1), the lateral resolution of Rhr also improved in Rhw/r ¼ 7.5 times (from 28.4 m to 3.8 m), and the first Fresnel zone radius RF1 decreased in RF1W/R ¼ 2.7 times (from 224.0 m to 82.0 m) (Table 4.2). In comparison with the results of the reference version 1, taken as 100%, the processing for the second version is marked by the following changes (Tables 4.3 and 4.4): (a) According to the original wave field (WF), the following changes are observed: the informativeness of the signal energy is dSinfw1 ¼ 86.9%, the signal-to-noise ratio is dRSNW1 ¼ 42.5%, the vertical resolution is dhRw1 ¼ 74.4%, and the horizontal resolution is dRhw1 ¼ 74.3% (Table 4.3). Obviously, for the second version, there is a significant potential for improving the quality of digital seismic data preprocessing, on the energy information content of the signal by ΔSinfw1 ¼ 13.1%; by the signal-to-noise ratio, ΔRSNW1 ¼ 57.5%; and by the vertical resolution, ΔhRw1 ¼ 25.6%, and the lateral resolution, ΔRhw1 ¼ 25.7% (Table 4.4); (b) Changes in the reflection coefficients (RC) have occurred: the informativeness in signal energy, which is dSinfr1 ¼ 79.2%; in the signal-to-noise ratio, dRSNR1 ¼ 41.9%; in the vertical resolution, dhRr1 ¼ 88.9%, in the horizontal resolution, dRhr ¼ 89.5% (Table 4.3). These differ from the corresponding estimates of case 1, whose values are taken as 100%. In general, this suggests that there is opportunities for improvement in model characteristics of inverted RC and AI sections (by increasing the efficiency of the second version of seismic data pre-processing): by the energy informativity of the signal ΔSinfr1 ¼ 20.8%, by the signal-to-noise ratio ΔRSNR1 ¼ 58.1%, by the vertical resolution ΔhRr1 ¼ 11.1%, and by lateral resolution ΔRhr1 ¼ 10.5% (Table 4.4). Complex characteristics, average values of processing efficiency (dEw1), inversion of seismic data (dEr1), and assessment of potential capabilities prior to optimal processing and solving the inverse seismic problem (ΔEw1, ΔEr1) using sets of estimated parameters with respect to case study 1, are given with their indices in Table 4.5. For the considered version 2, the efficiency of processing the wave field and inversion into the AI and RC data is as follows (Table 4.5): dEw1 ¼ 69.5% and dEr1 ¼ 74.9%, respectively. The potential possibility of increasing the information content of processing and data inversion is ΔEw1 ¼ 30.5% and ΔEr1 ¼ 25.1%, respectively. It should be noted that a significant noise level, preserved in the section of the original wave field after preprocessing, reduced the efficiency of the inversion results—no subtle features of the structure appear on the AI and RC data sections.

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

143

In addition, the structures in the time image as compared with case study 1 have a significant gradient of the fall of fold wings (Fig. 4.16a). It is also seen that the original wave field is complicated by random noise. On the section of acoustic impedance, the original noise appears to a significant extent (Fig. 4.16b). On the reflectivity, the noise is significantly weakened, but on the whole, the resolution of the obtained data is inferior to the first version—this is due to the use of more stringent parameters to ensure the stability of the solution (Fig. 4.16c). AI and RC sections can generally be considered suitable for standard interpretation. However, it should be noted that the signs associated with the trap discovery, the study of its internal structure, and the OWC in the dynamics in this case do not appear (Fig. 4.16b, c). The anticlinal structures along the section, based on the kinematic features of the structure, can be distinguished, but it is difficult to substantiate their prospects. As a result of this preprocessing, the information that was originally contained in the dynamics of the seismic record was largely irretrievably lost—the maximum amount of geological information contained in the original seismic record cannot be extracted.

4.2.5

Version 3: JDow

The result of the third preliminary data processing version for the profile under consideration is presented in Fig. 4.17a. The results of applying the HRS-Geo technology programs to solve the inverse problem to the original wave field are shown in Fig. 4.17b, c. The reflecting boundary (RB), traced from the top of the Bazhenov suite, deviated from the RB of the reference processing variant for 20 ms (Fig. 4.11), which indicates that the low-frequency component of the static corrections for the upper part of the section is completely correct. The study of the wave field dynamic characteristics shows the following. The energy of the wave field reflections is inside the frequency band of 10–58 Hz (Figs. 4.17, a and 4.12, a; version 3) and is quantitatively estimated as Psw ¼ 18.54 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 5.28 (Table 4.2), and the vertical resolution is ΔtRw ¼ 18.4 ms ( fRw ¼ 27.2 Hz and hRw ¼ 21.2 m) (Table 4.1; Fig. 4.17a). The radius of the first Fresnel zone is RF1W ¼ 231.0 m, and the horizontal resolution according to expression (4.3) is Rhw ¼ 30.2 m (Table 4.2). The dynamics of changes in the reflectivity is characterized by the following quantitative parameters: the signal energy covers the frequency band of 10–250 Hz (Figs. 4.17b and 4.12b; version 3), the signal energy in this band is Psr ¼ 79.04 c.u. (Table 4.1), the signal-to-noise ratio through comparison with the traditional method is RSNRi ¼ 1.3 (Table 4.2), the signal-to-noise ratio according to the proposed formula (4.7) is RSNR ¼ 8.86 (Table 4.2), and the vertical resolution is ΔtRr ¼ 5.0 ms ( fRr ¼ 100.1 Hz and hRr ¼ 5.7 m) (Table 4.1). The first Fresnel zone radius in accordance with (4.2) is RF1R ¼ 121.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 8.2 m (Table 4.2).

144

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.17 The results of the third version of seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

145

Fig. 4.17 (continued)

Comparing the characteristics of the initial WP and RC data after inversion, you can see that the information content of the signal energy Sinf increased to 76.5% and the vertical resolution of hRr improved in hRw/r ¼ 3.7 times (from 21.2 m to 5.7 m) (Table 4.1). The lateral resolution of Rhr is improved in Rhw/r ¼ 3.7 times (from 30.2 m to 8.2 m), and the first Fresnel RF1 zone radius decreased in RF1W/R ¼ 1.9 times (from 231.0 m to 121.0 m) (Table 4.2). When comparing the results of case study 3 with the reference version 1, the following changes are noted (Tables 4.3 and 4.4): (a) According to the original wave field (WF) section, the following changes are observed: the informativeness in signal energy is dSinfw1 ¼ 66.0%, the signal-tonoise ratio is dRSNW1 ¼ 100.6%, and the vertical resolution is dhRw1 ¼ 69.8%, the horizontal resolution is dRhw1 ¼ 69.9% (Table 4.3) regarding estimates of processing version 1, which are taken as 100%. It can be stated that there is a considerable potential for improving the quality of digital seismic data pre-processing. In particular, by the energy informativity of the signal by ΔSinfw1 ¼ 34.0%, by the vertical resolution ΔhRw1 ¼ 30.2%, and by the lateral resolution ΔRhw1 ¼ 30.1% (Table 4.4). There was practically no change in the signal-to-

146

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

noise ratio ΔRSNW1 ¼ 0.6% (Table 4.4). This is obviously due to the fact that this indicator is increased due to the narrowing of the frequency band (to values of 10–58 Hz). However, this worsened the detail (resolution) both vertically hRw (from 14.8 to 21.2 m) and horizontally Rhr (from 21.1 to 30.2 m); (b) In the reflection coefficients (RC), the following can be noted: the informativeness in signal energy is dSinfr1 ¼ 52.7%, in the signal-to-noise is dRSNR1 ¼ 98.0%, in the vertical resolution dhRr1 ¼ 42.1%, and in the horizontal resolution dRhr1 ¼ 41.5% (Table 4.3) of the corresponding estimates of version 1. Consequently, there is an opportunity to improve the seismic characteristics of the inverted RC and AI sections. In particular, this can be done by the signal energy information content of ΔSinfr1 ¼ 47.3%, by the signal-to-noise ratio, ΔRSNR1 ¼ 2.0%; by vertical resolution, ΔhRr1 ¼ 57.9%, and by lateral resolution, ΔRhr1 ¼ 58.5% (Table 4.4). In this variant of transformations, there is a considerable reserve for increasing the detail of the AI and RC sections, i.e., the optimal model in this case is not obtained. This can also be seen from the comparison of the energy spectra of the sections of the RC of variants 1 and 3 of processing (Fig. 4.12b; version 3): the energy of the signals at frequencies of 100–250 Hz is two times less than for version 1, i.e., thin-layer section is not restored optimally. The average values of seismic data processing (dEw1), data inversion (dEr1) efficiencies, and estimation of the potential for optimal processing and solving the inverse seismic problem (ΔEw1, ΔEr1) relative to the sets of estimated parameters for version 1 are given in Table 4.5. Here, for case 3, the efficiency of processing the wave field and inverting to the AI and RC sections is dEw1 ¼ 76.6% and dEr1 ¼ 58.6%. Potential increase of processing and inverting informativeness is ΔEw1 ¼ 23.4% and ΔEr1 ¼ 41.4%. Visually, the original section is characterized by a low-frequency composition of seismic records, complicated by noises, the structural details in the wave field are smoothed (Fig. 4.12a; version 3). In the reconstructed section of the AI, more thick layers dominate, and the section as a whole is noisy (Fig. 4.12b; version 3). On the RC section, there is no clear presentation about the internal structure of the target layers; OWC is practically not traceable (Fig. 4.17c).

4.2.6

Version 4: KMGph

The results of preprocessing and restoration of the medium model for version 4 are presented in Fig. 4.18. The kinematic features of the structure after processing are significantly different from the processing and subsequent transformations of the reference version 1. The maximum deviation of the horizon associated with the deposits of the Bazhenov formation top is approximately 20 ms and falls on the middle part of the profile PK 5000–12500 (Fig. 4.11). In the initial part of the line, a subhorizontal sedimentation is observed (Fig. 4.18a), whereas in processing version

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

147

Fig. 4.18 The results of the fourth version seismic data pre-processing using the HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

148

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.18 (continued)

1, the beginning of the line has a monoclinal immersion, which then turns into a small anticlinal structure in the area of location PC 8000 (Fig. 4.13a). The energy of the wave field reflections is in the frequency band 15–65 Hz (Figs. 4.18a and 4.12a; version 4) and is estimated by the value Psw ¼ 18.12 c.u. (Table 4.1). The signal-to-noise ratio is RSNW ¼ 5.49 (Table 4.2) and the vertical resolution is ΔtRw ¼ 14.3 ms ( fRw ¼ 34.8 Hz and hRw ¼ 16.5 m) (Table 4.1; Fig. 4.18a). The radius of the first Fresnel zone is RF1W ¼ 204.0 m, and the horizontal resolution according to (4.3) is Rhw ¼ 23.6 m (Table 4.2). The power spectrum band is shifted toward high frequencies as compared with case 3, with their equal width (Fig. 4.18a). The dynamic features of the structure in a RC cross section are characterized by the following parameters: the signal energy covers the frequency range of 15–250 Hz (Figs. 4.18c and 4.12b; version 4) with a frequency band rejection of 95–105 Hz, and the signal energy in this range is Psr ¼ 61.75 c.u. (Table 4.1). The signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 8.90 (Table 4.2), the vertical resolution is ΔtRr ¼ 2.5 ms ( fRr ¼ 196.7 Hz and hRr ¼ 2.9 m) (Table 4.1),

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

149

the first Fresnel zone radius in accordance with (4.2) is RF1R ¼ 86.0 m, and the horizontal resolution according to (4.3) is equal to Rhr ¼ 4.2 m (Table 4.2). In the trace energy spectrum of the RC sections (Figs. 4.18c and 4.12b; version 4) relative to Fig. 4.13c, version 1, the signal energy at the main frequency band of 10–70 Hz is much less (for formations with a considerable thickness) and more than two times less in the frequency band of the thin-layer part of 95–250 Hz. It may be marked that the thin-layer model is not optimally restored. When comparing with the reference version 1 of preprocessing and inversion, improvements can be achieved for the case study 4 (Tables 4.3 and 4.4) according to the calculated values: (a) In the initial WF section: according to the energy informativeness of the signal by the value ΔSinfw1 ¼ 35.5% (from 18.1 to 28.1 c.u.), by the vertical resolution ΔhRw1 ¼ 10.3% (from 16.5 to 14.8 m), and by the lateral resolution ΔRhw1 ¼ 10.6% (from 23.6 to 21.1 m); (b) In the RC section: according to the energy informativity of the signal by the value ΔSinfr1 ¼ 58.8% (from 61.8 to 150.0 c.u.), by the vertical resolution ΔhRr1 ¼ 17.2% (from 2.9 to 2.4 m), and by the lateral resolution ΔRhr1 ¼ 19.0% (from 4.2 to 3.4 m). In general, the efficiency of wave field processing and inverting into AI and RC sections for version 4 is dEw1 ¼ 87.0% and dEr1 ¼ 75.8% (Table 4.5), respectively; the potential possibility of increasing the informativeness of processing and inversion is ΔEw1 ¼ 13.0% and ΔEr1 ¼ 24.2% (Table 4.5), respectively. The inversion results as a whole make it possible to interpret perspective layers. However, thin geological objects cannot be distinguished.

4.2.7

Version 5: MU

The results of preprocessing and recovery of the medium for the fifth version are presented in Fig. 4.19. The reflected waves of the WF section turned out to be more gently sloping with a monoclinal trend across the entire profile, and the difference in the correlation of the horizon along the top of the Bazhenov suite reaches 40 ms with respect to processing version 1 (Fig. 4.11). The power spectrum of the wave field occupies a frequency band of 8–58 Hz (Figs. 4.19a and 4.12a; version 5), the signal energy is estimated at Psw ¼ 26.14 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 5.40 (Table 4.2), the vertical resolution is ΔtRw ¼ 17.4 ms ( fRw ¼ 28.8 Hz and hRw ¼ 20.0 m) (Table 4.1; Fig. 4.19a), the radius of the first Fresnel zone is RF1W ¼ 225.0 m, and the horizontal resolution according to (4.3) is Rhw ¼ 28.5 m (Table 4.2). In the dynamics of an RC cross section, the signal energy takes a frequency band of 10–250 Hz (Figs. 4.19c and 4.12b; version 5) with a value of Psr ¼ 93.13 c.u. (Table 4.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 8.82 (Table 4.2), the vertical resolution is ΔtRr ¼ 3.4 ms ( fRr ¼ 145.4 Hz and hRr

150

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.19 The results of the fifth version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

151

Fig. 4.19 (continued)

¼ 4.0 m) (Table 4.1); the radius of the first Fresnel zone according to the formula (4.2) is RF1R ¼ 100.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 5.6 m (Table 4.2). In the energy spectrum of the RC sections (Figs. 4.19c and 4.12b; version 5) with respect to case study 1 (Fig. 4.13c; version 1), the energy of the signals in the main frequency band of 10–70 Hz is the same, except for the frequency band 55–70 Hz, where it is almost two times less than in the frequency range for a thin-layer interval of the section (100–250 Hz). It is obvious that the thin-layer medium for case 5 is not optimally restored. A comparison of the results obtained (for version 5) with the reference case 1 showed that the possibility of increasing efficiency in determining the desired characteristics (Tables 4.3 and 4.4) remains: (a) In the initial WF section: according to the energy informativeness of the signal by the value ΔSinfw1 ¼ 6.9% (from 26.1 to 28.1 c.u.), by the vertical resolution ΔhRw1 ¼ 26.0% (from 20.0 to 14.8 m), and by lateral resolution ΔRhw1 ¼ 26.0% (from 28.5 to 21.1 m);

152

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

(b) In the RC section: by the energy information of the signal by the value ΔSinfr1 ¼ 37.9% (from 93.1 to 150.0 cu), by the vertical resolution ΔhRr1 ¼ 40.0% (from 4.0 to 2.4 m), and by lateral resolution ΔRhr1 ¼ 39.3% (from 5.6 to 3.4 m). The efficiency of wave field processing and inversion into the sections of the AI and RC version 5 is dEw1 ¼ 86.0% and dEr1 ¼ 70.1%, respectively, and the increasing informativeness possibility of processing and inversion is ΔEw1 ¼ 14.0% and ΔEr1 ¼ 29.9%, respectively (Table 4.5). The section of the wave field (Fig. 4.19a) is represented by low-frequency reflections with a relatively low noise level. Obviously, in the process of restoring the environment subsurface model (sections of the AI and RC), it is possible to extract additional geological information about the structural elements of the section (Fig. 4.19b, c). However, the maximum extraction of the structure subtle features for version 5 did not occur, since the preliminary seismic data processing did not reach its optimal mode.

4.2.8

Version 6: Prdgm

The results of preprocessing and restoration of the medium model for version 6 are presented in Fig. 4.20. The maximum deviation of the reflecting boundary from the top of the Bazhenov suite in the range of PK 6000–14000 reaches 25 ms (Fig. 4.11). The structures manifested in the section largely coincide with processing version 1. However, the ratio of the structures amplitudes is somewhat different. The power spectrum of the wave field has a frequency band of 18–65 Hz (it is narrowed on the low-frequency side) (Figs. 4.20a and 4.12a; version 6), the signal energy is estimated at Psw ¼ 20.06 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 3.27 (Table 4.2), the vertical resolution is ΔtRw ¼ 13.6 ms ( fRw ¼ 36.7 Hz and hRw ¼ 15.6 m) (Table 4.1; Figs. 4.20a), the first Fresnel zone radius is RF1W ¼ 199.0 m, and the horizontal resolution according to (4.3) is Rhw ¼ 22.4 m (Table 4.2). In the dynamics of the RC cross section, the signal energy is in a frequency band of 18–250 Hz (Fig. 4.20b and 4.12b; version 6) with a value of Psr ¼ 69.09 c.u. (Table 4.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 5.30 (Table 4.2), and the vertical resolution is ΔtRr ¼ 2.3 ms ( fRr ¼ 217.4 Hz and hRr ¼ 2.6 m) (Table 4.1); the first Fresnel zone radius (4.2) is RF1R ¼ 82.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 3.8 m (Table 4.2). In the energy spectrum of the RC section (Figs. 4.20b and 4.12b; version 6) relative to Fig. 4.13b (version 1), the signal energy at the main frequency band occupies a narrower band of 18–70 Hz; in the frequency band of 72–105 Hz, reflection energy is negligible. In the thin-layer part of the spectrum—the frequency band of 98–250 Hz—the energy of the reflected waves is two times lower than that of the case of version 1, i.e., thin layer not restored optimally. There are noises in the section with RSNR ¼ 5.30.

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

153

Fig. 4.20 Results of the sixth version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

154

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.20 (continued)

Compared with the reference case 1 for version 6, there remains a real possibility of improving the following characteristics (Tables 4.3 and 4.4): (a) In the initial WF section: according to the energy informativeness of the signal by the value ΔSinfw1 ¼ 28.6% (from 20.1 to 28.1 c.u.), by the vertical resolution ΔhRw1 ¼ 5.1% (from 15.6 to 14.8 m), and by the lateral resolution ΔRhw1 ¼ 5.8% (from 22.4 to 21.1 m); (b) In the RC section: according to the energy informativity of the signal by the value ΔSinfr1 ¼ 53.9% (from 69.1 to 150.0 c.u.), by the vertical resolution ΔhRr1 ¼ 7.7% (from 2.6 to 2.4 m), and by the lateral resolution ΔRhr1 ¼ 10.5% (from 3.8 to 3.4 m). The achieved efficiency of processing the wave field and inverting into the sections of the AI and RC for version 6 is dEw1 ¼ 80.7% and dEr1 ¼ 71.6%, respectively; there remains the possibility of increasing the informativeness of processing and data inversion at ΔEw1 ¼ 19.3% and ΔEr1 ¼ 28.4% (Table 4.5), respectively.

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

155

The wave section turned out to be more high-frequency; the noise level is average (Fig. 4.20a). The results of the medium model (AI and RC) restoration show the possibility of extracting additional geological information about the structural and dynamic features of the section (Fig. 4.20b, c). However, in general, the maximum extraction of subtle features did not occur—the preprocessing did not reach the optimal level.

4.2.9

Version 7: PtrAlnce

The results of the preprocessing and restoration of the medium model for version 7 are presented in Fig. 4.21. The deviation of the reflecting boundary from the top of the Bazhenov suite for version 7 in comparison with such a horizon for case study 1 in the time representation is minimal, and it reaches about 10 ms (Fig. 4.11). Small structures (of greater order) are smoothed compared to processing version 1. The power spectrum of the wave field occupies a frequency band of 8–70 Hz (Figs. 4.21a and 4.12a; version 7), the signal energy is estimated at Psw ¼ 26.97 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 3.74 (Table 4.2), and the vertical resolution is ΔtRw ¼ 12.3 ms ( fRw ¼ 40.7 Hz and hRw ¼ 14.1 m) (Table 4.1; Fig. 4.21a). The first Fresnel zone radius is RF1W ¼ 189.0 m, and the horizontal resolution according to expression (4.3) is Rhw ¼ 20.2 m (Table 4.2). In the dynamics of the RC section, the signal energy is in the frequency band of 10–250 Hz (Figs. 4.21b and 4.12b; version 7) with a value of Psr ¼ 146.87 c.u. (Table 4.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 4.75 (Table 4.2), and the vertical resolution is ΔtRr ¼ 2.6 ms ( fRr ¼ 195.3 Hz and hRr ¼ 2.9 m) (Table 4.1). The first Fresnel zone radius according to (4.2) is RF1R ¼ 86.0 m, and the horizontal resolution according to formula (4.3) is Rhr ¼ 4.2 m (Table 4.2). In the energy spectrum of the RC section (Figs. 4.21c and 4.12b; version 7) with respect to the first processing version (Fig. 4.13b, version 1), the signal energy is 98% comparable. However, the noise level is rather high: RSNR ¼ 4.75. In relation to the reference case study 1 for version 7, it is possible to improve the following characteristics (Tables 4.3 and 4.4): (a) In the initial WF section: on the energy informativeness of the signal by the value ΔSinfw1 ¼ 4.0% (from 27.1 to 28.1 c.u.), vertically and horizontally resolutions are comparable with version 1: hRw ¼ 14.1 m (14.8 m) and Rhw ¼ 20.2 m (21.1 m), respectively; (b) In the RC section: by the energy informativity of the signal by the value ΔSinfr1 ¼ 2.1% (from 146.9 to 150.0 c.u.), by the vertical resolution ΔhRr1 ¼ 17.2% (from 2.9 to 2.4 m), and by the lateral resolution ΔRhr1 ¼ 19.0% (from 4.2 to 3.4 m). The achieved efficiency of processing the wave field and inverting into the sections of the AI and RC of version 7 is dEw1 ¼ 94.2% and dEr1 ¼ 78.6%,

156

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.21 The results of the seventh version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

157

Fig. 4.21 (continued)

respectively; there also remains the possibility of increasing the informativeness of processing and inverting at ΔEw1 ¼ 5.8% and ΔEr1 ¼ 21.4% (Table 4.5), respectively. However, it should be noted that the source field contains a significant level of noise. In this case, it would be more efficient to suppress noise in the preprocessing process. The wave section is represented by fairly wideband reflection signals; the noise level here is high, RSNW ¼ 3.74 (Fig. 4.21a). The reconstructed model of the medium (AI and RC) (Fig. 4.21b, c) also contains a high level of noise, RSNW ¼ 4.75, which somewhat impairs the interpretability of the resulting data sections.

4.2.10 Version 8: SvMGph The results of preprocessing and medium recovery for version 8 are presented in Fig. 4.22. The reflector deviation from the top of the Bazhenov suite with respect to the horizon for case 1 on the section interval under investigation is quite significant and is about 45 ms (Fig. 4.11).

158

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.22 The results of the eighth version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

159

Fig. 4.22 (continued)

The energy spectrum of the wave field is quite narrow and takes a frequency band of 10–50 Hz (Fig. 4.22a; version 8), the signal energy is estimated at Psw ¼ 17.67 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 4.82 (Table 4.2), and the vertical resolution is ΔtRw ¼ 15.0 ms ( fRw ¼ 33.4 Hz and hRw ¼ 17.2 m) (Table 4.1; Fig. 4.22a). The first Fresnel zone radius is RF1W ¼ 209.0 m, and the horizontal resolution according to the formula (4.3) is Rhw ¼ 24.6 m (Table 4.2). The RC signal energy is in the frequency band of 10–250 Hz (Figs. 4.22c and 4.12 b; version 8) with a value of Psr ¼ 75.61 c.u. (Table 4.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 8.09 (Table 4.2), the vertical resolution is ΔtRr ¼ 3.9 ms ( fRr ¼ 127.5 Hz and hRr ¼ 4.5 m) (Table 4.1), the first Fresnel zone radius in accordance with (4.2) is RF1R ¼ 107.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 6.4 m (Table 4.2). In the RC energy spectrum (Figs. 4.22c and 4.12b; version 8) relative to the first processing case study (Fig. 4.13b, version 1), the signal energy is 51%. In relation to the reference version 1, it is possible to obtain the following improved characteristics (Tables 4.3 and 4.4):

160

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

(a) In the original section of the WF: according to the energy informativeness of the signal by the value ΔSinfw1 ¼ 37.1% (from 17.7 to 28.1 c.u.), by the vertical resolution ΔhRw1 ¼ 14.0% (from 17.2 to 14.8 m), and by the lateral resolution ΔRhw1 ¼ 14.2% (from 24.6 to 21.1 m); (b) In the RC data: according to the energy informativity of the signal by the value ΔSinfr1 ¼ 49.6% (from 75.6 to 150.0 c.u.), by the vertical resolution ΔhRr1 ¼ 46.7% (from 4.5 to 2.4 m), and by the lateral resolution ΔRhr1 ¼ 46.9% (from 6.4 to 3.4 m). The achieved efficiency of processing the wave field and inverting it into sections of the AI and RC for version 8 is dEw1 ¼ 81.6% and dEr1 ¼ 61.6% (Table 4.5), respectively; at the same time, there remains the possibility of increasing the information content of processing and inversion at ΔEw1 ¼ 18.4% and ΔEr1 ¼ 38.4% (Table 4.5), respectively. A section of the wave field is represented by low-frequency reflections with an average level of noise (Fig. 4.22a). Judging by the results of the restoration of the medium model (AI and RC) (Fig. 4.22b, c), it is possible to extract additional geological information about the structural and dynamic features of the section. There is a significant reserve for increasing the information content.

4.2.11 Version 9: SbNGph For the nineth version of the preprocessing and medium model restoration, the results are presented in Fig. 4.23. The main features of the section structure are as follows. The deviation of the boundary reflected from the top of the Bazhenov suite in comparison with the boundary of version 1 (Fig. 4.11) at the time interval under study reaches about 30 ms. The energy spectrum of the wave field is in the frequency band of 10–60 Hz (Figs.4.23a and 4.12a; version 9), the signal energy is estimated at Psw ¼ 20.81 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 4.66 (Table 4.2), the vertical resolution is ΔtRw ¼ 13.3 ms ( fRw ¼ 37.6 Hz and hRw ¼ 15.3 m) (Table 4.1; Fig. 4.23 a). The first Fresnel zone radius is RF1W ¼ 197.0 m, and the horizontal resolution according to (4.3) is Rhw ¼ 21.8 m (Table 4.2). The RC signal energy takes up a frequency range of 10–250 Hz (Figs. 4.23c and 4.12b; version 9) with a value of Psr ¼ 74.95 c.u. (Table 1.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 7.62 (Table 4.2), the vertical resolution is ΔtRr ¼ 2.9 ms ( fRr ¼ 170.7 Hz and hRr ¼ 3.4 m) (Table 4.1). The radius of the first Fresnel zone according to (4.2) is RF1R ¼ 92.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 4.8 m (Table 4.2). In relation to the reference version 1, it is possible to obtain improved characteristics (Tables 4.3 and 4.4): (a) In the WF: according to the energy informativeness of the signal by the value ΔSinfw1 ¼ 25.9% (from 20.8 to 28.1 c.u.), by the vertical resolution

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

161

Fig. 4.23 The results of the nineth version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

162

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.23 (continued)

ΔhRw1 ¼ 3.3% (from 15.3 to 14.8 m), by the lateral resolution ΔRhw1 ¼ 3.2% (from 21.8 to 21.1 m); (b) In the RC: according to the energy informativity of the signal by the value ΔSinfr1 ¼ 50.0% (from 75.0 to 150.0 c.u.), by the vertical resolution ΔhRr1 ¼ 29.4% (from 3.4 to 2.4 m), and by the lateral resolution ΔRhr1 ¼ 29.2% (from 4.8 to 3.4 m). The efficiency of wave field processing and inverting into sections of the AI and RC version 9 is dEw1 ¼ 89.1% and dEr1 ¼ 68.9% (Table 4.5), respectively; the possibility remains, increasing the informativeness of processing and inversion at ΔEw1 ¼ 10.9% and ΔEr1 ¼ 31.1% (Table 4.5), respectively. Wave section is represented by low-frequency reflections; the level of noise is average (Fig. 4.23a). As can be seen from the obtained results, including the restored model of the medium (Fig. 4.23b, c), it is possible to extract additional geological information about the structural and dynamic features of the section.

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

163

4.2.12 Version 10: Svginf The results of preprocessing and restoring the environmental subsurface model for version 10 are shown in Fig. 4.24. In this case, the reflector deviation for the top of the Bazhenov suite in relation to that for case 1 (Fig. 4.1) at the studied interval reaches about 30 ms at some points. The energy spectrum for the wave field takes up a frequency band of 15–60 Hz (Figs. 4.24a and 4.12a; version 10), the signal energy is estimated at Psw ¼ 24.76 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 4.30 (Table 4.2), the vertical resolution is ΔtRw ¼ 14.6 ms ( fRw ¼ 34.3 Hz and hRw ¼ 16.8 m) (Table 4.1, Fig. 4.24a). The first Fresnel zone radius RF1W ¼ 206.0 m, and the horizontal resolution according to (4.3) is Rhw ¼ 23.9 m (Table 4.2). The RC signal energy takes up a frequency band of 15–250 Hz (Figs. 4.24c and 4.12, b; version 10) with a value of Psr ¼ 84.02 c.u. (Table 4.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 6.97 (Table 4.2), the vertical resolution is ΔtRr ¼ 3.8 ms ( fRr ¼ 129.9 Hz and hRr ¼ 4.4 m) (Table 4.1). The radius of the first Fresnel zone in accordance with (4.2) is RF1R ¼ 106.0 m (Table 4.2), the horizontal resolution according to (4.3) is Rhr ¼ 6.3 m (Table 4.2). In relation to the reference version 1, the practical possibility of improving the following characteristics is maintained (Tables 4.3 and 4.4): (a) In the WF: according to the energy informativeness of the signal by ΔSinfw1 ¼ 11.9% (from 24.8 to 28.1 c.u.), by the vertical resolution ΔhRw1 ¼ 11.9% (from 16.8 to 14.8 m), by the lateral resolution ΔRhw1 ¼ 11.7% (from 23.9 to 21.1 m); (b) In the RC: according to the energy informativeness of the signal by the value ΔSinfr1 ¼ 44.0% (from 84.0 to 150.0 c.u.), by the vertical resolution dhRr ¼ 45.5% (from 4.4 to 2.4 m), and by the lateral resolution dRhr ¼ 46.0% (from 6.3 to 3.4 m). The efficiency of wave field processing and inverting into the AI and RC of version 10 is dEw1 ¼ 86.6% and dEr1 ¼ 60.4% (Table 4.5), respectively. It also remains possible to increase the informativeness of processing and inversion at ΔEw1 ¼ 13.4% and ΔEr1 ¼ 39.6% (Table 4.5), respectively. The wave field section is represented by low-frequency reflections with an average level of noise (Fig. 4.24a). The restored model of the environment (AI and RC) (Fig. 4.24b, c) provides an opportunity to extract additional geological information about the structural elements of the section.

4.2.13 Version 11: TNGph The results of pre-processing and restoration of the medium model for version 11 are presented in Fig. 4.25. The deviation of the horizon obtained along Bazhenov suite

164

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.24 The results of the tenth version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

165

Fig. 4.24 (continued)

top from the same boundry as for version 1 (Fig. 4.11) at the considered time interval is significant, for some points it reaches about 35 ms. The energy spectrum of the wave field occupies a frequency band of 10–50 Hz (Figs. 4.25a and 4.12a; version 11), the signal energy is estimated at Psw ¼ 11.79 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 5.15 (Table 4.2), the vertical resolution is ΔtRw ¼ 19.6 ms ( fRw ¼ 25.5 Hz and hRw ¼ 22.5 m) (Table 4.1; Fig. 4.25a). The radius of the first Fresnel zone RF1W ¼ 239.0 m, and the horizontal resolution according to (4.3) is Rhw ¼ 32.2 m (Table 4.2). The signal energy for the RC takes up a frequency band of 10–250 Hz (Figs. 4.25c and 4.12b; version 11) with a value of Psr ¼ 55.48 c.u. (Table 4.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 8.74 (Table 4.2), the vertical resolution is ΔtRr ¼ 2.9 ms ( fRr ¼ 173.6 Hz and hRr ¼ 3.3 m) (Table 4.1), the first Fresnel zone radius according to (4.2) is RF1R ¼ 92.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 4.7 m (Table 4.2). In relation to the reference case study 1, it remains possible to improve the following characteristics (Tables 4.3 and 4.4):

166

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.25 The results of the 11th version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

167

Fig. 4.25 (continued)

(a) In the WF: according to the energy informativeness of the signal by the value ΔSinfw1 ¼ 58.0% (from 11.8 to 28.1 c.u.), by the vertical resolution ΔhRw1 ¼ 34.2% (from 22.5 to 14.8 m), by the lateral resolution ΔRhw1 ¼ 34.5% (from 32.2 to 21.1 m); (b) In the RC: according to the energy informativeness of the signal by the value ΔSinfr1 ¼ 63.0% (from 55.5 to 150.0 cu), by the vertical resolution ΔhRr1 ¼ 27.3% (from 3.3 to 2.4 m), and by the lateral resolution ΔRhr1 ¼ 27.7% (from 4.7 to 3.4 m). The efficiency of wave field processing and inverting into sections of the AI and RC version 11 is dEw1 ¼ 67.8% and dEr1 ¼ 69.7% (Table 4.5), respectively; the possibility of increasing the informativeness of processing and inversion at ΔEw1 ¼ 32.2% and ΔEr1 ¼ 30.3% (Table 4.5), respectively, remains. Wave section is represented by low-frequency reflections; the noise level is average (Fig. 4.25a). From the restored model (AI and RC) (Fig. 4.25b, c), in particular, it is clear that there is a real opportunity to extract additional geological information about the structural and dynamic elements of the section structure.

168

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

4.2.14 Version 12: YtGph The results of the preprocessing and medium recovery for version 12 are presented in Fig. 4.26. The deviation of the reflector from the top of the Bazhenov suite from the analogous horizon of version 1 (Fig. 4.11) on the interval is significant and reaches 45 ms at some points. The wave field energy spectrum is in a frequency band of 10–67 Hz (Figs. 4.26a and 4.12a; version 12), the signal energy is estimated at Psw ¼ 27.09 c.u. (Table 4.1), the signal-to-noise ratio is RSNW ¼ 5.20 (Table 4.2), and the vertical resolution is ΔtRw ¼ 12.8 ms ( fRw ¼ 39.0 Hz and hRw ¼ 14.7 m) (Table 1.4; Fig. 4.46a). The first Fresnel zone radius is RF1W ¼ 193.0 m, and the horizontal resolution according to (4.3) is Rhw ¼ 21.0 m (Table 4.2). The signal energy for the RC traces takes up a frequency band of 8–250 Hz (Figs. 4.26c and 4.12b; version 12) with a value of Psr ¼ 148.16 c.u. (Table 4.1), the signal-to-noise ratio according to the formula (4.7) is RSNR ¼ 8.99 (Table 4.2), and the vertical resolution is ΔtRr ¼ 2.4 ms ( fRr ¼ 205.6 Hz and hRr ¼ 2.8 m) (Table 4.1). The radius of the first Fresnel zone in accordance with (4.2) is RF1R ¼ 84.0 m, and the horizontal resolution according to (4.3) is Rhr ¼ 4.0 m (Table 4.2). In relation to the reference version 1, it remains possible to improve the following characteristics (Tables 4.3 and 4.4): (a) In the WF: on the energy informativeness of the signal by the value ΔSinfw1 ¼ 7.1% (from 26.1 to 28.1 c.u.), the vertical and horizontal resolutions comparable to version 1: hRw ¼ 14.7 m (14.8 m) and Rhw ¼ 21.0 m (21.1 m), respectively; (b) In the RC: according to the energy informativeness of the signal by the value ΔSinfr1 ¼ 1.2% (from 148.2 to 150.0 c.u.), by the vertical resolution ΔhRr1 ¼ 14.3% (from 2.8 to 2.4 m), and by the lateral resolution ΔRhr1 ¼ 15.0% (from 4.0 to 3.4 m). The efficiency of wave field processing and inversion into AI and RC sections of version 12 is dEw1 ¼ 98.3% and dEr1 ¼ 91.6% (Table 4.5), respectively; there remains the possibility of increasing the informativeness of processing and inversion at ΔEw1 ¼ 1.7% and ΔEr1 ¼ 8.4% (Table 4.5), respectively. The wave field reflections are generally recorded with a low level of interference (Fig. 4.26a). The restored model of the subsurface medium (AI and RC) (Fig. 4.26b, c) provides an opportunity to extract additional geological information about the structural elements of the section. The above results of preliminary processing of real seismic materials (processed in different industrial and scientific organizations using different processing graphs included in different processing systems), as well as quantitative estimates of the results of their processing using the HRS-Geo technology, are depicted by the authors as concentrated summary figures and diagrams (Figs. 4.27–4.29). In accordance with these data, the following can be noted:

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

169

Fig. 4.26 The results of the 12th version seismic data preprocessing using HRS-Geo technology inversion programs: (a) the input time section for the WF inversion, (b) the result of inversion, AI section, (c) the result of inversion, RC section

170

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.26 (continued)

1. The comparison of the energy spectra (power spectra) of reflected waves in the interval of the Jurassic complex for all 12 variants of seismic data preprocessing (results shown in Fig. 4.12a) showed that among the power spectra, the widest frequency band differs in the spectra for first, seventh, and 12th versions. It should be borne in mind that the target information contained in the reflected waves is largely related to the bandwidth. The wider the frequency band, the more complete the identification information about geological indicators, which is contained in the dynamics of the seismic record. From Fig. 4.12a, in particular, it can be seen that the widest (and most complete in area) frequency band is at the first preprocessing version. Close to it are the frequency bands (with 96% of the area from case 1) of the seventh and 12th versions. For the remaining variants, this parameter varies in the range of 42–93%, since for these versions all the above-presented time sections and the results of their subsequent inversion—the RC and AI sections—are of lower quality. 2. When comparing the energy spectra (power spectra) of the RC sections restored in the same interval of the sediment Jurassic complex for all 12 versions of seismic data processing (spectra shown in Fig. 4.12b), an original picture was obtained. From these power spectra (covering the frequency range

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

171

Δf  8–250 Hz), in particular, it can be seen that the most complete areas occupied by this frequency band Δf relate to the spectra of RC traces for the first, seventh, and 12th preprocessing and recovery model of the subsurface environment. Considering that during the process of restoring the medium model, the parameters of the inversion procedure were the same for all 12 analyzed preprocessing versions; it is quite logical that the results of preprocessing are directly related to the results of restoring the environment model (viz., the wider the spectrum of the original wave field, the more completely (by the area of the components being restored) the power spectrum of the restored model of the medium). In addition, the larger the percentage of the taken up power spectrum area, the more resolved and dynamically expressed were the results of the model restoring on the traces of the corresponding sections of RC and AI. Without stopping to consider all the details and structural features of each of the 12 preprocessing versions, we only note that the use of HRS-Geo technology programs ultimately allows obtaining objective qualitative and quantitative results to assess the effectiveness of preprocessing and subsequent restoration of the real subsurface model. 3. The comparison of the signal-to-noise ratio, vertical and horizontal resolutions for the original wave field, and the results of the model recovery, calculated for all 12 preprocessing versions, showed a significant improvement in the estimated parameters at the stage of creating the inversion results (Fig. 4.27). In general, the application of the recovery procedure for the medium model, as can be seen from the diagrams, shows that for almost all 12 preprocessing and recovery versions, increased values of the signal-to-noise ratio for the inversion results (RSNR) were obtained. At the same time, the seismic data vertical (hRr) and horizontal (Rhr) resolutions have been significantly improved. Without dwelling on a detailed analysis of all quantitative estimates of the parameters under consideration, it is worth noting, in general, a significant increase in the informativeness of seismic data through the use of programs to restore the real subsurface model. 4. The results of comparing the characteristics of the original wave field and the inversion (the results of the subsurface model restoration, including such estimates as the signal-to-noise ratio, vertical and horizontal resolution, and the first Fresnel zone radius) are shown in Fig. 4.28. The ratio of these parameters to each other (within each of the presented versions sets), also on the different characteristics under estimation, showed that, for the results of the inversion, a significant increase in the signal-to-noise ratio was observed, as well as a decrease (improvement) in the vertical and horizontal resolution values and the first Fresnel radius. 5. The efficiency diagrams of the research results constructed using real seismic materials (implemented for preprocessing and recovery of the subsurface model) show separately the ratio of the effectiveness of the testing procedures in relation to some optimum—the first version of preprocessing and inverting, as well as the assessment of the potential to achieve the optimal solution of the problem as a whole (Fig. 4.29). It is clearly seen that for the preprocessing stage, the ratio of the specified parameters (efficiency and potential optimum) differs significantly from

172

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.27 Signal-to-noise ratio, vertical and horizontal resolutions for versions: (a) the wave field, (b) the results of the inversion

the parameters obtained for the seismic data inversion stage after preprocessing. This feature should be taken into account and used both in the processing and interpretation of seismic data. When working with materials from various service centers for processing seismic data, the authors sought to unambiguously assess the quality of preprocessing and subsequent restoration of the subsurface model and also perform the necessary actions to increase the potential of the process of interpreting seismic observation

4.2 Examples of Solving the Inverse Dynamic Problem on Test and Real Data

173

Fig. 4.28 Comparison of the characteristics for the input data and the results of data inversion: (a) the ratio of signal to noise, (b) vertical resolution, (c) lateral resolution, (d) the radius of the first Fresnel zone

174

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.29 Data preprocessing and inverse dynamic seismic problem solution efficiency diagrams and assessment of the potential to achieve optimum

materials. The results of such work performed by the authors in different seismic and geological conditions are presented in Chaps. 8 and 9.

4.3

Seismic Data Processing Using HRS-Geo Technology

The sections related to seismic modeling of wave dynamic parameters (Chap. 2) and the solution of the inverse dynamic seismic problem (IDSP) (Sects. 4.1.1, 4.2.2–4.2.14) provide materials showing that the ideal way to take into account (exclude) the effect of wave interference and build thin-layer subsurface models of the real medium is the numerical solution of the IDSP. On this basis, the information contained in the seismic record is divided into information about the source of elastic vibrations and information about the real geological environment [1–3, 5, 6, 25, 36, 37]. On the same basis, it becomes possible to get rid of the wave interference phenomenon created by the source of elastic oscillations and the propagation of a seismic pulse in a real medium. However, to effectively solve this problem at the stage of seismic data preprocessing, it is necessary to properly prepare the obtained seismic data in order to preserve primary reflected seismic waves against the background of various non-useful regular waves and random noise. At the beginning of this subsection, a schematic model of a seismic record is briefly reviewed, showing what signals (useful ones and noises) one has to deal with in the processing and interpretation of seismic data. The purpose of this subsection is to show how useless waves and noises manifest themselves in the background of the useful part of the seismic record, which have to be “dealt with” in the process of preprocessing seismic observation materials. The authors of the presented scheme (Fig. 4.30) emphasize (with respect to adequate restoration of the detailed internal structure of the thin-layer medium model under study) the distorting role of seismic interference, which can only be eliminated by solving the IDSP. Considering the presence in the record of weak seismic signals—local responses associated with such important geological indicators as porosity and oil and gas

4.3 Seismic Data Processing Using HRS-Geo Technology

Fig. 4.30 Seismic recording model formation scheme

175

176

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

saturation—complex processing of seismic data is performed according to some optimal graph. In the course of processing, only those processing system procedures are used that preserve the dynamics of the seismic record, which is largely adequate to the actual distribution of acoustic heterogeneities of the geological section under study.

4.3.1

Useful Signals and Noises in Seismic Exploration, Noise Classification, and Suppression (Attenuation) of Signal Distortion Factors and Noises

The seismic data of CDP (CMP) method, as is known, is always complicated by noise and undesirable effects. The effect of noise on seismic recording is reduced to masking the useful signals, to distorting their kinematic and dynamic characteristics. Parametrically, this leads to a decrease in the signal-to-noise ratio and deterioration of the seismic record resolution and, as a result, to a decrease in the amount of reliable information extracted from CDP seismic survey data. Field seismic data prior to using the results of seismic exploration for the interpretation purposes, prediction of geological and geophysical parameters, and the calculation of resources (reserves) are subjected to preliminary processing, the main task of which is to eliminate or attenuate regular wave noises and random signals. There are certain difficulties in the noise classification, since the factors distorting the shape of the useful signal in the reflected wave method are very diverse and have their own features. In recent decades, due to the need to solve an inverse dynamic seismic problem, as well as the development and widespread use of inversion technologies, the noise classification has become more complex, and new types of noise have appeared, which were not previously considered as noise (or main noise). In accordance with the well-known seismic data generation model—seismograms of the common shot point (CSP), adopted by the authors of [38] as some “initial” model, the seismogram consists of the following components: primary, multiple, surface (ground), low-velocity, microseismic, and random waves. Taking into account such features of the generation and subsequent formation of a seismic wave field, the authors proposed some generalized classification scheme for useful signals and noises, in which such an important property of a seismic signal as an interference character of the wave field formation process is given a certain place (Fig. 4.30). Also briefly affected are ways to eliminate (or significantly attenuate) various kinds of noise. The presented field formation scheme is largely determined by the well-known processes taking place during the propagation of elastic seismic oscillations in a real medium and the mechanism of transformation (processing) of these vibrations. A certain novelty of the presented scheme lies in the selected property of the wave field—its interference nature, which is conventionally assigned by the authors to the “noise” block. At the same time, the main “efforts” of the algorithm for solving an

4.3 Seismic Data Processing Using HRS-Geo Technology

177

inverse dynamic seismic problem should be aimed primarily at separating information about the environmental parameters (in the form of corresponding distributions of reflectivity and acoustic impedance) and information about the probing medium seismic pulse (the source of elastic oscillations). Let us briefly dwell on the features of the interaction of the signals used and noise with the medium and the process of seismic data preprocessing directly before the IDSP solution. The model of a primary wave can, as is known, be presented as a convolution operation of a seismic pulse S(x, y, t) with the reflection coefficients R(x, y, t) (Fig. 4.30). By the multiple waves of different types, we can attribute the reflection coefficients Rk(x, y, t) and describe the process of the multiple wave generation as a convolution of Rk(x, y, t) with a seismic impulse. In addition, seismic waves are subject to such phenomena as the weakening of their energy due to the spherical divergence of the wave front and the effect of wave absorption, which can be attributed to multiplicative noise (unless the absorption defining is not the purpose of the work). As can be seen in the seismogram formation block diagram (Fig. 4.30), the initial seismic pulse S0(x, y, t) interacts with the medium described by the parameters: V(x, y, z) is the spatial distribution of the wave propagation velocity in the medium, and ρ(x, y, z) is the density distribution of the rocks in it. As a result of the interaction of the pulse with the medium, a spherically diverging, decaying primary wave F1(x, y, t) is formed. It can be represented as a convolution of a varying pulse S1(x, y, t) with reflection coefficients R(x, y, t), creating interference within the seismic wavelength λ that is a block 3. In this case, a change in the pulse structure from S0(x, y, t) into S1(x, y, t) due to passing through the medium can be taken as an effect of multiplicative noise. In this case, regular F2(x, y, t) and random F3(x, y, t) noises are superimposed on a primary wave field—block 4. Additive and multiplicative combination of signals and noises give the function F4(x, y, t)—block 5. Its passage through the observation system (geophone spread and recording channel) with impulse response M(x, y, t) gives a set of seismogram traces F5(i, x, y, t)—block 6; together with the observation geometry and a shot point set, we obtain the traces F6(n, i, x, y, t)—block 7, which, with the background of residual regular and random wave noises, are a set of CSP seismograms of seismic surveys. The wave field stack F7(x, y, t)—block 9 after data preprocessing has three components: reflection coefficients R(x, y, t) - block 10 and impulse as a noise S2(x, y, t) and residual background regular and random noises F8(x, y, t) - block 11. The estimation of contributions to the parameters of reflection coefficients R(x,y,t) and acoustic impedance AI(x,y,t) of such geological indicators as lithological composition (Ccl, Csand and Ccb), reservoir properties (Kp), character of saturation (Co, Cg, and Cw), and others is implemented in block 12 (Fig. 4.30). As shown in Sect. 2.2, contributions concerning the nature of reservoir rocks saturation are due to the presence in the interference seismic recording of mainly weak seismic signals, namely, local responses from such acoustic heterogeneities as oil, gas, and formation water. Obviously, such important characteristics are saved in seismic records when

178

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

using a special processing graph (Sect. 4.3) and interpretation of seismic observation data, which use the procedures of high-resolution seismic technology. In turn, a sufficiently detailed classification of random noise can be performed according to various criteria: according to the distribution law, the correlation type, the stationarity nature, the genesis mechanism, etc. [39]. In accordance with this, according to the law of distribution, noise is divided into Gaussian and non-Gaussian. Such a division is convenient, since in most problems the noise has a Gaussian (normal) distribution, and many non-Gaussian noises are reduced to a Gaussian by transforming of the original data. Non-Gaussian noise can be combined into several groups according to the nature of the distribution (Laplace, Rayleigh, etc.). By the stationarity nature, stationary and nonstationary noises are distinguished. The stationary noise distribution function does not depend on the time coordinate. One of the tasks of detailed seismic data processing, as is well known, is to determine the type of random noise, their characteristics, and contribution (viz., the signal-to-noise ratio). If the noise is located in a narrow spectral band, then it represents the concentrated-on-the-spectrum noise, for example, industrial noise from electrical networks in field measurements. It is also known that various types of multiple (full and partial multiple) waves, reverberation waves, surface (ground roll) waves (Rayleigh and Love), refracted waves (refracted reflected, reflected refracted, etc.), side waves, etc. belong to regular noises. Interference noises, as is known, generate in a thin-layer section as a result of the imposition of seismic pulses reflected from a set of elementary boundaries. Seismic recordings of reflected waves recorded in environments characterized by acoustic heterogeneity in the vertical and horizontal directions are different in that each of its amplitudes on each of its discretes contains geological and geophysical information from the underlying elementary boundaries and strata (as shown in detail in Sect. 2.1). Quantitatively, the noise contribution to seismic records is determined by the signal-to-noise ratio. By this criterion, the effectiveness of the processing procedures is monitored. It is known that in many data processing procedures, an increase in the signal-tonoise ratio leads to a decrease in vertical and lateral resolutions, and vice versa. The peculiarity of interference noise is that their correct attenuation leads to both an increase in the signal-to-noise ratio and an increase in spatial detail (this feature is shown in Sect. 4.2).

4.3.2

Noise Suppression (Attenuation)

Using the differences in the characteristics of useful signals and noises, an algorithm is developed for noise suppressing (attenuating) in compiling a seismic data

4.3 Seismic Data Processing Using HRS-Geo Technology

179

processing graph. At the same time, at different stages of processing, different properties (components) of seismic data can act as useful signals. For example, at the stage of field data preprocessing, a useful signal is a seismic pulse, and the main purpose of such processing is to detect and highlight a seismic pulse among noise. At the stage of applying the procedures for the wave field inversion, the main noise is already the information carrier—the seismic impulses and the useful signals are the impulse responses of the medium in the form of reflection coefficients and acoustic impedance. At the preprocessing phase, different techniques are available to suppress various noises. Optimal noise suppression is possible if their characteristics are most fully investigated and defined. Random noise is filtered based on frequency composition, distribution function, distribution moments (mean, variance), correlation radius, and other parameters. In this case, for optimal suppression of random noise, the Bayesian approach can be used, depending on the study of noise characteristics: the method of maximum a posteriori probability, maximum likelihood method, or least squares method. Noise concentrated in a narrow-spectrum band is suppressed by narrow-band notch filters. Regular noise in various domains is suppressed by single- and multichannel filters (FK filtering, t-p filtering (Radon transform), procedures for modeling multiple and surface waves, followed by subtraction, predictive deconvolution, etc.). The technique of suppressing multiples is based on the difference in the kinematic and dynamic characteristics of primary and multiple waves: 1. 2. 3. 4. 5.

Differentiation in CDP velocity. Differences in the inclination angle of the phases in the CDP stack. Differentiation in frequency composition. Different wave periodicity. Other properties.

The attenuation of regular and random noise is accompanied by an increase in the signal-to-noise ratio and, as a rule, a decrease in resolution. At the stage of wave field inversion, interference noise holds a special place. The peculiarity of these waves consists in the superposition of reflections from the boundaries of a thin-layer section with different amplitudes and phases. This noise is the main obstacle in extracting the detailed sections of the RC and AI from the wave field. The carrier of this noise is the seismic impulse. Interference noise is suppressed during the multistep IDSP solution. In this case, the complete elimination of interference implies a reduction in the wavelength to the minimum size, i.e., to the seismic record sampling step in time. At this stage, the WF contains residual background of regular and random noises. These noises can be suppressed when the WF is inverted, if they are taken into account in the iterative calculation of the direct problem in the process of solving the IDSP.

180

4.3.3

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Seismic Data Processing Using a Special Graph

A detailed study of the physical property distribution of the geological section by the seismic method predetermines the need to save the influence of the main geological and geophysical indicators (lithology, porosity, reservoir saturation) on the characteristics of the wave field. Changes in the type and lithology composition, reservoir properties, and the nature of reservoir rocks fluid saturation, as is known, affect the acoustic properties of the section. The parameters of the reflected wave field depend directly on the distribution of acoustic impedance and reflectivities in the section under study. Consequently, the heterogeneity of the geological profile, manifested in changes of acoustic impedance and reflectivity and the associated changes in lithology, porosity, and saturation, is reflected in the characteristics of the wave field. Such changes of AI and RC should be adequate to the geological indicators of the studied section. Therefore, the peculiarities of the wave field, which are adequate to the corresponding changes in the AI and RC, should have true amplitude ratios. These features, which are manifested in the wave field structure, can be considered from two perspectives: preserving the ratios of reflection energies and saving the waveform according to changes in the reflectivities for the layer sequence of the real medium under study. Preserving the energy ratio of the reflected waves during the entire data processing cycle allows you to correctly extract information about the acoustic impedance of the medium from the wave field, while saving the phase spectrum of reflections ensures adequate resolution and possible subsequent correlation of the reservoir boundaries on the reconstructed reflection coefficients. Such an approach to the processing and interpretation of seismic data ultimately allows us to accurately describe the thin-layer model of the real environment. Two important processing tasks follow from the above, namely, the preservation in the wave field of the contributions of geological and geophysical environment parameters and the simplest amplitude ratios in the reflection coefficient distribution. This raises the problem of achieving maximum spatial detail in the presence of both random and regular wave noises. Obviously, the contradictory nature of processing between the amplitude, vertical, and lateral resolution and the saving significant influence of the targeted predicted geological and geophysical parameters under study manifest themselves here. Preprocessing of seismic data is thus an optimization problem. With the maximum attenuation of various noise type, it is required to retain the most information content on the predicted geological indicators and the best spatial detail. Another important feature of seismic data processing should be noted, which is largely related to the problem of identifying the so-called weak seismic signals [40]. This problem was acutely manifested in the period of 60–80 s of the last century, when the physical and geological bases for solving “direct” searches [41– 43] began to develop widely in the methods of exploration geophysics. It should also be emphasized that the problem of detecting weak signals is at the same time a problem of noise immunity of processing and interpreting systems. The sensitivity and reliability of such systems must be many times higher than in the analysis of

4.3 Seismic Data Processing Using HRS-Geo Technology

181

strong reflections, especially when the intensity levels of the useful weak signal and noise are comparable or the level of the first is much lower than the second one. In turn, the question of the noise immunity of such systems determines the degree of reliability of detectable perspective objects. It can be mentioned that, in real terrigenous environments, unevenly interbedded strata of sandy-aleuritic and clayey formations in terms of their acoustic properties are a diverse alternation of strata with elevated and decreased acoustic impedance, characterized by their various combinations and values of time thicknesses. Differences in the type of lithology, porosity, and saturation of the reservoir are also low-contrast acoustic characteristics. Such a geological environment leads to the formation of low-energy reflected waves—weak seismic signals (local responses from low-contrast heterogeneities), which in a thin-layer medium are influenced by strong primary wave interference, as well as a background of multiple waves, usually generating at a wavelength λ ¼ 100–250 m, probing the real environment. Often, in such a situation, significant complications of the wave pattern are observed due to the fact that the acoustic properties of reservoir rocks and impermeable rocks (ceilling formation) overlap, creating ambiguity in correlating the reflector phases. In this case, when processing seismic data, it is easy to lose local responses—the contributions of reflected waves from low-contrast inhomogeneities under study. When processing seismic data obtained under similar geological conditions, it is necessary to save reflections from low-contrast (acoustic properties) heterogeneities in the form of weak seismic signals against the background of the influence of fairly strong reflections. Hence there is a need for a special approach in preparing the source data for field seismic survey to solve an inverse dynamic problem—a special digital processing of seismic exploration data using the reflected wave method (MCDP). The process of forming a seismic record can schematically be represented by a convolution formula: W ðt Þ ¼ ½Waðt Þ  Wpðt Þ  Wsðt Þ  aðt Þ, where W(t) is the recorded seismic trace; Wa(t) is the factors associated with signal changes due to the influence of instrumental tracts; Wp(t) is the factors associated with signal changes due to the influence of various noise waves; Ws(t) is the factors associated with signal changes in the process of passing through medium; a(t) is the reflection coefficients associated with the properties of the real medium. The model of seismic wave field formation in a more complicated form is shown in Fig. 4.30, which shows the effect of various types (random and regular) of noise and gives an component-wise description of their influence on the seismic pulse in the form of multiplicative and additive components in convolutional expression during the wave pulse propagation in the geological subsurface environment. Based on the above model of the seismic trace, the factors included in the formula are analyzed, their contribution to the registered record is evaluated, and software and algorithmic actions are taken to compensate for noise and define environmental factors. According to the contribution of each factor, the algorithm and parameters

182

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

are selected to attenuate unwanted components. As a result of such optimal data processing, the result is a wave field of primary reflections with a low noise level and with a true ratio of reflection energies that spatially vary in proportion to the reflectivities and with the maximum possible preservation of the influence of low energy signals (weak signals). This is the main task of data preprocessing and their preparation for the stage of wave field inverting into the fields of acoustic impedance and reflectivities for their analysis, parameter prediction, and subsequent interpretation. The task of inverting the wave field is reduced to the optimal compensation of the influence of the elastic wave source—a seismic impulse, considering the factors left after preprocessing: the background of regular and irregular noises, the effects from unequal absorption in different parts of the spectrum, etc. The operator solving the direct problem is selected based on the influence significance of the remaining factors after preprocessing the seismic data. The main purpose of seismic data processing in the HRS-Geo technology is to obtain unmigrated and migrated seismic trace stacks, suitable for performing the seismic inversion procedure and further detailed geological interpretation of the results. The whole data processing must meet the following basic requirements: – Be performed with preservation of the true amplitudes ratio in a wide frequency range using surface- consistent procedures; – Provide the necessary signal-to-noise ratio (1.0), resolution, and traceability of reflecting horizons in the whole range of the section being studied, including the target seismic recording interval. A distinctive feature of the applied processing scheme is the exclusion from the preprocessing process of conversion procedures with relatively “hard” parameters (traditional deconvolution and multichannel FX-deconvolution for shot-receiver offsets and corrective filtering, distorting the dynamics of seismic record, making it unrestorable in relation to thin-layer acoustic heterogeneities of the real environment). This process is carried out by monitoring at each stage of the processing graph of the contribution of the extracted environmental parameters and the level of various kinds of noise. Another distinctive feature of the technological processing scheme is the inclusion in its structure of the seismic inversion procedure for restoring the thin-layer subsurface model and the possibility of solving the inverse dynamic seismic problem on this basis. Moreover, such a procedure is widely used for processing initial seismograms, as well as for processing final migrated sections of seismic records. From a formal point of view, the seismic inversion procedure is used instead of deconvolution procedures [6, 36, 44, 45]. Primary seismic materials are processed according to an identical special processing graph. Depending on the field survey quality, the noise level and the geological features of the study area and the graph can be adjusted and complicated. A typical application of geophysical procedures has the following sequence (Fig. 4.31): – Description, application, and control of observation geometry; – Editing of seismograms and traces; – Taking into consideration the geometric divergence;

4.3 Seismic Data Processing Using HRS-Geo Technology

183

Fig. 4.31 Typical scheme of special data processing, data inversion, and subsoil characteristic prediction according to the seismic survey and by HRS-Geo technology

– Inversion of the original seismogram traces into the traces ofeffective reflection coefficients; – Trace normalization; – First automatic static correction; – Preliminary velocity analysis; – Second automatic static correction; – Low-frequency component of static corrections; – Second stack velocity analysis; – Third static correction; – Multiple wave attenuation; – Final kinematic correction; – Residual phase shift correction; – Trace stacking according to the CDP; – Migration of the original seismograms and the finaltime sections; – Processing of stack traces; – Solution of the inverse dynamic problem in order to obtain sections and cubes of effective reflection coefficients and acoustic impedance. At the final stage of processing, pre- and post-migration processing of sections or a cube of seismic records is applied using a number of additional procedures for regularizing seismic recording (spectral whitening, filtering in various domains). The tasks of this stage are to attenuate the background of multiple waves, suppress

184

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

random noise, correct residual phase shifts due to underreporting of static and kinematic corrections at previous stages of the processing graph, etc., to solve the inverse dynamic problem in order to obtain sections and cubes of effective reflectivity and acoustic impedance. The results of processing and inversion in the form of sections and cubes of the wave field, acoustic impedance, and reflection coefficients, along with well logging data processing and additional a priori digital information, enter the “interpretation system” block (Fig. 4.31). Let us focus on the results of the application of only some but very important processing procedures, which within the framework of a special (optimal) processing graph ensure the preservation in the seismic record of both kinematic and dynamic parameters of longitudinal elastic waves on the example of real seismic data obtained on one of the Western Siberia areas. One of the distinctive features of the work area, as well as in most of the territory of Western Siberia, is the presence of a strong factor distorting the recorded wave field—multi-year frozen rocks (MYFR). It is taken into account in processing not in all cases (or performed using various techniques differing in efficiency), which often leads to significant errors in the results of structural constructions and distortions of the detailed dynamic interpretation results, as shown in Sect. 4.2 (when evaluating and comparing the results of preprocessing field gathers in various processing centers). This is primarily due to the curvilinearity factor undercounting of the interfaces and the local change in the velocities of elastic wave propagation, i.e., with high-velocity heterogeneities, which are confined, as a rule, to the upper part of the studied section (UPS). As is known, velocity inhomogeneities in the conditions of Western Siberia are mainly associated with the presence of MYFR anomalies, more precisely, with foci of multi-year frozen rocks of various thickness and varying degrees of consolidation. On the one hand, the presence of such anomalies leads to distortions of the t0(x) lines in the time sections, which are not related to the mapping of the geological features, and on the other hand, to distortions of the hyperbolic CDP hodographs. As a result, the reflecting horizons have a different configuration from reality, and with significant nonhyperbolicity of the hodographs, there is also a distortion of the reflections dynamics and even the loss of their correlation. The situation is sharply aggravated in those parts of the section under study, where the effect of the MYFR influence is superimposed on the effect of the gas cap, possibly located in the upper parts of the section. As a result, there are distortions in the formation of the desired models of objects—hydrocarbon deposits. An example taking into account the long-period static corrections compensating for the anomalies of the MYFR, confined to the upper part of the section (UPS), along the 2D profile of one of the studied areas, is shown in Fig. 4.32. The time section along this line is presented before (Fig. 4.32a) and after (Fig. 4.32b), considering the effect associated with the velocity heterogeneities of the MYFR. As seen in Fig. 4.32, the presence of MYFR in the near-surface section, which have thawing zone and thickness changes, leading to strong changes in the synphasic axes and amplitudes in the time section, has significant distortions in the whole section. In this case, the largest distorting effect of the seismic wave field is observed in the

4.3 Seismic Data Processing Using HRS-Geo Technology

185

Fig. 4.32 Fragment of a time section on the studied area before (a) and after (b) taking into account the influence of the upper part zone (UPZ)

interval of the upper horizons—in the UPS zone. Down along the depth, the distortions of velocity somewhat decrease, but at the same time, there is also a significant curvature of the CDP hodographs, expressed in their nonhyperbolicity and, as a result, the deterioration of reflector traceability and wave resolution in time sections as a whole. To compensate for the effect that creates kinematics and the dynamic distortions of the waves in the upper part of the section, a substitution method is used in area where velocity anomalies arise in the enclosing medium. Work stages consist of detecting distortion sources and their spatial mapping, determining the characteristics of the enclosing medium and local heterogeneities that cause distortions, and applying host medium parameters to the selected inhomogeneities during the processing – wave field transformation (due to substitution of local heterogeneities in the UPS). If there are well data on the upper part of the section, they are used to control the calculation correctness and the static corrections application for UPS. The method of choosing the calculating technique for UPS static corrections, the justification of the parameters, and the implementation of the appropriate data processing and interpretation to account for the anomalies of the UPS heterogeneous structure are performed using a graph that includes the following procedures: – Analysis of vertical VCDP velocity spectra and preliminary selection of reference horizons on seismic time sections; – Assessment of the quality of horizontal VCDP velocity spectra on the reference horizons and the final choice of horizons; – Assessing the possibility of accounting for velocity anomalies on selected horizons;

186

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

– The calculation of the horizontal VCDP velocity spectra over the reference horizons, their correlation, and the construction on this basis of the isochrones maps t0(x,y) along these horizons; – Beam migration of isochron map t0(x,y) surfaces and obtaining depth maps; – Allocation of zones associated with the UPS anomalies, based on the VCDP velocity maps, isochron maps constructed from sections obtained at different offsets, and time thicknesses maps along the horizons of the upper part of the section; – Mapping of interval velocities, consistent with the structural map on one of the reference horizons; – Carrying out the process of replacing the layer; – Mapping of static corrections; – Applying the received corrections into the CDP seismograms and construction of the final stacks for 2D seismic profiles. The implementation result of such a seismic data processing and interpretation graph presented in Fig. 4.32b shows a generally significant improvement in the stacking quality of reflecting horizons. Due to this correction of long-period static corrections, the reflectors are aligned (this is clearly seen in the interval of the UPS horizons) and become more associated with the behavior of lateral velocity changes in the individual layer—this is observed in the VCDP velocity spectra obtained directly from the seismograms with the long-period corrections entered for UPS. After entering the corrections and obtaining the final time sections, the residuals between the reflecting horizons on the 2D lines of the studied area should be no more than 2 ms. In general, due to the elimination of the non-hyperbolicity of the CDP hodographs, the geological information content of seismic data increased, which ultimately made it possible to match the seismic images in the upper part of the sections to the true geometry of the target reflecting horizons located in the lower part of the considered time section (Fig. 4.32b). As an example of suppressing the intense background of multiples, which makes it difficult to analyze the structure of the reflected wave field in almost the entire recorded time interval (including the target interval of the section under study), in Fig. 4.33, the CDP seismograms and the vertical VCDP velocity spectra are presented before (a) and after (b) multiple suppression. At the same time, on the CDP seismogram as a result of subtraction, multiple waves were significantly attenuated over the entire recording time interval, which increased the signal-to-noise ratio and resulted in improved traceability of primary reflections for applying the procedure for residual static correction and more reliable determination of stacking velocity. A significant simplification of the wave field pattern after subtracting multiple waves should be noted. One can see the uniqueness of the primary reflection selection in the target interval, the simplification of the velocity spectrum, in which, along with the suppression of overgrowths associated with multiple waves, the extremes corresponding to the primary reflections are localized.

4.3 Seismic Data Processing Using HRS-Geo Technology

187

Fig. 4.33 Fragment of vertical velocity analysis before (a) and after (b) multiple wave subtraction

One of the last actions in the processing graph is the procedure for seismic migration of the stack sections, which makes it possible to significantly refine the geometry of the reflectors in tracing time interval. Through the use of this procedure, the fault tracing is significantly improved. Therefore, on Fig. 4.34 is a fragment of one of the time sections before (Fig. 4.34a) and after (Fig. 4.34b) the application of the procedure for migration of the final stack. At the same time, as a basic velocity model of the environment when creating a velocity migration file, the stack velocity VCDP were used. In order to exclude anomalies affecting the efficiency of migration results, these velocities were previously interpolated and smoothed by a five-point operator—a second-degree polynomial. According to the results of testing, the traces used in the migration procedure constitute 95% of the smoothed VCDP velocities. The algorithm of finite-difference one-pass migration based on the solution of the Kirchhoff wave equation in the spatial-frequency (f-x) domain was used directly to implement this process. In particular, a modification of the migration transform was chosen, which carries out the seismic record migration with inclination angles up to 65 .

188

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.34 Time section fragments comparison before (a) and after (b) application of the migration procedure

The special processing graph used in this way at the stage of seismic data preprocessing allows saving the seismic record dynamics to the maximum extent. As noted above, in this processing graph, only those procedures were used that allow one to obtain a recoverable seismic recording dynamics that is adequate to the actual distribution of the acoustic heterogeneities of the section being studied. In its composition the seismic inversion procedure of the original seismograms was applied. This procedure allowed to maximize the seismic record spectrum band, significantly increase its resolution, and regularize the dynamic parameters of the recording. The resulting high-quality migrated seismic sections served as the basis for performing HRS interpretation of seismic data using the interpretation system when building maps for various purposes. This circumstance is very relevant when predicting the seismic data of the most important geological indicators for exploration and industrial prospecting, primarily such as lithological composition, reservoir properties and the nature and degree of reservoir rock fluid saturation. Figure 4.35 provides an example comparing the results of optimal and standard field seismic data processing [46]. The criterion for assessing and selecting the processing version was the relative acoustic impedance obtained for each case study. From the fragments comparison of the AI section, in particular, it can be seen that in the variant with the optimal (special) data processing, a thin-layer section with thicknesses of 1–4 ms layers (Fig. 4.35b) clearly appeared, while on the results

4.3 Seismic Data Processing Using HRS-Geo Technology

189

Fig. 4.35 Example of selecting a seismic data processing special graph to a minimum of distorting the seismic record dynamics: (a) a fragment of a time section at optimal processing, (b) restored from (a) acoustic impedance section; (c) fragment of time section at standard data processing, (d) restored from (c) acoustic impedance section

of the alternative processing, this thin-layer section stands out with significant distortion (Fig. 4.35d). In the latter version of the smoothing procedure with more rigid parameters (Fig. 4.35c), the contribution of thin layers to the wave field was sharply reduced. The formation of weak-reflected signals is also associated with the boundaries of these layers—if not optimally processed, these signals are suppressed (filtered), and upon further transformation, the possibility of their restoration and use disappears when interpreting and predicting the geological and geophysical characteristics of the target objects. It should be noted that the inclusion of additional procedures in the standard seismic data processing graph, and especially the repeated use of zero-phase and multichannel FX-deconvolution procedures, leads to very strong distortions of the reconstructed acoustic impedance curves. It should be noted that the parameters for the recovery programs for the AI curves, both in the case in Fig. 4.35b and in the case in Fig. 4.35d, remained unchanged. Without dwelling on all the details of such a conversion, we emphasize only one characteristic feature—in the interval 178–198 sections in the time range t0 ¼ 1515–1610 ms, there is a very thin layer in 1–2 time samples with increased AI values of a discontinuous type against the background of a more thick layer with low AI. This layer (indicated by arrows) in the first case is fixed with optimal tracking (Fig. 4.35b) and in the second, as strongly distorted (Fig. 4.35d). Figure 4.36 shows fragments of the original wave field (Fig. 4.36a) obtained as a result of special processing, and a section of reflection coefficients obtained as a

190

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

Fig. 4.36 Fragments of wave field (a) and reflection coefficients (b) with interpretation of tectonic faults and reflectors for perspective formations

4.4 Summary

191

result of solving an inverse dynamic problem (Fig. 4.36b) with the interpretation of tectonic faults and horizons of perspective layers. The reflectivity distribution represents the thin-layer model—the detail (vertical and lateral resolution) of the data has improved by about an order of magnitude. On these sections, free from interference, the details of practically all the structures that have manifested themselves and the internal geological structure of all strata and prospective objects are clearly manifested. Thus, the obtained materials show that the problem of identifying reliable weak seismic signals is possible both in favorable and in relatively unfavorable conditions for seismic exploration. Efficiently enough, it is solved on the basis of the selection and use of a special (optimal) seismic data processing graph, ensuring that the recording of identification information about the components of the geological section under study and the methods for solving IDSP, i.e., inversion (such as in the HRS-Geo technology), realizing the restoration of the acoustic model of a real environment with the accuracy of the seismic record discretization step over time.

4.4

Summary

An analog of the direct search method, such as the zero-order method (the HookeJeeves method), is used to find a solution to the IDSP. To find the minimum of the residual in this method, it is not necessary to calculate the derivatives of the objective function. The advantages of this method also consist in the fact that the objective function can be quite complex (it can also have discontinuous derivatives), not have an explicit representation, but can be set as a system of equations. The optimization of the solution consists in the fact that in this system (the vector of objective functions), various types of residuals between real and model data are iteratively calculated. At the same time, model studies of the algorithm for inversion seismic traces generally showed good results. Different graphs of processing real seismic data were previously performed by different geophysical companies. Their subsequent inversion into an acoustic model of the real environment was performed by the authors using the procedures of the HRS-Geo technology. In total, 12 variants of the results of preliminary data processing of the same seismic survey profile were tested. The obtained results were subjected to qualitative (visual) and quantitative assessments. In general, the results of processing real materials and quantitative estimates of the results of their inversion in the form of numerous corresponding tables, summary figures, and diagrams are presented. A schematic model of a seismic record is considered, showing what signals (useful and noise) one has to deal with in the processing and interpreting seismic data. It is shown how, against the background of the useful part of the seismic record,

192

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

non-useful waves and noises manifest themselves, which have to be “dealt with” in the process of preprocessing the materials of seismic observations. The distorting role of seismic interference, which can be eliminated only with the help of IDSP solution procedures, is emphasized (with regard to the adequate restoration of the detailed internal structure of the studied thin-layer model of the medium).

References 1. Trofimov, V., Khaziev, F., & Trofimova, A. (2018). Tekhnologiya VRS-Geo. Izucheniye nefteperspektivnykh obyektov metodom vysokorazreshayushchey seysmiki (HRS-Geo technology. Study of oil-prospective objects by the method of high-resolution seismic). Oil & Gas Journal Russia, 1–2(123), 28–35. 2. Khaziev, F. F., & Trofimov, V. L. (2003). Model’nyye issledovaniya rezul’tatov resheniya obratnoy dinamicheskoy zadachi seysmiki (Model studies of the solving inverse dynamic seismic problem results). Spetsial’nyy vypusk Geofizika: Tekhnologii seysmorazvedki-I I (Geophysics, special edition of “Seismic Technologies”). pp. 27–37. 3. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2011). Otsenka vklada elementarnykh granits i tolshch v seysmicheskoye volnovoye pole dlya mnogosloynykh pogloshchayushchikh sred (Assessment of the elementary boundaries and strata contribution to the seismic wave field in multilayer absorbing media). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 86–96. 4. Trofimov, V. L., Milashin, V. A., Khaziev, F. F. et al. (2004). Resheniye zadach neftyanoy geologii v razlichnykh rayonakh Zapadnoy Sibiri metodami vysokorazreshayushchey seysmiki (Solving the problems of oil geology in various regions of Western Siberia by high-resolution seismic methods): VII Scientific-Practical Conference “Ways of realizing the oil and gas potential of the Khanty-Mansiysk Autonomous Okrug”. pp. 26–45. 5. Trofimov, V. L., Milashin, V. A., Kachkin, A. A., Timonin, A. B., Khaziev, F. F., & Mal’tsev, G. A. (2005). Formirovaniye tonkosloistoy geologicheskoy modeli yurskikh otlozheniy na Zapadno-Tugrovskom uchastke KHMAO metodami vysokorazreshayushchey seysmiki (Formation of a thin-layered geological model of Jurassic sediments in the West Tugrovsk area of the KMAO by high-resolution seismic methods). Geofizika: Tekhnologii seysmorazvedki (Geophysics: Seismic Technologies), 2, 25–36. 6. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., et al. (2009). Spetsial’naya obrabotka i interpretatsiya dannykh seysmicheskikh nablyudeniy v slozhnykh geologicheskikh usloviyakh metodom vyokorazreshayushchey seysmiki (Special processing and interpretation of seismic observation data in difficult geological conditions by the method of high-resolution seismics). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 36–50. 7. Glebov, A. F. (2006). Geologo-matematicheskoye modelirovaniye neftyanogo rezervuara: ot seysmiki do geoflyuidodinamiki (Geological and mathematical modeling of an oil reservoir: from seismic to geofluidodynamics). (p. 344). Nauchnyy, M.. 8. Kaufman, A. A., & Levshin, A. L. (2006). Vvedeniye v teoriyu geofizicheskikh metodov. Chast’ 5. Akusticheskiye i uprugiye volnovyye polya v geofizike (Introduction to the theory of geophysical methods. Part 5. Acoustic and elastic wave fields in geophysics) (p. 663). Nedra. 9. Kozlov Ye, A. (2006). Modeli sredy v razvedochnoy seysmologii (Models of medium in exploration seismology). Tver (p. 480). GERS. 10. Petrashen, G. I. (1978). Osnovy matematicheskoy teorii rasprostraneniya uprugikh voln (Mathematical theory foundation of elastic wave propagation). Book: “Voprosy dinamicheskoy teorii

References

193

rasprostraneniya seysmicheskikh voln” (“Dynamic theory questions of seismic wave propagation”). Release XVIII, L., Nauka. p. 248. 11. Petrashen, G. I. (1980). Rasprostraneniye voln v anizotropnykh uprugikh sredakh (Wave propagation in anisotropic elastic media). L., Nauka. p. 280. 12. Khaziev, F. F., Trofimov, V. L., & Shkol’nik, S. A. (2014). Kolichestvennaya otsenka vklada geologicheskikh pokazateley v interferentsionnuyu seysmicheskuyu zapis’ i yeye psevdoakusticheskiye preobrazovaniya (Quantitative assessment of the geological indicators contribution to the interference seismic record and its pseudoacoustic transformations). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 70–83. 13. Vychislitel’nyye matematika i tekhnika v razvedochnoy geofizike. (1990). (Computational mathematics and technique in exploration geophysics). Spravochnik geofizika. Pod red. V.I. Dmitriyeva (Geophysicist handbook edited by V.I.Dmitriyeva). Second edition. M., Nedra. p. 498. 14. Romanov, V. G. (1984). Obratnyye zadachi matematicheskoy fiziki (Inverse problems of mathematical physics). M., Nauka. p. 264. 15. Tikhonov, A. N., & Arsenin Ya, V. (1974). Metody resheniya nekorrektnykh zadach (Methods for solving incorrect problems). M. p. 223. 16. Yagola, A. G. (2014). Obratnyye zadachi i metody ikh resheniya (Inverse problems and methods for their solution). Prilozheniya k geofizike (Elektronnyy rakurs) Yagola A.G., Van Yanfey Stepanova I.E., Titarenko V.N. 2BINOM. Laboratoriya znaniy (Knowledge laboratory), p. 216. 17. Yanovskaya, T. B., Prokhorova, L. N. (2004). Obratnyye zadachi geofiziki (Inverse problems of geophysics). Uchebnoye posobiye (Tutorial). Izd. S.-Peterburgskogo universiteta. p. 214. 18. Trifonov A.G. Postanovka zadachi optimizatsii i chislennyye metody yeye resheniya (Statement of the optimization problem and numerical methods for its solution). Matematika \OptimizationToolbox. Retrieved on December 11 from http://matlab.exponenta.ru/optimiz/ book_2/index.php 19. Aoki, M. (1977). Vvedeniye v metody optimizatsii (Introduction to optimization methods). M., Nauka. p. 344. 20. Trifonov, A. G. 2012. Mnogokriterial’naya optimizatsiya (Multi-criteria optimization). Optimization Toolbox 2.2. Rukovodstvo pol’zovatelya (User Guide). Retrieved on December 11 from http://matlab.exponenta.ru/optimiz/book_1/16.php 21. Gembicki, F. W. (1974). Vector Optimization for Control with Performance and Parameter Sensitivity Indices. Ph.D. Thesis, Case Western Reserve University. 22. Gogonenkov, G. N. (1987). Izucheniye detal’nogo stroyeniya osadochnykh tolshch seysmorazvedkoy (The detailed sedimentary strata structure study by seismic exploration) (p. 221). Nedra. 23. Kanasevich, E. R. (1985). Analiz vremennykh posledovatel’nostey v geofizike (Analysis of time sequences in geophysics) (p. 300). Nedra. 24. Polshkov, M. K., Kozlov Ye, A., & Meshbey, V. I. (1984). Sistemy registratsii i obrabotki dannykh seysmorazvedki (Seismic data registration and processing systems). M., Nedra. p. 381. 25. Trofimov, V. L., Khaziev, F. F. (1991). Modelirovaniye volnovykh poley dlya mnogosloynykh pogloshchayushchikh sred s otsenkoy vklada elementarnykh granits i tolshch (Modeling of wave fields for multilayer absorbing media with an assessment of the contribution of elementary boundaries and strata). Study of the Pripyat trough deep structure by methods of exploration geophysics: collection of BelNIGRI scientific papers. pp. 3–14. 26. Barkov Yu, A., Shteyn Ya, I., Yakovlev, I. V., & Grechishnikova, T. A. (2008). Pereobrabotka dannykh 3D-seysmorazvedki dlya povysheniya nadozhnosti interpretatsii i vyyavleniya osobennostey geologicheskogo stroyeniya (Re-processing of 3D seismic data to improve the reliability of interpretation and identification of the geological structure features). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 38–42.

194

4 Inverse Dynamic Seismic Problem Solution in the HRS-Geo Technology

27. P’yankov, A. A. (2013). Otsenka razreshayushchey sposobnosti seysmicheskikh izobrazheniy na osnove primeneniya novogo atributa (Estimation of the seismic images resolution based on the application of a new attribute). Sovremennyye problemy nauki i obrazovaniya (Modern problems of science and education). № 6. Retrieved on July 22, 2018 from http://www.scienceeducation.ru/ru/article/view?id=11280 28. Sheriff, R., & Geldart, L. (1987). Seysmorazvedka (Seismic exploration). Tom 1 (448 pages), Tom 2 (400 pages). M., Mir. 29. Cordsen, A., Galbraith, M., & Peirce, J.. Planning Land 3-D Seismic Surveys. Geophysical Developments № 9, SEG, ISBN: 978–1–56, 080-089-7. 30. Denham, L. R. (1980). What is horizontal resolution? Presented at the Ann. Mtg., Can. Soc. Expl. Geophys. 31. Trofimov, V. L., Khaziev, F. F., & Milashin, V. A. (2012). Dinamicheskiye kharakteristiki otrazhennykh voln s uchetom vklada elementarnykh granits i tolshch (Dynamic characteristics of reflected waves taking into account the contribution of elementary boundaries and strata). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 12–24. 32. Claerbout, J. F. (1985). Imaging the earth’s interior (Vol. 18). Blackwell Publications. 33. Ebrom, D., Li, X., McDonald, J., & Lu, L. (1995). Bin spacing in land 3-D seismic surveys and horizontal resolution in time slices. The Leading Edge, 14. pp 37–40. 34. Sil’via, M. T., & Robinson, E. A. (1983) Obratnaya fil’tratsiya geofizicheskikh vremennykh ryadov pri razvedke na neft’ i gaz (Inverse filtering of geophysical time series in oil and gas exploration). M., Nedra. p. 247. 35. Belousov, A. V. (2011). Standartnyye otsenki kachestva polevogo seysmicheskogo materiala (Standard assessments of the field seismic material quality). Pribory i sistemy razvedochnoy geofiziki (Devices and systems of exploration geophysics). № 3. pp. 31–36. 36. Trofimov, V. L., Khaziev, F. F., Milashin, V. A., et al. (2007). Avtomatizirovannaya obrabotka i interpretatsiya dannykh GIS dlya obnaruzheniya nefteperspektivnykh obyektov metodami vysokorazreshayushchey seysmiki (Automated processing and interpretation of well logging data for the detection of oil-prospective objects by high-resolution seismic methods). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 54–66. 37. Trofimov, V. L., & Khaziev, F. F. (2011). Izucheniye vliyaniya sostava i svoystv porod na geologo-geofizicheskiye parametry nefteperspektivnykh otlozheniy (Study of the influence of the composition and properties of rocks on the geological and geophysical parameters of oilpromising deposits). Tekhnologii seysmorazvedki (Seismic Technologies), 1, 22–33. 38. Seysmorazvedka: Spravochnik geofizika. (1990). (Seismic exploration: Geophysics handbook). Tom 2 edited by Nomokanov. M., Nedra. p. 400. 39. Akimov, P. S., Yevstratov, F. F., Zakharov, S. I., et al. (1989). Obnaruzheniye radiosignalov (Detection of radio signals). In A. A. Kolosov (Ed.), Radio i svyaz’ (Radio and communications) (p. 288). 40. Milashin, V. A., Trofimov, V. L., & Khaziev, F. F. (2004). Vydeleniye slabykh signalov metodom seysmicheskoy inversii (Weak signals detection by the method of seismic inversion). VII Scientific- Practical Conference “Ways of realizing the oil and gas potential of the KhantyMansiysk Autonomous Okrug”. t. 2. pp. 295–307. 41. Berezkin, V. M., Kirichek, M. A., & Kunarev A.A. (1978). Primeneniye geofizicheskikh metodov razvedki dlya pryamykh poiskov mestorozhdeniy nefti i gaza (Application of geophysical exploration methods for direct exploration of oil and gas fields). Nedra. p. 224. 42. Kuznetsov, O. L., Petukhov, A. V., Zor’kin, L. M., Zubayrayev, S. L., Kirichek, M. A., & Popsuy-Shapko, G. P. (1986). Fiziko-khimicheskiye osnovy pryamykh poiskov zalezhey nefti i gaza (Physical and chemical foundations of direct prospecting for oil and gas deposits) (p. 336). Nedra.

References

195

43. Mandel’baum, M. M., Puzyrev, N. N., Rykhlinskiy, N. I., Surkov, V. S., & Trofimuk, A. A. (1988). Pryamoy poisk uglevodorodov geofizicheskimi metodami (Direct search for hydrocarbons by geophysical methods). Akademicheskiye chteniya AN SSSR (Academic readings of the USSR Academy of Sciences). M., Nauka. p. 160. 44. Trofimov, V. L., & Khaziev, F. F. (2002). Kolichestvennyy prognoz veshchestvennogo sostava i neftegazonosnosti poristykh fatsiy metodami vysokorazreshayushchey seysmiki (Quantitative prediction of material composition and oil and gas content of porous facies using highresolution seismic methods): Geofizika: Tekhnologii seysmorazvedki – 1 (Geophysics, special edition of “Seismic Technologies”). pp. 130–141. 45. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2008). Opredeleniye geologogeofizicheskikh parametrov real’noy sredy metodom vysokorazreshayushchey seysmiki (Determination of geological and geophysical parameters of the real medium by the high-resolution seismic method). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 25–30. 46. Metodicheskiye rekomendatsii po ispol’zovaniyu dannykh seysmorazvedki dlya podscheta zapasov uglevodorodov v usloviyakh karbonatnykh porod s poristost’yu treshchinnokavernoznogo tipa. (2010). (Methodological recommendations on the use of seismic data for calculating hydrocarbon reserves in carbonate rocks with fractured-cavernous porosity). Edited by V.B.Levyant. M., OAO «TSGE».

Chapter 5

Processing and Automated Interpretation of Well Logging Data

Abstract The features of the methodology for processing and interpreting the data of standard well logging methods are considered. This means implements a method of geophysical parameters’ functional transformations by integrating them into information systems. In addition to the continuity of data processing and analysis, this ensures the quantitative interconnection of all geophysical parameters based on their direct relationships, as well as the integrated use of data and the results of solving individual problems. The physical prerequisite for solving such problems is the various sensitivities of different geophysical parameters to changes in lithological and reservoir properties. When normalizing the readings of individual methods in rocks of one type, according to the lithology and nature of the fluid, there is a characteristic discrepancy in the indications for rocks of another type. The results obtained are used in the study of the thin-layer internal structure of prospective reservoirs and the prediction of various geological and geophysical parameters by combining well data and high-resolution seismic materials using HRS-Geo technology software tools. The results of processing the vertical seismic profiling (VSP) data by the polarization method (PM VSP), obtained on the basis of the development and use of the SKOR software package for determining the velocity characteristics of various wave types (longitudinal, transverse, exchange), effective elastic-deformation parameters, and the stress state of the real geoenvironment in situ conditions, are presented. A method for studying the stress state of rocks in the areas of possible earthquakes is proposed, which is based on the use of kinematic and dynamic parameters of the seismic wave field obtained using one of the modifications of the seismic exploration polarization method (PM)—its borehole modification of vertical seismic profiling (PM VSP). This method of observation is supplemented by special regime (monitoring) measurements in the well and specialized processing and interpretation of the obtained survey materials.

After the seismic record was released from the wave pulse to determine the elastic parameters of the medium, such as acoustic impedance and reflection coefficients, the next that is important for the solution of complex seismic studies are borehole ones using these methods of standard well logging, providing the definition of geological and geophysical parameters of the medium (such as lithology, porosity, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_5

197

198

5 Processing and Automated Interpretation of Well Logging Data

nature and degree of reservoir saturation, etc.). It should be noted that the existing ambiguity in determining such parameters of the environment in the context of each of the wells using borehole measurement methods can be represented as a special “interference” of different characteristics (geological indicators) of the environment. Due to the presence of several independent methods of well measurements, their different sensitivity and direction of the parameter influence the presence of a priori information (drilling data, core analysis results, etc.), it is possible to resolve ambiguities and determine the parameters with satisfactory accuracy. The difficulties encountered when linking well and seismic data are noted above (Sect. 4.3). The following subsection addresses processing and automated interpretation of standard well logging (GBS) and vertical seismic profiling (VSP) data. The software and methodical implementation of such research directions was carried out by the authors in the form of software packages—interpretative systems that are part of the HRS-Geo technology as integral elements [1–3].

5.1

Physical and Geological Rationale for the Study of Sections According to Well Data

The creation of thin-layer geological models of hydrocarbon deposits from highresolution seismic data involves the use of at least two methods for studying real geological media. On the one hand, the method provides information on the geological structure of the real environment, obtained from seismic data (see Chap. 4 and [4, 5]) and, on the other hand, methods that allow the study of sections based on geophysical data well surveys (GBS) [4, 6, 7]. The use of these methods, both separately and in combining the data of these methods (HRS-Geo and GBS) for the purpose of predicting the composition, properties, and nature of oil-bearing strata saturation, is quite complex and deserves more detailed consideration with the inclusion of actual observational materials and experimental results. In addition to software and methodological developments, the authors have carried out research on their use on factual materials obtained in various regions. Some examples of the application of the high-resolution seismic method, its specific implementation—the HRS-Geo technology—for the conditions of the Volga-Ural province, Western Siberia, and other regions are given in Chaps. 8 and 9 and in a number of works [1, 8–10]. In this subsection, we will dwell in greater detail on developments related mainly to the processing and automated interpretation of GBS data, which are an integral part of the complex process of predicting the lithological composition, reservoir properties, and oil and gas saturation from seismic data. The issues of physical and geological substantiation of the section study based on GBS data using the method of functional transformations of geophysical parameters and their integration into information systems are briefly covered [4, 6, 7]. The results are then directly used in solving the problem of structural mapping of reflecting horizons on materials 2D

5.1 Physical and Geological Rationale for the Study of Sections According. . .

199

and 3D CDP surveys of effective acoustic impedance (AI) and reflection coefficients (RC). These results are also widely used in the study of the thin-layer internal structure of promising layers and the prediction of various geological and geophysical parameters by integrating well and high-resolution seismic data using software tools of the HRS-Geo technology. Determining the composition and rock properties of oil perspective strata according to well logging data is necessary for the formation of a detailed geoacoustic model of the medium containing information on the distribution of acoustic velocities, density, lithology, reservoir properties, and oil and gas saturation of the strata reservoir rocks in the studied interval [1, 4, 8, 9]. Specifically, this model is used at the stage of integrating the results of solving an inverse dynamic problem of seismic and GBS due to the adequate convergence of high-resolution seismic data—the results of seismic inversion and materials of well field geophysics, bringing the adequate scale measurements of seismic and GBS to the level of the ability to study the physical, facial, and structural features of the geological section for relatively small sizes. One of the most effective ways of interpreting well logging data obtained in wells is a method of functional transformations of geophysical parameters that are integrated into information systems. The program algorithm of the functional geophysical parameter transformation system is based on the method of such conversions [6, 7]. As noted by the authors [6, 7], the expediency of continuous processing follows from the continuous nature of the sedimentation process and the structure of geological sections. In addition to the continuity of processing and analyzing data, this method ensures the quantitative interconnection of all geophysical parameters on the basis of their direct relations, as well as the integrated use of data and the results of solving individual tasks. The physical prerequisite for solving such problems is various sensitivity of different geophysical parameters to changes in lithological, reservoir, and fluid properties. Using the property of different influence of GBS methods to the same parameters of the section, it is possible to construct normalized measurement curves. For example, when normalizing the indications of certain methods in rocks of one type according to lithology and the nature of the fluid, a characteristic divergence of indications for rocks of another type is observed. The effect of a specific property is revealed by sequential compensation of distorting factors by means of a comparative analysis of normalized diagrams of various methods in certain combinations, after which the result is calibrated to the value of the desired parameter, corrections are introduced and evaluated quantitatively in the appropriate dimension. When integrating data from a significant number of methods, a whole complex of rock characteristics is determined. Based on the set of parameters used, located in a particular system, the material composition, porosity, content of bound water, and useful capacity are established; oil and gas saturation of reservoirs is estimated; a number of very important physical characteristics of geological sections are calculated. There are extensive publications on the method of functional transformations based on the integration of geophysical data [6, 7].

200

5 Processing and Automated Interpretation of Well Logging Data

For the joint integrated use of well data (GBS) and high-resolution seismic materials—the HRS-Geo technology—an interpreting well processing system (WPS) was created, which is based on the principles of continuous functional data transformations for studying well sections. In this system, there is a software and technology unit, on the basis of which an optimal coordination of the physical parameters, extracted from the log data, with the corresponding high-resolution seismic ones is performed. Let us briefly discuss the main stages of the automated processing of GBS data using WPS subsystem of the HRS-Geo technology.

5.1.1

Lithological Composition Determination

The main initial data for the lithological stratification, as is known, are the methods of SN (sonic log) and NL (neutron log). The lithology definition is based on the different contributions of rocks to SN and NL measurements. In addition to lithology, the porosity has a significant influence on the readings of these methods. To determine the lithological feature, it is necessary to exclude the effect of porosity. For this, the SN and NL curves are reduced to the same sensitivity scale, in particular, to porosity, and the difference between the transformed curves is calculated. SN and NL curves are related to porosity by different dependencies. To ensure the same scale of porosity, these curves must be functionally transformed and reduced to a form that satisfies the equations: SN N ¼ K p þ LSN ,

ð5:1Þ

NLN ¼ K p þ LNL ,

ð5:2Þ

where SNN and NLN are the normalized porosity curves SN and NL and Kp is the porosity. Calculating the difference between Eqs. (5.1) and (5.2), we obtain an equation that determines the dependence of the transformed curves on the lithological characteristic: SN N þ NLN ¼ ΔL þ C,

ð5:3Þ

where C is the constant and ΔL is the lithological characteristic in units of specific time, [μs/m]. In relation to the limestone, the parameter ΔL has the values given in the table: Lithology Dolomite Anhydride Sandstone Salt Clay, marls

ΔL, μs/m 30 20 40 70–90 0–10

5.1 Physical and Geological Rationale for the Study of Sections According. . .

201

A change in the lithological component leads to a proportional change in the parameter ΔL. On this basis, the quantitative ratio of lithological components is determined. Standard SN and NL logging methods uniquely identify two-component mixtures of the form: limestone + dolomite, sandstone + clay, and limestone + anhydrite. Three-component mixtures and other uncertainties of the lithological composition are resolved using other methods and a priori information. For example, according to the data of the SN + GGL complex, there are uncertainties that remain when using only SN + NL (against the background of the combined normalized (in ΔT) SN and GGL curves in the clay intervals, the magnitude of the divergence of the ΔT and GGL values changes proportionally to the volume content of the clay material).

5.1.2

Porosity Determination

The methods of SN (sonic), NL (neutron), GR (gamma ray), and SP (spontaneous potential) are used to estimate the porosity. The total porosity is determined from the SN and NL curves. Normalization of the SN and NL curves in lithology makes it possible to exclude the influence of the rock matrix. The SN and NL curves normalized by lithology are presented in the form: SN N ¼ K po þ L,

ð5:4Þ

NLN ¼ K po  L,

ð5:5Þ

where Kpo is the total porosity and L is the contribution of the lithologic factor. Summation of Eqs. (5.4) and (5.5) allows to exclude the influence of lithology and determine Kpo: K po ¼ ðSN N þ NLN Þ=2 þ C,

ð5:6Þ

where C is a constant. For terrigenous rocks, when determining the effective porosity, it is necessary to make a correction for the pores occupied by bound water according to the formula: K p:eff ¼ K po  K p:bn ,

ð5:7Þ

where Kp.eff is the effective porosity and Kp.bn is the porosity occupied by the bound water. To determine Kp.eff, GR and SP methods are used. In this case, the GR curve is normalized and compared with the SP curve for dense and clayey rocks. When determining the open porosity Kpo (formed by open pores that link with each other and constitute a single pore system), the formula for fully or partially hydrophilic reservoirs is used:

202

5 Processing and Automated Interpretation of Well Logging Data

K po ¼ K p:eff: =ð1  K wr Þ,

ð5:8Þ

where Kwr is the residual water saturation coefficient, defined as the difference between open and effective porosity.

5.1.3

Oil and Gas Saturation Determination

To determine the oil and gas pore saturation, curves of the total porosity Kpo and electrical resistance of the rock ρp are used, i.e., basic methods needed are SN (sonic), NL (neutron), GR (gamma ray), SP (spontaneous potential), LL (lateral log), and IL (induction log). The basis for calculating oil saturation is the change in fluid resistance. The Kpo and ρp curves are normalized by a nonlinear law and are combined over the water saturated interval, and their difference is found. The combination of the curves can be carried out along the clay interval with a shift by the difference in resistance of bound and formation waters. Implemented formulas are presented in the form:
 K n po ¼ A  log K po þ B,
 ρn p ¼ C  log ρp þ D, ΔH ¼ K

n

po

 ρ p, n

ð5:9Þ ð5:10Þ ð5:11Þ

where Knpo and ρnп are the normalized curves of total porosity and resistance, respectively; A, B, C, and D are the coefficients; and ΔH is a sign of fluid saturation. When processing well log data in the studied intervals of the oil or gas section, a significant difference ΔН appears in the normalized curves, due to an increase in fluid resistance. The magnitude of the difference in this case will be proportional to the amount of oil and gas, which can be quantified.

5.1.4

Evaluation of the Reservoir Filtration Properties

In the studied intervals of the section, there are separate permeable interlayers with a complex structure of the pore space, which are characterized by different filtrationcapacitive characteristics. To explore these characteristics, the quantitative definition provides for the calculation of the coefficients of absolute (physical) and phase permeabilities for water and oil (graphs ka, kw, ko). In the developed calculation system of the ka, kw, and ko values provides the possibility of solving the problem in two versions: (1) model representations of the permeability coefficient dependence on a set of known parameters (including porosity) and in the lack of specific data to determine the corresponding petrophysical dependencies “core versus GBS” and

5.1 Physical and Geological Rationale for the Study of Sections According. . .

203

(2) the availability of appropriate data “core versus GBS,” providing the study of lithological-petrographic parameters and filtration-capacitive properties (FCP) of productive intervals for a particular research area. It should be noted that the last of the variants of calculations was prepared by the authors and applied to the actual data. Some of the features of such estimates of the ka, kw, and ko used in these two approaches to these calculations are the following. (1) In the first version of the calculations, the rock permeability is estimated using the Kozeni-Karman capillary model equation, which relates the porosity, specific surface area, and permeability of the medium [11]. According to this model, the permeability of dry gas-saturated rocks, as is known, is a power dependence on the average diameter of single-sized grains with cubic packing and increases with increasing porosity coefficient. Such a model satisfactorily describes real porous media in cases where the shape of the pore channels (Kozeni constant) is not too different from the experimentally known forms, such as a circle, an ellipse and a rectangle (with certain ratios of semi-axes and sides), square, a slot with parallel walls, an equilateral triangle, and a slit with coaxial walls. If the porous medium will have a wider range of pore sizes, the Kozeni-Karman model will give a significant error, requiring corrections. The results obtained without tuning to the FCP laboratory data of a specific core material are only some qualitative (or relative) characteristic, since it is often impossible to estimate the structure of the pore space of the studied natural system in advance and its effect on the reliability of the obtained results. In addition, it is known that in real rocks, characterized by significant tortuosity of the pore channels, the presence of dead pores, clay cement, and a significant range of changes in pore sizes and the interaction of reservoir fluids with rock matrix, filtering of liquid or gas, are usually not all over the pore space. The filtering volume, other things being equal, may also vary depending on the depression at which the facility is operated. With such a volume of pore space and the corresponding filtration surface (Sf), as is known, the permeability coefficient is most closely related [12]. (2) In the second version of calculating the values of the ka, kw, and ko, namely, if there are relevant specific materials “core versus GBS,” the results of the core research for a number of wells are used. Below is an example of calculations by the authors for one of the well-known oilfields in the Shaim region. The calculation was made using WPS software. In determining the possible reservoir FCP threshold values of the productive intervals (sediments of the Tyumen formation (T1, horizon Т1(Ю2); T2, horizon Т2(Ю3 + Ю4); T3, horizon Т3(Ю5)), the hydrodynamic studies were also used, which were obtained when testing wells. The formation permeability in the nearwellbore zone is calculated from them. On this basis, graphs of reservoir productivity versus permeability coefficient η ¼ f(Ka) are compiled, and its limit values are determined. According to the laboratory core data, the petrophysical method was used to determine the limit values of the filtration-capacitive properties (FCP), which were used in practical calculations of the characteristics: ka, kw, and ko. In particular, the FCP threshold values are established through the limit values of the effective

204

5 Processing and Automated Interpretation of Well Logging Data

porosity (Kp.eff) (this porosity, as is known, determines the volume of the mobile fluid, starting from which the reservoir will “give up” the formation fluid). When plotting the dependences Kp. eff ¼ f (Kwr) on specific values of the Kwr parameter, the limit values of the effective porosity are determined, which are equal to Kp. eff. lm ¼ 3.4% for the T1 reservoir and Kp. eff. lm ¼ 3.6% for the T2–T3 reservoirs (where Kwr is the residual water saturation coefficient, the limit values of which for the T1 and T2–T3 layers are 78% and 76%, respectively; the division into classes is based on the interpretation of GBS materials). The limit values for open porosity Kp. lm was defined on the values of Kp. eff. lm using dependency Kp. eff ¼ f (Kp), which implies that Kp. lm ¼ 15% for the layer T1 and Kp. bn ¼ 14.9% for formations T2–T3 (open porosity Kp, as is known, is characterized by a set of pores in the mineral rock matrix, interconnected together, i.e., it is quantitatively defined as the ratio of pore volume to sample volume). When estimating the boundary values of absolute permeability, the dependences ka ¼ f(Kp) were used (graphs of such dependencies were plotted in coordinates Kp and lg ka, and function lg ka with Kp was defined using regression analysis). The limit values of absolute permeability for the T1 and T2 formations (with the above threshold values of porosity Kp. lm) are 0.44  103 μm2 and 0.5  103 μm2, respectively. The correlation equations found for the dependence of the absolute permeability on porosity are presented in the form: (a) For the Т1 reservoir: ka ¼ 0,0000125℮0.7Kp with the correlation coefficient r ¼ 0.70. (b) For the Т2 reservoir: ka ¼ 7.3107℮0.9Kp with the correlation coefficient r ¼ 0.79. The statistical dependences ka ¼ f(Kp), refined on this basis for specific geological and geophysical conditions, were used as the foundation for the development of a software module that was directly used in the process of automated continuous processing and interpretation of logging curves. To determine the values of phase permeabilities for water kw and for oil ko, we used the results of laboratory determination of residual oil saturation and oil displacement coefficient by water, as well as the results of experimental measurement of relative phase permeabilities for oil and water during stationary filtration. The calculations took into account the depth of core samples; a brief description of lithology; conditions for sample preparation and testing; test modes and results; the results of conductivity determination and permeability for oil and water; reservoir properties; the nature of the core sample saturation with water and oil; the table of phase and relative permeabilities for oil and water; and the graphs of relative phase permeability versus water saturation coefficient (Fig. 5.1). The dependencies of relative phase permeabilities for oil ko ¼ f(Kw) and water kw ¼ f(Kw) versus water saturation selected in this way were used to determine actual well log data from a large volume of wells in the process of automated continuous processing. This version of the calculation turned out to be adequate to the real distribution of the ka, kw, and ko values in the studied intervals of the real medium.

5.1 Physical and Geological Rationale for the Study of Sections According. . .

205

Fig. 5.1 Experimental determination results of relative phase permeability for oil and water of the formation T2 at stationary filtration from well data 10257 + 7103 (according to GTE TPP “Uraineftegaz”)

5.1.5

Classification of Pre-Jurassic Basement Sediments

Despite the fact that for a significant number of wells in the research area (in this case, for the Shaim oil and gas region) there is the core macro-description of the pre-Jurassic basement for the sediments of Late Permian and Triassic complexes, there is no generalized classification, there is no direct relation of the core with the behavior of the GBS diagram standard well logging, and possible geological and geophysical features for the classification of the whole section of the pre-Jurassic base were not evaluated before.

206

5 Processing and Automated Interpretation of Well Logging Data

These difficulties in solving the problem of a rather “strict” classification of the pre-Jurassic complex deposits are due to the fact that a significant proportion of effusive and volcanogenic facies is confined to the upper, top part of this section. Here, the main distribution became the basic rocks of the basaltoid formation, volcanic-clastic (pyroclastic) rocks of the Middle and Upper Triassic. The solution to the problem of allocating reservoirs and determining the nature of their saturation here is faced with considerable difficulties due to the presence of ambiguity zones between the parameters characterizing water-saturated, oil-saturated, and “dense” strata. The lithology evaluation of effusive rocks using geophysical well survey diagrams largely depends on the amount of data available—in each new area, it is necessary to have data of core analyses to develop interpretation criteria. The experience of logging curve interpretation as a whole shows that effusive rocks differ from sedimentary ones in the nature of the GR and NL curves relative to each other. The indication of these methods in the effusive rocks does not reveal those relationships that are characteristic of sedimentary rocks [13]. When studying the composition and properties of the desired objects in effusive and volcanogenic formations and substantiating reservoir models for such rocks, it is very important to study them: – Spatial and stratigraphic (time) distribution in the area under consideration; – Petrophysical features, including mineralogical composition, texture, and structure of rocks; – Fracture and fault zones; – Reservoir properties and other petrophysical characteristics. Using the available material based on the macro-description of the core for a number of wells in the work area, it should be noted that this information covers the above geological and geophysical factors and conditions for the formation of interpretative (structural, petrophysical, etc.) models of effusive and volcanogenic formations to an insufficient extent. But the available material can be quite used to solve some practical problems of classification. For this purpose, a table with a field description of the rocks of the pre-Jurassic complex was previously formed, which included known data for one part of the wells under study. For the other part of the wells, information on the rock composition was taken from the primary documents with the results of the description of the wellbore hollowing or the interval selection and description of the core in the pre-Jurassic complex. As the data showed, the material complexes of pre-Jurassic basement rocks in the research area, which were opened by wells mainly in its top part, are represented by a significant variety of effusive, volcanogenic-sedimentary, metamorphic, and weathering rocks [1]. As a result of the analysis of such a rock-type variety of the pre-Jurassic basement (mainly in its upper part), the following large groups were identified depending on the origin of the work site: 1. 2. 3. 4. 5.

Effusive and effusive-sedimentary rocks. Volcanogenic-clastic (pyroclastic) rocks. Metamorphosed rocks. The rocks of the weathering crust. Rocks of the Paleozoic basement.

5.1 Physical and Geological Rationale for the Study of Sections According. . .

207

It should be noted that the boundaries for these groups of rocks are fuzzy; to some extent, they are “blurred.” Among the effusive and effusive-sedimentary rocks, there are metamorphic and metamorphosed volcanic origin, and in the rocks of the weathering crust, along with weathered formations, there are also tuffs, breccias and clay shales, porphyry weathered tuffs, etc. The groups of rocks identified in this way were used to interpret the intervals of the pre-Jurassic complex according to GBS data. The basis for the partition of the section is based on a certain correspondence between different mutual correlations of the GBS methods curves and the corresponding information on the macrodescription of the core. An example of the classification for the pre-Jurassic basement is presented in Figs. 5.2, 5.3, 5.4, 5.5.

Fig. 5.2 Litho-stratigraphic column along the well 10636 (legend on the Fig. 5.5)

208

5 Processing and Automated Interpretation of Well Logging Data

Fig. 5.3 Litho-stratigraphic column in the well 7978

5.1 Physical and Geological Rationale for the Study of Sections According. . .

209

Fig. 5.4 Litho-stratigraphic column in the well 10209

5.1.6

Assessment of the Productive Sediment Saturation Nature by Wells of the Studied Areas

As a result of processing GBS data using the HRS-Geo technology, the geological indicators of the studied section were estimated, including reservoir properties, oil and gas saturation of reservoir rocks, and screening (isolating) properties of seal formations.

Fig. 5.5 Correlation scheme of Jurassic sediments along the profile XI-XI of the lithostratigraphic columns of wells 218, 252, 227, 268, and 269

210 5 Processing and Automated Interpretation of Well Logging Data

5.1 Physical and Geological Rationale for the Study of Sections According. . .

211

Sections of almost all exploration wells’ surveyed areas were studied by various methods of standard logging. In particular, data were obtained from radioactive logging (GR, NL), electrometry (AR, SP, IL, LL), caliper (CAL), and acoustic (SN (ΔΤ)) logging. For productive and prospective depth intervals for most wells, test results are available. These data are always taken into account when setting parameters for specific intervals and evaluating the results of automated processing of GBS materials. On the presented lithological and stratigraphic columns of well sections (Figs. 5.2 and 5.4), in addition to the columns themselves, the available set of initial GBS diagrams directly used in their continuous automated processing, and the test results (in the figures in the form of corresponding graphs and columns with test intervals), the calculated curves of absolute (physical) and phase permeability coefficients for water and oil (graphs ka, kw, and ko), are also given, respectively. In addition to the results of processing GBS diagrams shown in Figs. 5.2–5.4, for each of the wells, these results are presented in a quantitative form in two summary tables. They, in particular, provide basic geological and geophysical parameters for permeable well intervals in the form of pointwise determination and layer-by-layer parameter determination. The results of processing and interpretation thus obtained are graphically presented in Figs. 5.2–5.4 and in digital form in the tables (which are not given here), which are a certain unified (complex) set of results that are interconnected and complement each other well. The formalization of well logging results for a large volume of wells in such a form (“figure + table with pointwise and interval parameter values”) virtually eliminates the need for a detailed description of the each well section [1]. On examples with the results of processing and automated interpretation of well log data for the presented wells (Figs. 5.1–5.5), oil-saturated interlayers in reservoirs T1(Ю2) and T2(Ю3 + Ю4) with relatively high filtration-capacitive parameters are fairly confidently fixed. These strata in the field in question are industrial oil-bearing. The Т3(Ю5) reservoir within the territory under consideration is either strongly clayed, as can be seen from the figures presented, or an aquiferous (some signs of oil saturation in the T3 reservoir were identified only in individual wells). The tests carried out, in particular, on well 636 in a number of studied intervals, confirm the obtained results. Among the total volume (more than 100) of the processed wells in the area under consideration, only two of them have no deposits of the Tyumen suite. This is due to the fact that they are located within the most elevated erosion benches of the pre-Jurassic complex (local elevations—positive structures of the fourth order). In a few wells, a rather thin (less than 10 m) productive formation T1 of the Tyumen suite is allocated. Such a reduction in the thickness of the T1 reservoir here is due to the fact that these wells fall on the slopes of elevated erosion ledges of the pre-Jurassic complex of local uplift deposits, where sediments are eroded as a result of a sharp change in sedimentation conditions. In the formations of the pre-Jurassic complex, oil saturation is observed only in a few wells. These are mostly sparse oil manifestations in the form of obvious oil-saturated intervals or only in the form of some signs.

212

5 Processing and Automated Interpretation of Well Logging Data

As noted above, reservoirs in productive strata by structural properties of the pore space, lithological groups of rocks, grain size, and type of cementation mainly belong to classes 4, 5, and 6 according to Khanin (A.A. Khanin, 1973). Single samples are characterized as reservoirs of class 3 (with values ka ¼ 100–500 mD). Such collectors, as is known, are characterized by slightly low, low, and very low reservoir properties. However, in some wells, layers with high values of absolute permeability coefficients ka were identified, which belong to the second (ka ¼ 500–1000 mD) and first (ka  1000 mD) Khanin classes [14]. Of particular interest are wells, in which there are obvious facial signs of the section or indirect diagnostic features on the GBS diagrams and on the resulting lithologic-stratigraphic columns, which characterize to a certain extent specific sedimentation situations. On the curves of the GBS and the resulting columns for these diagnostic features, it is possible to establish the features of the stratification (or the direction of their change from the bottom to the top). According to such facies signs, it is possible to establish some typical lithological characteristics of sediments, for example, for such as buried river systems [15]. With such features, characterizing the presence of the buried riverbed, in particular, is well 209 (Fig. 5.4). Here, in the context of the proposed buried river channel, due to the claying of sandstones and clay filling, the amplitude of the SP curve to the top of the reservoir decreases noticeably (the same situation can be seen on the constructed lithological columns in the ratio of clay and sandy components). This is explained by the fact that the riverbeds from the very beginning of their formation were in a very active hydrodynamic setting, and this contributed to the leaching of clay particles from the lower part of the channel facies. Here, alluvial deposits are characterized by a decrease in the size up the section—from gravel through sand to siltstones (such sediment formation is associated primarily with the weakening of the flow velocity as the channel is filled). When linking borehole material with seismic data, it is often necessary to resort to correlation schemes of litho-stratigraphic units (“packs,” horizons) constructed either on sets of initial GBS diagrams or on corresponding results of their processing and interpretation. The need for such schemes arises in the overall assessment of changes in the geological structure of the target horizons in the work area, in particular, the nature of changes in the thickness of productive and potentially productive intervals, oil-saturated thickness, lithofacial substitutions, pinching out, etc. These schemes make it possible to evaluate the capabilities of the seismic method for identifying the corresponding target reflectors, ultimately assessing the consistency of the results of solving kinematic interpretation problems (structural mapping) and, in some cases, dynamic processing of seismic data. Such correlation schemes of the Jurassic complex horizons are constructed using the results of processing GBS charts in the form of well lithologic-stratigraphic columns in a large number of sublatitudinal and submeridional directions. One of such correlation schemes for target sediments is shown in Fig. 5.5. The lithologic stratigraphic columns used here are exposed in their hypsometric levels, that is, in accordance with their absolute depths—geological horizons of the litho-stratigraphic subdivisions of the section being studied. For each of the wells in the correlation schemes,

5.1 Physical and Geological Rationale for the Study of Sections According. . .

213

individual values of the wellbore extensions were used (i.e., the well inclinometry data were taken into account). For sediments of the Tyumen formation, as can be seen from Fig. 5.5, there is a decrease in the total thickness and the number of horizons in the arched (raised) part of the structure under consideration (in the area of the wellhead 227), while simultaneously increasing them on the wing (lowered) parts of the structure; the oil saturation in the Т3(Ю5) horizon sediments lying on the structure’s wings is almost not manifested, with the exception of a thin oil-saturated interlayer in the depth interval of 2107–2110 m in the vertical section of the well 268. The pre-Jurassic basement deposits along this profile are also not very prospective. Of all the presented well sections, only one can be of interest—well 227 (in the structure arch), in the upper part of the pre-Jurassic base of which (in the depth interval of 2009–2011 m) a relatively thin oil-saturated interlayer quite confidently stands out. The obtained results of GBS data processing were used directly in the interpretation of seismic data in the form of CDP time sections and the seismic data inversion results in the form of effective acoustic impedance (AI). An example of such sections in one of the seismic lines is shown in Figs. 5.6 and 5.7. Figure 5.6, in particular, shows a fragment of the original time section obtained after the preliminary processing of seismic data according to a special graph [1, 4, 5, 8, 9]. The use of this processing graph provides, above all, the preservation of the seismic record dynamics and identification information about the geological and geophysical parameters of the real environment that are most important for prospecting and industrial exploration. For the stratigraphic binding of the reflecting horizons to the corresponding geological horizons of the studied section, we used data from wells that are located directly in the profile: 218, 436, 252, 227, 269, and 268 (Fig. 5.5). In particular, for

Fig. 5.6 A fragment of the CDP time section along the profile 5798101 as a result of the special graph use for seismic data processing

214

5 Processing and Automated Interpretation of Well Logging Data

Fig. 5.7 Effective acoustic impedance along the line 5798101

this purpose, sonic logging (ΔТ) curves, previously converted to a timescale, were used. Such SN curves with their geological breakdowns, superimposed on the vertical cross sections of the effective AI, fit quite well into this section, while allowing relatively easy identification of the corresponding lithologic-stratigraphic boundaries. Judging by what was presented in Fig. 5.7, the effective AI, it can be stated that a significant part of the lithological-stratigraphic boundaries can be traced confidently. This section shows all the elements of wedging and replacement of rocks with some characteristics of wave resistance to others, showing the whole construction and internal structure of the above and below (from the Jurassic complex) deposits, as well as its main tectonic features. The obtained information on the processed well data is directly used for the quantitative prediction of various geological components based on the integrated use of drilling data, GBS, and highresolution seismic materials.

5.2

Velocity and Elastic-Deformation Characteristic Determination from VSP Data Study of the Physical Property Spatial Distribution

One of the main physical parameter distributions of the real subsurface medium, determined using the seismic exploration method, is the spatial distribution of the wave propagation velocities. The distribution of seismic wave velocities, besides the fact that it acts as an independent kinematic characteristic (velocities, as is known, in many tasks of processing and interpreting seismic data are used), it also affects the dynamic characteristics. This seismic parameter is included in the expression

5.2 Velocity and Elastic-Deformation Characteristic Determination from. . .

215

describing the acoustic impedance, being one of the factors in it, and in the expression of the reflection and transmission coefficients, the distribution of which determines the energy and shape of the seismic signal probing the medium. Knowledge of wave propagation velocity is required at different stages of seismic data application: during field surveys (design of observation schemes), preliminary and final seismic data processing, construction of structural maps, wave field inversions, integrated data interpretation, when calculating resources and hydrocarbon reserves, monitoring subsurface using seismic data, etc. In practice, there are several sources for obtaining wave velocity data: sonic measurements (SN) in wells, borehole seismic observations at internal points of the medium (VSP), and multiple measurements during positional seismic observations (MCDP). Significant perspectives in the development of seismic surveys are shown by materials of multiwave seismic exploration when solving prospecting problems of petroleum geology, such as predicting a geological section [16–18]. As is well known, an increase in the detail and accuracy of studying geological objects can be achieved by expanding the complex of informative parameters extracted from three-component seismic observations. The importance of using such parameters is that the use of, for example, transverse waves together with longitudinal ones contributes to an unambiguous indication of hydrocarbons, as shown by a number of researchers [18–20]. It has been found theoretically and experimentally that the velocity of transverse waves VS is less sensitive to fluids saturating the substance than the velocities of longitudinal waves VP, and therefore VS are used as normalizing quantities with which VP are compared [17]. In order to extract information about the composition and properties of rocks by measuring the characteristics of the waves in the internal points of the medium using polarization method PM VSP, a special technique and a set of SKOR programs have been developed for calculating the velocity characteristics of longitudinal and transverse waves and effective elastic-deformation modules of rocks under the in situ conditions [21, 22]. A brief look at the content of the program algorithms included in the SKOR complex and the method of its application, the block diagram of which is shown in (Fig. 5.8). The complex consists of three programs: PRESIC (accuracy), CORREL (correlation), and VELOC (velocity) [2, 23]. The PRECIS program calculates the formation and average velocities of longitudinal and transverse waves and the accuracy of their calculation. The input data for this program and for the other two programs are vertical hodographs of longitudinal tP(z) and transverse tS(z) waves, previously corrected for scatter of observed events on a control receiver to the “vertical graph” method, for vertical time, and an explosion moment for each observation points. Under this program, the vertical hodograph times are corrected for the offset of the shot point from the wellhead. The calculation of layer velocities is made by the angular coefficient of the linear approximating function of the vertical hodograph points within each of the selected layers. The approximation of the points t(z) is

216

5 Processing and Automated Interpretation of Well Logging Data

Fig. 5.8 A block diagram of SKOR program complex algorithms for kinematic data processing of VSP (PM VSP) data

performed by the least squares method. The times of the vertical hodograph within each layer and the deviations of the initial times t(z) from the averaging linear dependence are smoothed by a linear function. Errors in the velocity calculation are determined by known formulas. The values of layer velocities and the accuracy of their calculation are defined in two ways: without smoothing the values of the times t(z) and with smoothing their sliding polynomial of a given degree and the number of points through which it is carried out. The initial data for the program are the materials of the PM VSP or VSP. However, it can also be used to process the so-called conventional vertical hodographs, obtained by a special recalculation (Vav. = > tcn.(z)) of the velocity analysis results of CDP seismic gathers. If there is only VSP data, you can use the program to determine the average, layer, and interval velocities of longitudinal waves and the accuracy of their calculations. The CORREL program determines the optimal parameters (optimization problem) n and m of an approximating continuous curve for smoothing vertical hodographs of longitudinal and transverse waves by a sliding polynomial of arbitrary degree n according to an arbitrary number m of hodograph points based on the choice of the maximum value of the correlation coefficient for regression equations:

5.2 Velocity and Elastic-Deformation Characteristic Determination from. . .

217

V P,S fm ¼ A  V P,S ðzÞ þ B: The work of the program is to smooth the values of the vertical hodograph time t (z), their differentiation, and calculation on this basis of the interval velocities, finding the coefficients of the linear regression equation between the values of layer and interval velocities and linear correlation coefficients {r} for different smoothing n and m parameters of wave hodographs. The range of parameters n and m is set. When smoothing t(z) values, the coefficients of the approximating polynomial are determined by the least squares method from a system of n + 1 linear equations: nþ1 X i¼1

ai

m X k¼1

1 2 zii ¼ k

m X

t ðzk Þ  zki1 1 ,

ði1 ¼ 1, 2, . . . , n þ 1Þ:

k¼1

The polynomial found in this way is differentiated in the middle of m points of the vertical hodograph, through which it is approximated. The interval velocity of seismic wave propagation at the points zk of the vertical profile is determined as the inverse of the smoothed hodograph derivative [24, 25]: V ðzÞ ¼

1 dt=dz

The velocity curves V(z) essentially depend on the smoothing parameters n and m. Their search allows to find the most optimal values of n and m according to the analysis results of the corresponding correlation coefficients ri of the desired regression equations of the form: V P,S fm ¼ Ai  V P,S ðzÞ þ Bi Figure 5.9 shows an example of determining the most optimal smoothing parameters n and m using the correlogram. From this example it can be seen that the optimal smoothing parameters of the vertical hodograph n ¼ 2 and m ¼ 11 were found, at which the maximum correlation coefficient of the sought equation is r ¼ 0.98. The VELOC program calculates the values of the interval propagation velocities of the longitudinal VP(z) and transverse VS(z) waves, vertical velocity gradients of these waves V 0Z P , V 0Z S , velocity ratio VS(z)/VP(z), specific time ΔТP(z) and ΔТS(z) of wave propagation and the ratio of absorption coefficients αP(z)/αS(z) of longitudinal and transverse waves, values of Poisson’s ratio ν, and side expansion coefficient k, and, if there are distributions of rock density in the range of depths under study, effective horizontal stress σ x,y and effective elastic-deformation medium parameters: Young’s modulus E, volume expansion coefficient K, compressibility β, and Lame constants μ (shear modulus) and λ (stretching/compression modulus), as well as the

218

5 Processing and Automated Interpretation of Well Logging Data

Fig. 5.9 The results of optimization for the smoothing parameters n and m of the vertical hodograph tj(z) to determine the velocity model VL ¼ f(z)

reflection coefficients of longitudinal RPP and transverse RSS waves and their ratio RPP/RSS. All these physical indicators are calculated with the optimal smoothing parameters of vertical longitudinal and transverse wave hodographs, which are preliminarily determined by the results of the CORREL program and with two versions (iterations) of smoothing by a sliding polynomial of these hodographs. The algorithm for calculating wave interval velocities of VP(z) and VS(z) is similar to that described in the previous program CORREL. In this case, a model of a continuous medium is used as a model of velocities, in which the seismic wave velocity is an arbitrary continuous and differentiable function of the vertical coordinate. To bring for the optimal base (interval) of vertical hodograph smoothing and to the corresponding velocity curves of the base analysis of the rock density curve σ(z), the hodograph approximation by a continuous variable function in the form of a hyperbolic tangent function is used [26, 27]. In order to calculate the basic physical parameters of the heterogeneity, the formulas obtained from the classical elasticity theory [28, 29] are used (the depth coordinate z is omitted here): ν¼

 V 2P  2  V 2S

2  Poisson’ s ratio ; 2 2  VP  VS

k¼

ν ðside expansion coefficientÞ; 1ν

σ x,y ¼ k  σ n  g  H ðhorizontal stressÞ;

5.2 Velocity and Elastic-Deformation Characteristic Determination from. . .

219

where Н is the formation depth, σ n is the average density of overlying rocks, g is the normal gravity acceleration;  3 αP 4 V S ¼  ðabsorption coefficient ratio of longitudinal and transverse wavesÞ; αS 3 V P
  σ  V 2S  3  V 2P  4  V 2S

2  E¼ Young’ s modulus ; 2 2  VP  VS   4 K ¼ σ  V 2P   V 2S ðvolume expansion coefficientÞ; 3 β¼

1 ðcompressibility factorÞ; K

μ ¼ σ  V 2S ðSecond Lame parameter ðshear modulusÞÞ;
 λ ¼ σ  V 2P  2  V 2S ðFirst Lame parameterÞ ðstretching=compression modulusÞ; RPP ¼

σ 2  V 2P  σ 1  V 1P ðlongitudinal wave reflection coefficientÞ; σ 2  V 2P þ σ 1  V 1P

RSS ¼

σ 2  V 2S  σ 1  V 1S ðshear wave reflection coefficientÞ: σ 2  V 2S þ σ 1  V 1S

The computation technique for the SKOR software package provides for the participation of the geophysical interpreter in the processing at the stage of selecting processing parameters and setting (up to a certain limit, determined by the magnitude of the observation step) the degree of the section detail with the desired characteristics. The kinematic scheme of wave propagation with lithology features along the section is analyzed. Data processing is carried out in an interactive mode, involving the sequential and cyclical use of programs to refine the reservoir models and interval velocities of longitudinal and transverse waves and obtain on this basis the physical parameters of the real environment of appropriate detail. Testing of the developed program complex was performed on the PM VSP materials obtained using the seismic borehole three-component installation of the SSTU-2PO seismic receivers, oriented in space in a given direction [21, 30]. With this installation, at each observation point, the axis of maximum sensitivity of a horizontal X-geophone is set in the radial plane. The interval selected for testing covers a depth range of 2100–3300 m. Intersalt deposits (D23 zd-ptr, depths of 2967–3279 m) occur in the lower part, which are productive. They are predominantly dense clayey limestone and dolomite. Above these sediments lies the halite substratum (D23 lb1, depth 2195–2967 m) of the upper salt-bearing, which is represented mainly by pure salt with sparse (10–15%) interlayers of non-salt rocks. Vertical hodographs of the longitudinal tP(z) and transverse tS(z) waves were read from the Z- and X- components of the wave field in the selected interval, respectively. Data on the density of rocks were obtained from the results of geophysical

220

5 Processing and Automated Interpretation of Well Logging Data

Fig. 5.10 The results of PM VSP data processing by SKOR complex programs

well log data processing [31]. The main results of calculations using the SKOR software package are shown in Fig. 5.10. The analysis of the resulting set of parameters allows us to highlight both the general relations of changes in the physical characteristics of various rock types exposed by the well and specific features of the mineral substance state and layer-bylayer zoning of the elastic properties of the rocks described by the calculated indicators. The most general dependencies of their changes in the well under study are consistent with well-known ideas about the nature of the indicator distribution in real environments. Thus, smaller values of E, K, μ, and λ are characterized by less dense rocks (salt, σ ¼ 2.15–2.25 g/cm3) and relatively large, more dense rocks (carbonate, σ ¼ 2.60–2.85 g/cm3). The observed changes in the values of almost all the considered physical characteristics, including the distribution of the reflection coefficients RPP and RSS, correspond to changes in the lithological types and rock composition. Based on the data obtained in the well section, three areas can be distinguished, differing by the values of interval velocities VP(z) and VS(z); ratios VS(z)/VP(z); and elastic characteristics Е, К, β, μ, and λ (Table 5.1). The areas indicated in the table that coincide with the main lithologic and stratigraphic subdivisions of the section under study are quite clearly distinguished by the main physical characteristics. The presence in the bottom of the clay-halite substratum of the clay-marl interlayers led to some deterioration of the elastic parameters in region I with respect to regions II and III. This is indicated by an increase in the compressibility of rocks (β ¼ 36–50 MPa1), their decompression due to an increase in clay content in the section, and lower values of VP, VS, E, and μ. Region II is characterized by elasticity higher than region I and relatively lower compressibility (β ¼ 28–37 MPa1). This area is represented mainly by crystallized coarse-grained rock salt formations with good acoustic intergranular contacts. In this area, several sections can be distinguished, represented only by pure salt, which confirms the well-known position arising from Hooke’s law for an ideally elastic

III

II

No of area I

Lihological-stratigraphical division Clayer-galite sublayer of upper salt layer (D32lb2) Galite sublayer of upper salt layer (D32lb1) Intersalt (mostly) carbonate deposits (D32zd-ptr)

Depth (m) 2100– 2195 2195– 2967 2967– 3279

Vp (m/s) 3750– 4330 4400– 4780 4840– 64202 Vs (m/s) 2190– 2500 2420– 2770 540– 3340

Table 5.1 Physical parameters in the area of anomaly changing ΔTp (mcs/m) 230– 280 210– 227 156– 207

ΔTs (mcs/m) 400– 460 360– 413 300– 394 Vp/Vs 1.68– 1.73 1.63– 1.88 1.48– 2.11

ν 0.23– 0.25 0.20– 0.30 0.08– 0.35

E (MPa) 0.013– 0.017 0.017– 0.027 0.021– 0.082

K (MPa) 0.020– 0.027 0.027– 0.037 0.033– 0.082

12–13

28–37

β (MPa-1) 36–50

μ (MPa) 0.011– 0.014 0.013– 0.017 0.016– 0.028

λ (MPa) 0.009– 0.014 0.011– 0.022 0.008– 0.056

5.2 Velocity and Elastic-Deformation Characteristic Determination from. . . 221

222

5 Processing and Automated Interpretation of Well Logging Data

medium: the coincidence of the λ and μ values at ν ¼ 0.25 and VP/VS ¼ 1.73 (Poisson’s hypothesis) is noted. Region III is quite clearly expressed, which is characterized by high elasticity and low compressibility (β ¼ 12–30 MPa1). This is an area of intersalt deposits, which, as already noted, is composed mainly of dense clay limestone. However, this part of the section is heterogeneous. Porous-cavernous limestones and dolomites with low reservoir properties are observed here, which manifests itself in almost all velocity and elastic parameters. When studying the parameter informativeness obtained as a result of seismic studies, they were compared with well field geophysical data (Fig. 5.11). The results presented in this figure show that the interval velocities of transverse and longitudinal waves and the Poisson’s ratio vary significantly in the zone of limestone substitution with dolomite. When changing the type of lithology in carbonate rocks, a change in the physical properties of the rocks is observed—of effective porosity and oil-saturated capacity (Fig. 5.11). It can be seen that between such indicators as the velocity of longitudinal waves, Poisson’s ratio, and the level of limestone substitution by dolomites, there is a direct relationship. The minimum values of these indicators are characteristic of the section, composed mainly of dolomite differences. These characteristic features of the physical parameters distribution indicate that the lithology of the fluid-saturated section and the structure of the interpore space affect the parameters of transverse and longitudinal waves in different ways. Thus, there is a fairly reliable dependence between the physical parameters of rocks found using the SKOR software package and the geological structure of the section. At the same time, it is possible to form a complex of seismic indicators having a direct dependence with the material composition of rocks and their saturation.

Fig. 5.11 A comparison of field-geophysical well data with velocity characteristics of longitudinal and transverse waves in intersalt deposits

5.3 Study of Geological and Geophysical Processes Taking Place in Time

5.3

223

Study of Geological and Geophysical Processes Taking Place in Time

In addition to determining the spatial distribution of physical properties, it is important to study the geological and geophysical processes occurring in the depths of the sedimentary strata, i.e., the study of changes in physical properties over time by the parameters’ monitoring. An example of an important practical task is to obtain information about the quantitative changes in hydrocarbon reserves when they are extracted during a certain period of time and predict the environment parameters for the future (studying changes in the level of GWC and OWC when replacing oil and gas with formation water, reservoir pressure, geomechanical properties, etc.) [32, 33]. Another important task is the study of the stress state of the rock mass under the influence of rock pressure its observation over time, and prediction of the physical and geomechanical properties of the real environment in the future before the onset of possible deformations of continuous media and the manifestation of the tectonic process [34, 35, 36]. In the borehole version, this approach was used by the authors to monitor the stress state of the real geological environment [2, 37]. Monitoring the stress state of an elastic medium can be carried out to study the conditions for the occurrence of technogenic earthquakes with long and intensive extraction of hydrocarbons from oil and gas deposits and the choice of methods to prevent them or predict the time and location of natural earthquakes, predicting their consequences and measures to minimize them. It should be noted that the study and monitoring of fluid flow dynamics in porous media with a change in the stressed state of the medium is also an equally important task. The distribution of fluids in reservoir rocks, as is known, depends on the distribution of pressures at the internal points of oil and gas deposits [34–36, 38–40]. Changes in the structure of the reservoir lead to a change in the stress state of the reservoir, since fluids, due to their mobility properties, instantly react to changes in the distribution of stresses at internal points of the medium, forming complex flows until an equilibrium is established. It is also known that the reasons for the change in the distribution of tension in rocks in a local environment are diverse. Such changes in the tension of the environment can be both natural and artificial (man-made). The most important characteristics of deposits, i.e., filtration-capacitive and lithological, depend on the stress state of deposits. Changes in the distribution of stresses lead to changes in the numerical values of porosity, permeability, hydrocarbon extraction factor, etc. In turn, this leads to a change in the estimated parameters of prospective and projected oil and gas reserves. Monitoring of the stress state makes it possible to take into account such changes in the objective assessment of resources and hydrocarbon reserves. Information on changes in the environmental characteristics (FCP) over time with active or passive effects on the reservoir or in the process of extracting hydrocarbons from it provides a basis for optimal well

224

5 Processing and Automated Interpretation of Well Logging Data

operation and a more reliable prediction of the process of extracting oil and gas from the reservoir. The above tasks point to the importance of studying the stress state of target objects and their enclosing media. Indeed, the study of the rock mass stresses in zones of development of foci and possible manifestations of natural and “artificial” (man-made) earthquakes is very relevant and in connection with the urgent need to prevent serious catastrophic consequences from their occurrence. Particular attention should be paid to those areas of possible earthquakes, which are caused by the violation of the ecological balance of the geological environment due to engineering activities, including those associated with the extraction of mineral resources. Such activities can lead to the energy redistribution of elastic deformation and stress state in rock massifs. There are ways to control the stress state of rocks, which are based mainly on the study and analysis of various sets of geophysical and geochemical field parameters [19, 33]. The study of earthquake precursors is carried out on the basis of longterm (periodic) monitoring of the stressed state of rocks in special observation wells. The main information parameters indicating the appearance of earthquake precursors with reference to SN [19, 33] are as follows: (1) an increase in the Vp/Vs ratio (at ΔVp/Vp  10%, ΔVs/Vs  (40–60%) and a decrease αp/αs at Δm/m  50%); (2) a decrease in the values of the deformation-elastic moduli E, G, and μ (Young’s and shear moduli and Poisson’s ratio, respectively); (3) an increase in the side expansion coefficient k and the horizontal component of stress σ h (with Δσ h/σ h  80–100%); (4) displacement of the resonance and antiresonance ranges in the spectra of PPP and PSP waves; (5) the fall of the Lamb-Stoneley wave amplitudes; and (6) an increase in the level of acoustic noise in the high- and low-frequency bands of the spectra due to the flow of fluid and the appearance of additional microfractures. With regard to thermometry, this is (1) an increase in thermal diffusivity (with Δα/α  103%); (2) a local temperature change in the reference layers; and (3) an increase in heat flow. With reference to radioactive methods, this is (1) an increase in indications on the NL curves (with ΔJNL/JNL  40–60%) and (2) a decrease in the indications of the GGL density. As can be seen from the presented information indicators (obtained mainly on the basis of laboratory and borehole experiments), their changes in the process of approaching an earthquake can be several tens or even hundreds of percent. The expediency of using the analysis of both kinematic and dynamic (amplitude) characteristics of seismic waves in seismic acoustic methods for studying the Earth’s crust is noted. At the same time, the earthquake focus, confined to the areas of the extreme stress state of the Earth’s crust, can be reliably fixed based on the analysis of the data gradient of the studied characteristics. The methodology for studying the conditions for the occurrence of technogenic earthquakes associated with the development and production of hydrocarbons remains not fully developed. The paper [37] proposes a technique for studying the stress state of rocks in the areas of possible earthquakes, which is based on the use of information kinematic and dynamic parameters of the seismic wave field obtained using one of the

5.3 Study of Geological and Geophysical Processes Taking Place in Time

225

modifications of the polarization method (PM) of seismic exploration—its borehole modification of vertical seismic profiling (PM VSP). At the same time, the method of observations of VSP PM is supplemented by special regime (monitoring) measurements in the well and specialized processing and interpretation of the obtained observation materials. The field method provides for the registration (in pre-drilled and prepared deep wells in which three-component orthogonal X, Y, and Z or homogeneous I, II, and III seismic receivers (geophones) are permanently installed) of a seismic wave field periodically excited by the source (for a sufficiently long time period). The orientation of the seismic record components in space in the appropriate directions is performed either with forced (“physical”—directly in the well) [21, 30] or with preliminary laboratory orientation (providing for the use of special software) [41–45]. As an example Fig. 5.12 shows fragments of PM VSP seismograms—the Z and X components of seismic records registered by a three-component downhole seismic probe with a forced orientation of the SSTZ-3GI-1/90 (designed by M.N. Isaenko) in one of the deep wells located in the northern structural tectonic zone of the Pripyat Trough [21, 30]. A distinctive feature of working with such an installation, orientated in space in a given direction, as compared to a non-orientated installation of seismic geophones, is the need to perform a number of operations to control the device in a well using a special instrumentation complex consisting of surface and

Fig. 5.12 PM VSP seismograms along one of the wells of the northern structural tectonic zone of the Pripyat Trough: (a) Z component, (b) X component

226

5 Processing and Automated Interpretation of Well Logging Data

downhole blocks and analyzing the seismograms of X, Y, and Z components of the first break of a direct passing wave [2]. It takes into account that this phase is linearly polarized and the rays of this wave in a horizontally layered isotropic medium deviate from the vertical XOZ plane only within the limits of orientation errors. As the visual analysis of PM VSP seismograms on Z, Y, and X-components shows, an abundance of reflected longitudinal and exchange waves is observed. Figure 5.12 shows only two major components (Z and X). A significant majority of reflectors associated with PP-type waves are also exchange boundaries where PS-type waves are formed. Moreover, the exchange and longitudinal waves are associated with the boundaries, located both within the studied depth interval and below it. The basis for digital data processing of three-component seismic observations in wells is a set of SKOR programs developed by the authors, which, as noted above, are used to calculate the effective elastic deformation parameters and stress state in situ conditions of the studied deposits [2, 21, 23]. The system of data processing and interpretation of monitoring measurements in such conditions, directly in wells, is shown in Fig. 5.13. It consists of three information-related blocks: SKOR program complex; monitoring system—data processing unit of VSP PM; and WPS data processing system [2]. The results of the calculations using the SKOR software package in the vertical section are directly determined (Fig. 5.13): average, layer, and interval velocities of longitudinal and transverse waves; their ratios; Poisson’s ratio; lateral expansion factor; and transverse and longitudinal wave absorption coefficients ratios; in the presence of the studied depth range of the rock density distribution, the Young’s modulus, Lame constants, volume expansion coefficients, vertical geodynamic and effective horizontal stresses, and the reflection coefficients of longitudinal and transverse waves and their ratios are determined [2]. At the final stage of processing using statistical analysis programs based on the results of multiple, periodic measurements for all parameters being determined, statistical estimates are found (standard deviation, coefficient of variation, asymmetry, excess, etc.) and their relationships. The conclusion about the preparation of the study area for the possible earthquake manifestation is made on the basis of the analysis of changes in statistical estimates over time of the kinematic and dynamic parameters of the seismic wave field and their relations (Fig. 5.13). On this basis, the time (date) of its manifestation is predicted. Testing of the developed methods and program set for seismic monitoring by the authors was performed on the materials from one of the deep wells located within the Tengiz oilfield. Previously, a complex technological scheme for testing the vertical profile under study was developed (by E.I. Galperin)—wells, stationary fixed near elastic oscillation shot points (cycles of periodic testing were specially “nested,” starting from a measurement period of 4 h during the week up to 15 days during the year) [2, 37]. When testing the methods and techniques of the research, the authors used the materials which by that time characterized the research cycle in full volume. A “week cycle” was chosen with a period of “working out” (shooting and registration of the wave field at internal points of the medium) at 4 o’clock, starting from 18:30

5.3 Study of Geological and Geophysical Processes Taking Place in Time

227

Fig. 5.13 Processing and interpretation system for data monitoring of the underground stress to predict seismicity in the areas of natural and technogenous (man-made) earthquakes

on March 17, 1991, until 10:30 on March 24, 1991. To exclude a random component, during the “working out,” each act of elastic oscillation generation and registration from the same point of the explosion was repeated ten times. This was taken into account in the subsequent processing of observation data: from the tenfold “testing” data at each observation point were the arithmetic mean values (assuming their normal distribution) and the results were taken as a whole as a single act of shot and registration of oscillations (according to the influence of the statistical effect

228

5 Processing and Automated Interpretation of Well Logging Data

pffiffiffi measurement accuracy due to multiple (k times) measurements increases in k times). According to the results of periodic measurement data processing of the “week cycle,” a whole set of “initial” graphs Ψ i(z, t) was built (only their minimum required number was selected). In particular, the sets of curves in the form of vertical hodographs tp(z), the corresponding interval velocity of longitudinal wave propagation Vp(z), effective horizontal stresses Gx,y(z), Lamé constants λ(z), and Young’s modulus Ε(z) (which are fixed at each moment of time t) are obtained. During the weekly research period over the Tengiz oilfield, the following changes in the studied parameters are most confidently observed. There is a tendency of gradual decrease in the values of the difference hodograph of longitudinal waves in time—the time field ΔTp(z, t) (Fig. 5.14). Several directions (axes) of the maxima of changes in this parameter are recorded at depths of 750, 1100, 1450, 1500, 1600, and 1650 m. The largest changes are observed in the depth interval of 600–1000 m in the period from 1830 in March 23, 1991, to 1030 in March 24, 1991. The most significant changes in the parameters ΔGx,y(z, t), Δλ(z, t), and ΔΕ(z, t) are observed in the depth interval 1400–1650 m (Fig. 5.15a–c). Such changes have alternating character with sign change lines approximately at depths of 1450, 1600, and 1650 m. A number of local zones with areas of increased parameter changes are also noted on these fields. On average, the variation ranges of the considered parameters are characterized by the following values for horizontal stress ΔGx,y(z, t)  1.0  0.5 MPa (Fig. 5.15a), Lame constants Δλ(z, t)  2.0  0.410–3 MPa (Fig. 5.15b), and Young’s modulus ΔΕ(z, t)  2.0  0.410–3 MPa (Fig. 5.15c). As can be seen from these data, the sensitivity of the applied technology complex (measuring and processing systems) turns out to be very high, quite suitable for solving subtle problems of predicting changes in the stress state of the studied rocks under in situ conditions. It should be noted that in this case the real environment was influenced only by natural factors (tidal, seasonal, climatic, etc.), and the changes in the parameters given above were observed in the absence of a direct impact (man-made) on the environment (i.e., there was no fluid extracting from the productive horizon). Thus, based on the change analysis in statistical estimates over time of kinematic and dynamic parameters, seismic wave field set, and their relationships, a conclusion is made about preparing the study area for a possible earthquake, and the time of its occurrence is predicted. If there is feedback in the system being studied, the information obtained about the changing parameters and stress state of the environment is the basis for the correction of the work technology—the process of natural resource production. The results presented above or similar to this research system can be effectively used to directly predict changes in the stress state of rocks in areas of possible manifestations of artificial (man-made) or natural earthquakes.

5.3 Study of Geological and Geophysical Processes Taking Place in Time

229

Fig. 5.14 Travel time field of longitudinal wave variation ΔTP(z, t) (underground stress monitoring, Tengiz oilfield)

230

5 Processing and Automated Interpretation of Well Logging Data

Fig. 5.15 Underground stress dynamic prediction (for parameters) according to monitoring studies: (a) variation field of horizontal stress ΔGx,y(z, t), (b) the variation field of Lama constant Δλ(z, t), (c) variation field of Young’s modulus ΔE(z, t)

5.4 Summary

231

Fig. 5.15 (continued)

5.4

Summary

When processing and automated interpretation of GBS data, the effect of a specific property of the studied geological section is revealed by sequential compensation of interfering factors using comparative analysis of various methods and normalized diagrams in certain combinations. According to the set of parameters used, located in a certain system, the material composition, porosity, content of bound water, and useful capacity are established; the oil and gas saturation of reservoirs is estimated; and a number of very important characteristics of the geological section are calculated. On this basis, a thin-layer geological model is formed in the vertical section of the well section, and lithological and stratigraphic columns and detailed wells geoacoustic models are constructed. Data processing of vertical seismic profiling (VSP and PM VSP) is performed on the basis of the SKOR software package for determining the velocity characteristics of various types waves (longitudinal, transverse, exchange), effective elastic deformation parameters, and the stress state of the real medium in situ conditions of the rock mass in the context of the studied well. The system for processing, interpreting, and monitoring measurement data directly in wells using the SKOR complex is designed in such a way that at the final stage of processing using statistical analysis programs, based on the results of multiple, periodic measurements, various statistical estimates, and their relationships are found for all the determined parameters. The conclusion about the preparation of the studied area for the possible earthquake manifestation is made on the basis of the

232

5 Processing and Automated Interpretation of Well Logging Data

changes in statistical estimates over time analysis of the seismic wave field kinematic and dynamic parameter complex and their relations. On this basis, the time of its (earthquake) onset is also predicted.

References 1. Trofimov, V. L., Khaziev, F. F., Milashin, V. A., et al. (2007). Avtomatizirovannaya obrabotka i interpretatsiya dannykh GIS dlya obnaruzheniya nefteperspektivnykh obyektov metodami vysokorazreshayushchey seysmiki (Automated processing and interpretation of well logging data for the detection of oil-prospective objects by high-resolution seismic methods). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 54–66. 2. Trofimov, V. L., & Khaziev, F. F. (2004). Prognozirovaniye dinamiki napryazhennogo sostoyaniya massiva gornykh porod po dannym trekhkomponentnykh skvazhinnykh issledovaniy (Predicting the dynamics of the rock mass stress state based on the data of threecomponent borehole studies). Tekhnologii seysmorazvedki (Seismic Technologies), 1, 50–56. 3. Trofimov, V., Khaziev F., & Trofimova A. (2018). Tekhnologiya VRS-Geo. Izucheniye nefteperspektivnykh obyektov metodom vysokorazreshayushchey seysmiki (HRS-Geo technology. Study of oil-prospective objects by the method of high-resolution seismic). Oil & Gas Journal Russia, 1-2(123), 28–35. 4. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., et al. (2009). Spetsial'naya obrabotka i interpretatsiya dannykh seysmicheskikh nablyudeniy v slozhnykh geologicheskikh usloviyakh metodom vyokorazreshayushchey seysmiki (Special processing and interpretation of seismic observation data in difficult geological conditions by the method of high-resolution seismics). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 36–50. 5. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2011). Otsenka vklada elementarnykh granits i tolshch v seysmicheskoye volnovoye pole dlya mnogosloynykh pogloshchayushchikh sred (Assessment of the elementary boundaries and strata contribution to the seismic wave field in multilayer absorbing media). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 86–96. 6. Zalyayev, N. Z. (1984). Vliyaniye litologii i plastovykh uglevodorodov na geofizicheskiye parametry (Influence of lithology and reservoir hydrocarbons on geophysical parameters). Book: “Metodika i rezul’taty geologo-geofizicheskikh issledovaniy v Pripyatskom progibe” (“Methodology and results of geological and geophysical research in the Pripyat Trough”). Minsk. Science and Technology. pp. 61–74. 7. Zalyayev, N. Z. (1990). Metodika avtomatizirovannoy interpretatsii geofizicheskikh issledovaniy skvazhin (Technique of automated interpretation of well logging). Minsk. University Edition. p. 142. 8. Trofimov, V. L., Khaziev, F. F., & Shkol’nik, S. A. (2014). Sovershenstvovaniye metodiki prognozirovaniya geologicheskikh pokazateley metodom vysokorazreshayushchey seysmiki (Improvement of the methodology for predicting geological indicators by the method of highresolution seismic). Ekspozitsiya Neft’ Gaz (Oil and Gas Exposition), 6(38), 13–19. 9. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2008). Opredeleniye geologogeofizicheskikh parametrov real’noy sredy metodom vysokorazreshayushchey seysmiki (Determination of geological and geophysical parameters of the real medium by the high-resolution seismic method). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 25–30. 10. Trofimov V.L., Milashin V.A., Khaziev F.F. Ponamarev S.A. 2009 Vozmozhnosti kolichestvennogo prognoza geologicheskikh pokazateley metodami vysokorazreshayushchey seysmiki (Possibilities of quantitative prediction of geological indicators by high-resolution seismic methods). Oil and Gas Innovations. 2. pp. 11–26.

References

233

11. Golf-Rakht, T. D. (1986). Osnovy neftepromyslovoy geologii i razrabotki treshchinovatykh kollektorov (Fundamentals of oilfield geology and fractured reservoir development) (p. 608). Nedra. 12. Vendel’shteyn Yu, B., & Rezvanov, R. A. (1978) Geofizicheskiye metody opredeleniya parametrov neftegazovykh kollektorov (pri podschete zapasov i proyektirovanii razrabotki mestorozhdeniy) (Geophysical methods for determining the parameters of oil and gas reservoirs (when calculating reserves and designing field development)). M., Nedra, p. 318. 13. Shilov Ya, G., & Dzhafarov, I. S. (2001). Geneticheskiye modeli osadochnykh i vulkanogennykh porod i tekhnologiya ikh fatsial’noy interpretatsii po geologo-geofizicheskim dannym (Genetic models of sedimentary and volcanic rocks and the technology of their facies interpretation from geological and geophysical data). M., State Science Center “VNIIgeosystem”. p. 397. 14. Khanin, A. A. (1973). Porody-kollektory nefti i gaza neftegazonosnykh provintsiy SSSR (Oil and gas reservoir rocks of the USSR oil and gas provinces). M., Nedra, p. 304. 15. Movshovich, E. B., Knepel, M. N., Nesmeyanova, L. I., & Pol’ster, L. A. (1981). Printsipy vyyavleniya zon fatsial’nogo kontrolya neftegazonakopleniya (Principles of identifying of facial control zones of oil and gas accumulation) (p. 268). M., Nauka. 16. Mnogovolnovyye seysmicheskiye issledovaniya (Multiwave seismic research). (1987). Novosibirsk. Nauka. Institut geologii i geofiziki (Institute of Geology and Geophysics), Editor - N.N. Puzyrev. p. 213. 17. Puzyrev, N. N., Trigubov, A. V., Brodov Yu, L., et al. (1985). Seysmicheskaya razvedka metodom poperechnykh i obmennykh voln (Seismic exploration by the method of shear and converted waves). M., Nedra. 18. Trofimukm, A. A., Mendel’baum, M. M., & Puzyrev, N. N. et al. (1981). Pryamyye poiski nefti i gaza i ikh primeneniye v Sibiri (Direct search for oil and gas and their application in Siberia). Geologiya i geofizika (Geology and Geophysics). № 4. 19. Sibiryakov B. P. , Tatarnikov M.A., Maksimov L. A. Rasprostraneniye uprugikh voln v mikroneodnorodnykh sredakh, soderzhashchikh flyuidy (propagation of elastic waves in microinhomogeneous media containing fluids). Review. Novosibirsk.1978. 20. Tacham, R. E., & Stoffa, Р. L. (1976). VP/VS—potential hydrocation. Geophysics, 41(5), 837– 1058. 21. Klushin S.V., Trofimov V.L., Isayenko M.N. et al. 1987 Trekhkomponentnyye skvazhinnyye issledovaniya na territorii Belorussii (Three-component borehole surveys on the territory of Belarus). Collection “Mnogovolnovyye seysmicheskiye issledovaniya” (“Multiwave seismic surveys”). Nauka. pp.112-119. 22. Fizicheskiye svoystva gornykh porod i poleznykh iskopayemykh (petrofizika). (1984). (Physical properties of rocks and minerals (petrophysics)). Spravochnik geofizika / Pod red. N.B. Dortman (Geophysicist Handbook edited by N.B.Dortman). Second Edition. M., Nedra. p. 455. 23. Trofimov, V. L., Lisitsa, A. I. (1986). Opredeleniye skorostnykh kharakteristik prodol’nykh i poperechnykh voln i svyazannykh s nimi parametrov razreza po dannym PM VSP (Determination of velocity characteristics of longitudinal and transverse waves and related parameters of the section according to PM VSP data). Collection “Novyye rezul’taty geofizicheskikh issledovaniy v Belorussii” (New results of geophysical research in Belarus). BelNIGRI. pp. 82-93. 24. Oblogina, T. I., Yudasin, L. A., & Ivanov, O. P. (1972). Metod preobrazovaniya vremennykh razrezov v glubinnyye (The method of transforming time sections into deep ones). VINITI. Reg. № 4628-72. 25. Trofimov V.L., Oblogina T.I. Polya skorostey i gradiyentov skorostey uprugikh voln i ikh ispol’zovaniye v seysmorazvedke (Fields of velocities and velocity gradients of elastic waves and their use in seismic exploration). Book: Metody razvedochnoy geofiziki v BSSR (Exploration geophysics methods in BSSR). Сollection of scientific papers. BelNIGRI. pp. 68-80. 26. Trofimov, V. L., Khaziev, F. F., & Chernikov, D. I. (2010). Resheniye pryamoy i obratnoy zadach preobrazovaniya glubinnykh i vremennykh kinematicheskikh razrezov dlya ryada

234

5 Processing and Automated Interpretation of Well Logging Data

skorostnykh funktsiy V(z) i V(x,z) (Solution of direct and inverse problems of transformation of depth and time kinematic sections for a number of velocity functions V(z) and V(x,z)). Tekhnologii seysmorazvedki (Seismic Technologies), 4, 18–25. 27. Sаttlеgger, J. W. (1964). Series for three-dimensional migration in reflection seismic interpretation. Geopyhsical Prospecting, 12(1), 115–134. 28. Spravochnik geofizika (Geophysicist Handbook). (1976). Fizicheskiye svoystva gornykh porod i poleznykh iskopayemykh (petrofizika) (Physical properties of rocks and minerals (petrophysics)). M., Nedra. 29. Seysmorazvedka: Spravochnik geofizika (Seismic survey: Geophysicist handbook). (1990). Two volumes edited by Nomokanova. Volume 2. Second Edition. M., Nedra. p. 400. 30. Klushin, S. V., Trofimov, V. L. Isayenko, M. N. et al. (1984). Osnovnyye rezul’taty i perspektivy razvitiya polyarizatsionnogo metoda seysmorazvedki v BSSR (The main results and prospects for the development of the polarization method of seismic prospecting in the BSSR). Book: Geofizicheskiye issledovaniya na neft’ v Belorusskiy SSR (Geophysical Research for Oil in the Belarusian SSR ). Minsk: Nauka i tekhnika (Science and Technology). pp. 31–60. 31. Zalyayev, N. Z. (1981). Kompleksnaya interpretatsiya geofizicheskikh parametrov funktsional’nymi preobrazovaniyami s pomoshch’yu EVM (Complex interpretation of geophysical parameters by functional transformations using a computer) (p. 150). BelNIGRI. 32. Ivakin, B. N., Karus Ye, V., & Kuznetsov, O. L. (1978). Akusticheskiy metod issledovaniya skvazhin (Acoustic well survey method) (p. 320). Nedra. 33. Kuznetsov, O. L., & Simkin, E. M. (1990). Preobrazovaniye i vzaimodeystviye geofizicheskikh poley v litosfere (Transformation and interaction of geophysical fields in the lithosphere) (p. 270). Nedra. 34. Pisetskiy, V. B. (2006). O vybore paradigmy v metodakh prognoza flyuidnykh parametrov po seysmicheskim dannym (On the choice of a paradigm in methods for predicting fluid parameters from seismic data). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 19–29. 35. Ibrayev, V. I. (2006). Prognozirovaniye kollektorov i flyuidouporov neftegazovykh zalezhey v Zapadnoy Sibiri (Prediction of reservoirs and seals of oil and gas deposits in Western Siberia). Tyumen’. OAO «Tyumenskiy dom pechati», p. 208. 36. Barenblatt, G. I., Yentov, V. M., Ryzhik, V. M., (1984). Dvizheniye zhidkostey i gazov v prirodnykh plastakh (The movement of liquids and gases in natural formations). M., Nedra. 37. Trofimov V.L., Abulashvili V.U., Khaziev F.F. 1990 Metodika izucheniya napryazhennogo sostoyaniya gornykh porod v oblastyakh vozmozhnykh zemletryaseniy (Methods for studying the stress state of rocks in areas of possible earthquakes). Republican scientific and technical seminar “Assessment of the economic activities impact on the geological environment”. BelNIGRI. pp. 97–99. 38. Babenko, I. A., Fedotov, S. L., Nekrasova, T. V., Yevdokimova, M. L., & Krylova, M. V. (2012). Osobennosti ispol’zovaniya inversionnykh tekhnologiy dlya prognoza kollektorov na shel’fe Okhotskogo moray (Features of the inversion technologies use for predicting reservoirs on the Okhotsk Sea shelf). Scientific and technical bulletin of OJSC “Rosneft”, Geology and Geophysics. № 3. Release 28. pp. 12–15. 39. Piyp, V. B., & Oblogina, T. I. (1973). Vosstanovleniye dvumernoy skorostnoy funktsii metodom podbora (Reconstruction of the two-dimensional velocity function by the fitting method). M., Nedra. 40. Zenov, A. A., Malkin, A. L., Sorin Ya A., & Finikov, D. B. (1985). Sovremennyye metody obratnoy i korrektiruyushchey fil’tratsii seysmicheskikh zapisey (Modern methods of inverse and corrective seismic records filtering). M., VNIIOENG. Overview information. Seriya neftegazovaya geologiya i geofizika (Oil and gas series in geology and geophysics). p. 60. 41. Voskresenskiy Yu, N. (2001). Izucheniye izmeneniy amplitud seysmicheskikh otrazheniy dlya poiskov i razvedki zalezhey uglevodorodov (Study of changes in the seismic reflections amplitudes for prospecting and exploration of hydrocarbon deposits). Study guide for students of specialties 650200, 650100, 553200. Russia. Gubkin University. M. p. 68.

References

235

42. Vychislitel’nyye matematika i tekhnika v razvedochnoy geofizike (Computational mathematics and technique in exploration geophysics). (1990). Geophysicist Handbook edited by V.I. Dmitriyeva. Second Edition. M., Nedra. p. 498. 43. Gal’perin Ye, I. (1977). Polyarizatsionnyy metod seysmicheskikh issledovaniy (Polarization method of seismic research) (p. 137). Nedra. 44. Gal’perin Ye, I. (1982). Vertikal’noye seysmicheskoye profilirovaniye (Vertical seismic profiling). Nedra. 45. Gal’perin Ye, I. Frolova, A. V., & Gal’perina R.M. et al. (1984). Metodicheskiye rekomendatsii po primeneniyu polyarizatsionnogo metoda seysmicheskoy razvedki (Methodological recommendations for the application of the polarization method of seismic exploration). Alma-Ata, KazVIRG, p. 184.

Chapter 6

Elastic Wave Velocity and Velocity Gradient Fields for Heterogeneous Geological Media

Abstract Elastic wave velocity and their gradient fields are used to study heterogeneous geological media. Materials describing the separation of the lithological and structural-tectonic features of the medium using the modulus of the true velocity gradient vector and its angle in the plane of the seismic section are presented. In this case, the value of |grad V(x, z)| is due to changes in the lithological composition of rocks in the studied medium, and the angular function θ(x, z) is related to the structural features of the section (the geometry of the medium). The chapter describes the features of the methodology for constructing velocity fields and their gradients based on the use of data from the velocity analysis of seismograms, and presents the results obtained on one of the CDP seismic profiles. The developed methodology for studying the velocity fields and velocity gradients of seismic waves allows us to extract specific quantitative information about the section from the CDP materials in a convenient and easily interpreted form. Based on the use of this technique, it is shown that it is possible to identify local inhomogeneities of the real geoenvironment associated with horizontal lithology variability and tectonic disturbances.

Higher requirements for exploration in the search for hydrocarbon deposits cause an increase in the detail and reliability of seismic exploration by the CDP method. When interpreting seismic data, the transition from the use of the wave field to the fields of acoustic impedance and reflectivities provides knowledge of the detailed velocity field distribution and its derivatives in 2D and 3D heterogeneous media. These data are largely necessary for obtaining detailed characteristics of the thinlayer medium in the formulation and solution of the inverse dynamic seismic problem. In addition, data on the distribution of velocity fields and velocity gradients in inhomogeneous media are also used independently to interpret seismic data, if the necessary detail and accuracy of their determination are provided. Horizontal-layered models of media installed according to the data of integral seismic logging or processing of CDP seismograms in vertical sections can be used to solve a relatively limited range of seismic kinematic problems [1]. When solving problems of structural geology, seismic methods often do not take into account © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_6

237

238

6 Elastic Wave Velocity and Velocity Gradient Fields for. . .

horizontal velocity heterogeneities, as a result of which the accuracy of kinematic methods for constructing horizons turns out to be low, especially when identifying low-amplitude structures. A special role is acquired by a detailed velocity study under the conditions of the strong velocity heterogeneities. More accurate information about seismic velocity is required. As is known, there is no general solution to the inverse kinematic seismic problem for 2D inhomogeneous media. At the same time, the possibility of using numerical methods and computer technology makes it possible to reasonably approach a number of inverse seismic problems, including kinematic ones, and to develop new approaches to interpreting the results of seismic observations. Two-dimensional heterogeneous models, in which the velocity of elastic wave propagation is an arbitrary continuous and differentiable function of the medium point coordinates, are a natural generalization of layered models. Features of the direct and inverse kinematic seismic problem solution for some velocity functions V(x) and V(x,z) in heterogeneous media are given in [2]. For media with a variable velocity V(x, z), new characteristics of the medium— velocity fields and their gradients—were introduced in [3]. The concept of the velocity field has become particularly widely used in the interpretation of deep seismic sounding (DSS) data. With the introduction into practice of developments related to the inversion of wave fields into the AI and RC fields, the need arose for information about the trend (background) model of acoustic impedance in a heterogeneous medium [4–6]. This information is necessary for the transition from relative acoustic impedance to absolute one and is actually a procedure for calibrating the acoustic impedance field. The solution of the inverse kinematic problem—finding the velocity distribution V(х, z) in a medium over the hodograph system on its surface—is implemented differently: using a special differential equation with a shifted argument [7], using the integral geometry method in the linearized setting [8], the selection method based on the similarity theory [9], and others. As for media where the logarithm V(х, z) is a harmonic function, the inverse problem of seismic exploration can be reduced to the Dirichlet problem for the half-plane [10]. It also uses the method of Tikhonov regularization. In [11], the idea was put forward about the separation of lithological and structural tectonic features of the medium using the modulus of the gradient vector of the true velocity and its angle in the plane of the seismic section. This idea is that the magnitude |grad V(x, z)| is due to a change in the lithological composition of the rocks in the studied medium, and the angular function is associated with the structural features of the section (the geometry of the medium). The following describes the features of the methodology for constructing velocity fields and their gradients based on the use of velocity seismic analysis data, the results obtained on one of the CDP lines, which is located in the central structural tectonic zone of the Pripyat Trough, and an analysis of the results in terms of the proposed idea. The previously developed technique was tested by the authors on well measurement materials [12].

6 Elastic Wave Velocity and Velocity Gradient Fields for. . .

239

Fig. 6.1 CDP time section along the line 781 (ti is time lines of the main reflectors)

Before building the velocity fields and their gradients along the CDP line, the time section of which is shown in Fig. 6.1, a velocity analysis of seismic gathers using a fairly dense network of observations was implemented. Velocity analysis was performed in vertical sections with an interval of 0.5 km. At each section of the CDP seismogram, the kinematic VCDP(t0i) and energy ρ(t0i,τ) characteristics of the reflected waves were found based on a search of hyperbolic hodograph varying in time and distance with a constant increment at the edge of the stacking base and using signal extraction algorithms. The obtained data of the stacking velocities VCDP(t0i), for which the energy signal-to-noise ratio exceeded 0.25 on each of the cross sections, was analyzed for the presence of distorted values (concentrated noise). This noise was edited by antialiasing (low-pass filtering). Further, the values VCDP(xk, t0i) were used as input data for determining the velocity characteristic of the medium, which provides for obtaining graphs of average velocities Vav(xk, t0k, i) as tabular values on veritcals хk of the time section at discrete points t0k,i. Following data processing consisted in recalculating the average velocities into conditional vertical hodographs according to the rule: the times remain the same with the values, and the depths are calculated by the formula: zk,i ¼ V av ðxk , zk,i Þ  t k,i

ð6:1Þ

The calculations that precede directly constructing the velocity field V(x,z) and the velocity gradient field |grad V(x, z)| are based on the use of smoothing methods, numerical differentiation of conventional vertical hodographs tk, i = t(xk, zk, i) and interpolation of the function given in a table on a set of verticals.

240

6 Elastic Wave Velocity and Velocity Gradient Fields for. . .

Values of the true velocity for each of the sections are defined as the reciprocal of the smoothed vertical hodograph derivative: 1 V ðzÞ ¼ dt

=dz

ð6:2Þ

Smoothing of the hodograph values was performed by a sliding polynomial of a given degree n passing through m points of the vertical hodograph. In this case, the coefficients of the approximating polynomial are determined by the least squares method from a system of (n + 1) linear equations. Numerical simulation shows that the shape of the true velocity curve, obtained by differentiating a vertical hodograph smoothed by a sliding polynomial, significantly depends on the smoothing parameters n and m. When analyzing the graphs of the velocity V(z), constructed with different values of the parameters n and m, it is possible to obtain only the most general idea of which set of curves V(z) can be chosen specific curves with those or other parameters. Since there are no general criteria for choosing the smoothing parameters of experimental curves, in our problem solution, the ray orthogonality condition to the reflecting horizons proposed by T.I. Oblogina was used; the violation of this condition with certain smoothing parameters corresponds to the cases when the selected velocity characteristic is not consistent with the actual velocity distribution in the real environment. The verification of the ray orthogonality condition to the desired reflecting horizons as a criterion for choosing the optimal smoothing parameters for vertical hodographs n and m is included in the method for converting kinematic time sections into deep ones for media with variable velocities V(x,z) developed at Moscow State University [13]. In this method, using a system of normal rays and travel times along them, a one-to-one correspondence is established between the points of the deep reflecting horizon and the points of the time line. It is assumed that either the true velocity versus depth curves, determined from well observation data, or graphs of effective or average velocities are specified. The velocity distribution is approximated by a 2D velocity function V(x, z), which is taken as a polynomial of arbitrary degree n, over the set of values (x, z). The transformation of time sections into deep ones is reduced to the problem of numerical integration of a first-order ordinary differential equation system with Cauchy initial conditions [2]. The ray point coordinates at the corresponding points of observation are calculated. The exit angle of normal rays is in accordance with the apparent velocity law by differentiating the time line. For the time section horizons in Fig. 6.1, four variants of parameter combinations n and m were calculated. By the orthogonality condition, the combination of the parameters n ¼ 3 and m ¼ 9 turned out to be optimal. The depth model, corresponding to the optimal version of the parameters n and m, is shown in Fig. 6.2. The relative errors in orthogonality do not exceed 1.0–1.5%. Note that after choosing the optimal smoothing parameters for conventional vertical hodographs in order to best match the true velocity curve to its actual distribution in the real environment at intermediate points between the vertical

6 Elastic Wave Velocity and Velocity Gradient Fields for. . .

241

Fig. 6.2 Depth section along line 781 through the well 4

sections, which was used for the CDP gather velocity analysis, the true velocity V(х, z) was determined by x and z value interpolation. To do this, you can use different interpolation method (spline interpolation, Bois method, standard interpolation procedure according to the Lagrange polynomial). In this problem, interpolation using the piecewise cubic Lagrange polynomial was used, which in this case has an advantage over other interpolation methods of the 2D velocity function V(х, z) due to speed and relatively high interpolation accuracy. The values V 0x ðx, zÞ and V 0z ðx, zÞ were calculated by the standard program of function interpolation and definition of derivatives according to the Aitken scheme. The values of the vertical and horizontal components of the velocity gradient are used to calculate the modulus of the total gradient vector: s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2  2 ∂V ðx, zÞ ∂V ðx, zÞ þ jgrad V ðx, zÞj ¼ ∂x ∂z

ð6:3Þ

and its angle with the z axis: θðx, zÞ ¼

V 0 ðx, zÞ 180  arctg x0 π V z ðx, zÞ

ð6:4Þ

where 180 π is the factor that converts the radian angle measure to the degree one. The angular function, standing in the expression (6.4), is determined by its main value: 

π 2


0) and Vp, ν, Gz, E, K, Rp, φ, and VSN curves have the left one (As < 0). The shape of the experimental distribution curve for each of the analyzed variables is estimated from the values of the normalized fourth-order central moment— the excess (the kurtosis). From Table 7.1, it can be seen that the density curves of almost all analyzed geological and geophysical parameters have peaked distributions in comparison with the density of the normal distribution. Of these, the curves Rs, Ko, Kp.o., Rp, Gz, Jnl, Kp.f., Rp/Rs and A differ by the highest and “sharp” vertex in relation to other data. The analysis of the coefficients As (asymmetry) and EK (kurtosis) indicates a significant difference between the experimental distributions of the analyzed variables from the normal. In such a situation, so that the experimental distribution laws of the quantities under study and their weight residues are close enough to normal, it is advisable to resort to either replacing the variables—to transform them—or to shorten the research interval (up to individual lithologic stratigraphic packs). Before the start of a complex analysis, the editing of materials for which dependencies are determined, in order to eliminate outliers or errors in the data set, is also important. Based on the results of step regression programs, partial correlation, and multidimensional regression, the choice of the “best” multiple linear regression based on the F-criterion testing for including and excluding variables, the following direct multidimensional dependencies were obtained (direct named, by analogy with [5]; such relationship models, which express one of the physical properties through its reservoir; lithological and structural ones): V p ¼ 5612:152
35:649  K p:bn: þ 3:282  K w:res: , V s ¼
337:016 þ 1240:163  σ þ 2:439  K w:res:
64:559  K p:o:
13:261  K o:f :

ð7:1Þ ð7:2Þ

1=γ ¼ 1:844
0:010  K o þ 0:047  K p:o: ,

ð7:3Þ

ν ¼ 0:298
0:004  K o þ 0:16  K p:o: ,

ð7:4Þ

αp =αs ¼ 0:163 þ 0:0029  K o
0:013  K p:o: ,

ð7:5Þ

7 Determination of Dependencies between Geological and. . .

260

Gz ¼
0:830 þ 0:617  σ
0:003  K w:res: ,

ð7:6Þ

Gxy ¼ 0:308
0:0018  K o ,

ð7:7Þ

E ¼
0:393 þ 0:265  σ þ 0:0007  K w:res:
0:0035  K p þ 0:0007  K o ,

ð7:8Þ

K ¼ 0:625
0:016  K cl þ 0:0036  K o þ 0:011  K p:ef f : ,

ð7:9Þ

μ ¼
0:556 þ 0:296  σ þ 0:0013  K o þ 0:0004  K w:res:
0:0023  K o:f : ,

ð7:10Þ

λ ¼ 0:379
0:0038  K o
0:011  K cl ,

ð7:11Þ

Rp ¼ 0:0067
0:013  K p:o: ,

ð7:12Þ

Rs ¼ 0:0038
0:081  K p:o: ,

ð7:13Þ

E Σ ¼ 0:393
0:1428  σ þ 0:0028  K p
0:005  K p:o: ,

ð7:14Þ

A ¼ 1:630 þ 0:027  K p:o:
0:453  σ,

ð7:15Þ

φ ¼ 69:651 þ 0:572  K p:ef f : þ 0:982  K p:o:

ð7:16Þ

With the same set of arguments, the interrelation equations for initial well field geophysical characteristics are calculated: V SN ¼
5063:438
60:893  K p þ 4222:168  σ
23:289  K p:bn: , J gr ¼ 1:017 þ 0:136  K p:bn: þ 0:159  K cl þ 0:137  K p
0:087  K p:ef f : , J nl ¼ 4:662
0:168  K p
0:011  K w:res:

ð7:17Þ ð7:18Þ ð7:19Þ

The coefficients in the presented regression equations are arranged in order of decreasing the influence of independent variables on the dependent ones. Using the well-known scale of multiple correlation coefficient |r|: | r | < 0.3 0.3  | r |  0.5 0.5  | r |  0.7 0.7  | r |  0.9 | r |  0.9

– – – – –

Weak relationship Moderate Significant Strong Very strong (functional practically)

as well as the square of multiple correlation (determination coefficient), which is the dispersion proportion of dependent variables on the corresponding independent ones, it is possible to estimate the quality of dependent variables approximation (relationship degree) of the multiple linear regression equation (Table 7.2). Only Eq. (7.17) has a very strong, practically functional relation; strong relation is the characteristic of Eqs. (7.2), (7.6), (7.8), (7.10), (7.18), and (7.19); a significant relationship is for Eq. (7.14); moderate is for Eqs. (7.1), (7.3), (7.4), (7.5), (7.9), (7.11), (7.12), (7.13), (7.16); and weak is for Eqs. (7.7) and (7.15). Regression equations are found through Eqs. (7.2), (7.6), (7.10), (7.14), (7.17), (7.18), and (7.19) better than other regression equations explaining the data

7.1 Multidimensional Dependence Determination Between Seismic and Well. . .

261

Table 7.2 Multiple correlation, determination coefficients, and tests of significance for dependency Eqs. (7.1)–(7.19) Dependent variable 1 Vp Vs 1/γ ν αp/αs Gz Gxy E K μ λ Rp Rs EΣ A VSN φ Jgr Jnl

Multiple correlation coefficient 2 0.3994 0.7051 0.3949 0.4584 0.4263 0.7415 0.2518 0.7060 0.3927 0.7437 0. 3447 0.4212 0.3112 0.5516 0.2550 0.9653 0.4058 0.8603 0.7186

Determination coefficient 3 0.1595 0.4971 0.1559 0.2101 0.1817 0.5498 0.0634 0.4985 0.1542 0.5531 0.1188 0.1774 0.0968 0.3042 0.0650 0.9317 0.1647 0.7401 0.5164

F statistics 4 14.42 29.46 14.04 34.92 16.88 92.82 10.36 37.27 9.18 46.41 10.25 32.99 16.40 22.01 5.29 686.78 14.98 106.79 81.14

Significance (ρ is less than) 5 0.0000 -” -” -” -” -” -” -” -” -” -” -” -” -” -” -” -” -” -” -

variation. This can be seen both from the determination coefficient estimates |r| and from the values of F statistics characterizing the statistical significance of regression models (Table 7.2). To evaluate the dependence measure of each dependent variable with all the other dependent variables in Eqs. (7.1)–(7.16), the correlation matrix was analyzed only by seismic indicators. As a result, a degenerate matrix was obtained, which is characterized by the presence of a linear relationship between a separate set of seismic variables, presented in Table 7.3. The influence of all the other dependent variables on each remaining dependent ones can be estimated by the magnitude of the multiple correlation squares and the criteria of significance presented in Table 7.4. The column “Significance (ρ is less than)” (Tables 7.2–7.4) shows the result of testing the null hypothesis of multiple regression coefficients between dependent variables H0: βm ¼ 0. From the data obtained, it follows the hypothesis that the contribution to the prediction of each of the dependent variables with fixed values of other dependent parameters insignificant is erroneous. It is obvious that the presence of dependent variables very strong, linear, practically functional relationship (Table 7.3), and strong for many other

7 Determination of Dependencies between Geological and. . .

262

Table 7.3 The linear relationship of individual dependent variables. Variable 1 Vp αp/αs E K μ λ

Quadratic multiple correlation 2 0.9988 0.9922 0.9958 0.9917 0.9973 0.9964

F-statistics 4 10841.10 1661.50 3052.15 1556.32 4744.21 3548.69

Significance (ρ is less than) 5 0.0000 -” -” -” -” -” -

Table 7.4 Multiple correlation squares and significance criteria for each dependent variable with the rest of the dependent variables. Variable 1 Vs 1/γ ν Gz Gxy Rp Rs Rp/Rs EΣ A φ

Quadratic multiple correlation 2 0.5080 0.8689 0.5200 0.6660 0.8641 0.8446 0.8879 0.3281 0.6453 0.6459 0.5324

F statistics 4 21.42 95.45 15.60 27.71 91.56 78.24 114.04 7.03 26.20 26.24 16.40

Significance (ρ is less than) 5 0.0000 -” -” -” -” -” -” -” -” -” -” -

parameters (Table 7.4) is the main prerequisite for reducing the dimension of the seismic ones under study. The obtained direct multidimensional dependences (Eqs. (7.1)–(7.19)) on the basis of an optimally selected combination of variables can be taken as natural and physically explained. To establish inverse multidimensional dependencies (inverse, by analogy with [5, 13], such relation models are named that express one of the reservoir, lithological, or structural properties of the section through its physical properties), the same programs of step regression, partial correlation, and multidimensional regression. On the basis of these data obtained below were used. For scattered clay: K cl ¼ 36:545
17:450  E
32:321  Gz þ 20:844  Rs þ 0:124  Rp =Rs ,

ð7:20Þ

Fictitious saturation: K o:f : ¼
5:882 þ 44:497  Rs
53:933  Rp þ 0:202  φ þ 0:044  EΣ
10:290  K,

ð7:21Þ

7.1 Multidimensional Dependence Determination Between Seismic and Well. . .

263

Effective porosity: K p:ef f : ¼
4:419 þ 38:011  Rs
43:488  Rp þ 0:100  φ
12:518  Gxy þ 11:446  μ
0:164  Rp =Rs ,

ð7:22Þ Oil-saturated capacity: K p:o: ¼
24:707 þ 9:622  1=γ þ 46:099  αp =αs
28:553  Rp þ 16:104  Rs
0:374  ν,

ð7:23Þ Oil saturation: K o ¼ 31:641
223:282  Rp þ 157:542  Rs
1:747  ν þ 119:950  αp =αs þ 54:099  E,

ð7:24Þ Bound water saturation: K w:res: ¼
1:392
478:090  Rp þ 365:847  Rs
122:790  Gxy þ 236:600  μ
1:917  Rp =Rs ,

ð7:25Þ Density: σ ¼ 1:968 þ 0:721  Gz þ 0:407  E,

ð7:26Þ

Porosity ratio: K p ¼ 16:960
20:500  Rp þ 0:051  E Σ þ 0:171  φ
20:963  Gz
9:388  K,

ð7:27Þ

Bound porosity: K p:bn: ¼ 2:707 þ 0:044  E Σ
37:772  E þ 0:209  φ þ 0:259  Rp =Rs

ð7:28Þ

The quality of dependent variables approximation by regression equations (7.20)–(7.28), as in the analysis of direct dependencies, can be estimated by the multiple correlation coefficient | r |, the determination coefficient, and the corresponding criteria of these equations’ significance (Table 7.5). Using the above scale of |r|, it can be noted that equations (7.24), (7.26), and (7.27) are characterized by a strong relationship, and for the other equations—(7.20), (7.21), (7.22), (7.23), (7.25), and (7.28)—this relation is significant. In evaluating the effect of each dependent variable on the remaining dependent these in the equations of relationships (7.20)—(7.28), the correlation matrix of simple correlation coefficients presented in Table 7.6 was analyzed.

264

7 Determination of Dependencies between Geological and. . .

Table 7.5 Multiple correlation, determination coefficients, and tests of significance for dependency (7.20) to (7.28) Dependent variable 1 Kcl Ko.f. Kp.eff. Kp.o. Ko Kw.res. Σ Kp Kp.bn.

Multiple correlation coefficient 2 0.6786 0.6618 0.6272 0.6371 0.7443 0.6867 0.8022 0.7102 0.5933

Determination coefficient 3 0.4605 0.4380 0.3933 0.4058 0.5540 0.4634 0.6435 0.5044 0.3520

F statistics 4 32.01 23.38 16.10 20.49 30.83 25.90 137.21 30.53 20.51

Significance (ρ is less than) 5 0.0000 -” -” -” -” -” -” -” -” -

From this table it follows that a strong relationship logically manifests itself between the variables Ko (oil saturation) and Kp.o. (oil-saturated capacity) with a correlation coefficient of
0.791. A significant relationship is also observed in the variables Kw.res. (bound water saturation) and Kp.eff. (effective porosity) with a correlation coefficient of
0.599. It is obvious that out of two pairs of variables, one can be left only one at a time, i.e., reduce the dimension of the studied space of signs without prejudice to the information content of the variables set, which was adopted at the beginning. To assess the relation of each dependent variable with all other independent ones selected by the step-by-step procedure P2R as their optimal set, the regression equations (7.20)–(7.28) considered the correlation matrices of simple correlation coefficients (for brevity, Table 7.7 shows an example for one model—(7.25)). In this case, a strong relationship is manifested in a pair of variables Rp and Rs with a correlation coefficient r ¼
0.856. The remaining independent variables are practically weakly interrelated, which indicates the relatively good quality of the adopted model. In addition to calculating various statistical estimates and experimental data criteria, the software used [8] makes it possible to obtain materials in graphical form, effectively edit them, and evaluate the shape of the approximating curves. A number of graphical image examples of the statistical analysis results are shown in Figs. 7.3 and 7.4. In Fig. 7.3a, the predicted function graph of the well field geophysical parameter Ko.f. (7.21) (approximation regression values Ŷi) was superimposed on the charts of observed values of Ko.f. (function Yi). This graph shows how the observed values of the function are approximated by linear multiple regression using the least squares method. The quality of such an approximation, in addition to the statistical characteristics presented above in various tables, can be assessed on the basis of the residuals analysis, the diagram of which is shown in Fig. 7.3b. The residuals are the deviations of each observed value of the function Yi from the approximating regression Ŷi, i.e., difference [7]:

Variable Kcl Ko.f. Kp.eff. Kp.o. Ko Kw.res. σ Kp Kp.bn.

Kcl 1.000
0.442 0.051 0.051 0.184 0.206
0.282
0.413
0.369 Kp.eff.

1.000 0.017
0.146
0.599 0.001
0.129 0.049

Ko.f.

1.000
0.128
0.105
0.242 0.007 0.266 0.000
0.245 1.000
0.791
0.067 0.005
0.094 0.034

Kp.o.

1.000 0.010
0.162 0.022 0.040

Ko

1.000
0.246
0.284 0.105

Kw.res.

Table 7.6 Simple correlation coefficients between dependent the variables in the relationship Eqs. (7.20)–(7.28)

1.000 0.354
0.004

σ

1.000
0.302

Kp

1.000

Kp.bn.

7.1 Multidimensional Dependence Determination Between Seismic and Well. . . 265

7 Determination of Dependencies between Geological and. . .

266

Table 7.7 Simple correlation coefficients matrix between the variables in Eq. (7.25) Variable Gxy μ Rp Rs Rp/Rs

Gxy 1.000 0.247 0.478
0.355 0.268

M

Rp

Rs

Rp/Rs

1.000 0.192 0.008 0.002

1.000
0.856
0.011

1.000 0.057

1.000

Fig. 7.3 Dependency graphs and diagrams: (a) the graph of the predicted function on the observed value diagram, (b) the diagram of the residuals: (1) the graph of the predicted function; (2) the position of the observed values; (3) points with the number of correlated value hits; and (4) scattering ellipse

ei ¼ Y i
Y^ i , ði ¼ 1, 2, 3, . . . , nÞ In Fig. 7.3b are these residues of the function Ko.f. grouped approximately within the limits of their “own” set—the scattering ellipse. One can see how, with respect to this ellipse, “distinguished” or “doubtful” values of the residuals and the corresponding values of the parameter itself are found. The graph of the normal probability distribution of residuals is shown in Fig. 7.4a. The argument of the empirical distribution function is plotted on the horizontal axis, and the corresponding values of the normal distribution function are on the vertical axis. The analysis of materials using this graph determines the nature of the predicted value distribution, which will be close to the normal distribution if the residual graph

7.1 Multidimensional Dependence Determination Between Seismic and Well. . .

267

Fig. 7.4 Residues graph of normal probability distributions according to Ko.f. ¼ f(RS, RP, φ, EΣ , K): (a) the graph of the normal distribution of residues, (b) the graph of the normal distribution of residues with a remoted linear trend

is approximately a straight line. In Fig. 7.4a it can be seen that this graph is close to a linear trend in its middle part and is characterized by some nonlinearity at the edges. In combination with the graph (Fig. 7.4a), the graph of the normal distribution of the residuals probabilities with the linear trend removed shown in Fig. 7.4b helps to identify possible “defects” of the desired regression models. If the residual graph (Fig. 7.3b) is a “band” of approximately constant width relative to their zero level, then the values of ei within this “band” have a constant dispersion, and the desired regression Ŷ satisfactorily approximates the observed values of the function Yi. With a substantially curvilinear nature of the residual graph with respect to their zero level, the existing curvature of graph e, first of all, indicates the inadequacy of the model. The graph of residues (Fig. 7.4b) shows that it has a curvature along the edges. In the middle part, there is a “band” of fairly constant residuals probabilities relative to the zero level. All the graphs (and tables) shown above confirm the initial prerequisites for the close interrelation of seismic characteristics with the lithological composition, reservoir properties, and oil and gas saturation of rocks. Thus, the considered method of studying the effect of geological indicators on the acoustic and dynamic characteristics of seismic record and determining the relationships between them is a good physical and geological basis for analyzing the extremely complex interference structure of the wave field and studying various combinations of the composition and properties of the section being studied in conjunction with seismic characteristics. On this basis, it is possible to establish some specific features of seismic data interpretation—in particular, to solve the problem of estimating the error of stratigraphic binding of reflected waves to

268

7 Determination of Dependencies between Geological and. . .

geological boundaries and to investigate the process of mapping the seismic signals of weak intensity in the structure of the seismic record dynamics.

7.2

Petrophysical Equation System in the High-Resolution Seismic Method

At the final stage of the complex interpretation of observational materials in accordance with the technological scheme of dynamic data processing (widely using the procedures of the HRS-Geo technology), the prediction of the composition and properties of oil prospective sediments is performed. This is very important for the entire process of prospecting and industrial exploration of oil and gas deposits. The subsection discusses one of the algorithms and the results of predicting various geological indicators obtained using the high-resolution data in the form of 2D lines or 3D cubes of effective RC and AI. Implemented research using the HRS-Geo technology programs are focused primarily on extracting information about the lithological composition, reservoir properties, and oil saturation of reservoir rocks from seismic data [14–18]. As was shown in Sect. 2.1 and partly in other subsections, it is not possible to solve such a task based on using only the features of the wave field structure with the required accuracy. One of the main reasons for this, as already noted, is the comprehensive interference of reflected seismic waves. As a result of such wave interference in each of the amplitudes, on each discrete seismic record, additive information is recorded from the above local acoustic heterogeneities. This information is recorded in amplitudes in different ratios, proportional to the acoustic contrast above the adjacent (relative to the considered time discrete) thin-layer interfaces, in the form of local contribution curves—seismic responses—formed from each specific inhomogeneity, as was shown in a detailed seismic modeling in Chap. 2. As a result of solving the inverse dynamic seismic problem, the information on geological section extracted from seismic data becomes comparable to the results of processing and interpreting GBS data (as shown in Chap 4). According to seismic records, the acoustic model of the medium is restored (due to the inversion procedure application)—the seismic trace is converted into the impulse response of that model. From these traces, sections of effective RC and AI are formed, in which samples of AI values along the vertical follow with a seismic record discretization step over time. Practical vertical resolution of terrigenous sections in this case is 3–4 m and carbonate ones 5.5–6.5 m (at the sampling step of seismic recording Δt ¼ 0.002 s and the velocity of elastic oscillations propagation in the terrigenous sect. V ¼ 3000–4000 m/s and in the carbonate that V ¼ 5500–6500 m/s); when Δt ¼ 0.001 s, the resolution doubles, that is, 1.5–2.0 m and 2.7–3.7 m for the same values of the elastic wave traveling velocity. There are various approaches for assessing geological and geophysical parameters. The classic basis for predicting the characteristics of the environment is a

7.2 Petrophysical Equation System in the High-Resolution Seismic Method

269

preliminary study of the parameter distribution of the investigated medium according to measurements in wells, analysis of drilling data, and laboratory measurements of acoustic and other properties of the core. Seismic data is directly related to the distribution of the medium acoustic properties, therefore, the determination of the acoustic-geophysical parameters is reduced to the search for dependencies between the estimated parameters (lithology, porosity, degree, and character of saturation) and acoustic properties (velocity, density, acoustic impedance, reflection coefficients, and elastic deformation characteristics). Before the direct prediction of the desired parameters, the section interval under study is compared with the physical parameters previously obtained for this interval from the GBS data. And then, the geological and geophysical indicators are determined for a given interval (reservoir) for a given lateral direction in the seismic line plane (or 3D seismic cube). Determination of the composition and properties of oil promising strata rocks in wells is carried out based on the use of GBS data by the method of functional transformations of geophysical parameters and their integration into information systems in accordance with the methods and software described in Sect. 5.1 and a number of works [14–16, 19]. In a more specific form, this is done as follows. For the prediction of the lithological composition and component of the fluid, the dependences of velocity, density, clay content, and porosity on acoustic impedance are preliminarily determined from the curves of the GBS [20]: V ¼ f ðaiÞ, ρ ¼ f ðaiÞ, Ccl ¼ f ðaiÞ, K p ¼ f ðaiÞ, where V is the velocity of elastic wave propagation in the rock, ai is the acoustic impedance, ρ is the rock density, Ccl is the clay content, and Kp is porosity. For example, for the studied sediments of an oil and gas complex one of the West Siberian oil and gas province, linear dependencies of environmental parameters on acoustic impedance were found (and for each of the areas under consideration, these approximations, as a rule, turn out to be special): V ¼ 0:3042427897  ai þ 765:7174072266,

r k ¼ 0:98566,

ρ ¼ 0:0000828390  ai þ 1:8264358044,

r k ¼ 0:86681,

where rk is the correlation coefficient between the independent (ai) and dependent parameters (V, ρ). An example of the clay content dependence on the acoustic impedance for deposits of the upper clay part of the Jurassic complex (lithologic stratigraphic analogue of the Bazhenov and Abalaki suites) is the expression: Ccl ¼
0:0210582688  ai þ 232:2260742188,

r k ¼ 0:82414:

270

7 Determination of Dependencies between Geological and. . .

For the Tyumen Formation deposits, the relations Ccl ¼ f(ai), Kp ¼ f(ai), are obtained, having the form: Ccl ¼ 0:0206550844  ai þ 95:9185562134,

r k ¼ 0:61840,

K p ¼
0:0048715849  ai þ 45:4391937256,

r k ¼ 0:620935:

For the model of a two-component lithological composition (clay, sandstone), the sandiness of the formation is determined from the expression: C sand ¼ 100
C cl
K p : From the equations of average time (Wyllie M. R., Gregory A. R., 1956) and the model for calculating the equivalent specific physical property of a two-component mixture (Boganik V.N., 1983), we have:
Δt ¼ Δt f  K p þ Δt m  1
K p ,
ρ ¼ ρ f  K p þ ρm  1
K p , where Δt is the interval time of the elastic wave in the studied formation, Δtm is the travel time of the wave along the rock matrix, ρ is the rock density, and ρm is the rock matrix density. These equations determine the contribution model of the fluid and matrix components for the wave propagation time and rock density. Next are the interval travel time of the wave for the fluid Δtf and the fluid density ρf :

Δt f ¼ Δt
Δt m  1
K p =K p , ρ f ¼ ðρ
ρm  ð1
K p ÞÞ=K p : The description of the material composition of the rocks and the fluid reservoir filling the pore space is conveniently represented as an additive linearized model—a weighted sum of the contributions of each of the components (lithology and fluid), i.e., fluid-petrophysical equations. Since during processing and seismic data inversion, information about the medium is extracted from observation materials in the form of the distribution of the effective acoustic impedance of the medium (which appears, as is known, as a product of independent wave propagation velocity and density of the component rocks ai(x, y, z) ¼ (ρ(x, y, z)  V(x, y, z)); then for the prediction of the material composition, the authors developed special systems of petrophysical Eqs. (7.29) and (7.30) regarding lithology and fluid. In these equations, as a base (original), the parameters calculated from the acoustic impedance ai(x, y, z) were used in the form of density ρ(x, y, z) and the velocity of elastic waves

7.2 Petrophysical Equation System in the High-Resolution Seismic Method

271

propagation V(x, y, z) (or inverse velocity values—specific wave propagation time Δt(x, y, z)). As a result, to determine the composition of the fluid, the system of linear petrophysical equations is solved: Δt f ¼ Δt w  C w þ Δt o  C o þ Δt g  Cg , ρ f ¼ ρw  C w þ ρo  C o þ ρg  C g ,

ð7:29Þ

1 ¼ Cw þ Co þ Cg , where Cw, Co, and Cg are the desired water, oil, and gas saturation; Δtw, Δto, and Δtg are interval times of elastic waves in water, oil, and gas; and ρw, ρo, and ρg are densities of water, oil, and gas in reservoir conditions. To analyze the lithology of a more complex, multicomponent model of the medium, the developed system of linear petrophysical equations Ccl, Csand, and Ccb is presented in the following form: Δt m ¼ Δt cl  C cl þ Δt sand  Csand þ Δt cb  C cb , ρm ¼ ρcl  C cl þ ρsand  C sand þ ρcb  C cb ,

ð7:30Þ

1 ¼ C cl þ Csand þ Ccb , where Ccl, Csand, and Ccb are the desired values of clay content, sandstone content, and carbonate one; Δtcl, Δtsand, and Δtcb are the travel times of the wave in the matrix of pure clays, sandstones, and carbonates; and ρcl, ρsand, and ρcb are the matrix densities of clays, sandstones, and carbonates in reservoir conditions. The first two equations in the systems (7.29) and (7.30) describe the contribution of each of the components to the resulting fluid (Δtf, ρf) or lithology (Δtm, ρm), and the third, the coupling equation ensures the uniqueness of the solution. The system parameters Δtw, Δto, Δtg, ρw, ρo, ρg, Δtm, ρm, Δtcl, Δtsand, Δtcb, ρcl, ρsand, and ρcb are taken or calculated on the basis of core analysis and the results of GBS data processing, given the reservoir temperature and pressure conditions and other prior data (by selecting them from the appropriate intervals of the reference wells). The possibility of a certain correction (within the physically realized compact) of these parameters allows the authors to adapt the results of the prediction of the desired geological and geophysical parameters for almost all 2D data sections of acoustic impedance (ai) or 3D volume of ai values, including in the appropriate intervals of the expert wells. The quantitative assessment of the required predictive parameters involves the determination of the volume content: clay, sand, porosity, and water, oil and gas saturations (i.e., parameters (or properties), limited by the area of research in the space between the specific top and the bottom of the productive or prospective formation). There is a certain analogy in the computing method of such volume

272

7 Determination of Dependencies between Geological and. . .

parameters with the volume calculation method of reserves (or resources) of oil and gas fields depending on the stages and prospecting and exploration methods. Predicted geological and geophysical parameters of the estimated reservoir (clayiness, sandiness, porosity, water, and oil saturations), thus, are calculated directly from the effective acoustic impedance by previously found dependencies and systems of petrophysical equations at each point along the vertical with a discretization step of seismic record by time and laterally in accordance with the mesh step of the areal grid. The translation of the found prediction parameters from the time scale to the depth one is carried out on the basis of using either constant or variable lateral seismic velocities. A direct assessment of the above prediction parameters—geological indicators— is carried out as a percentage of the total rock volume. Since each of the desired parameters varies vertically, to determine their values (and the subsequent image in the plan), the average value of the desired parameters within the stratum of the studied horizon is calculated at each of the CDP points. To calculate the oil saturation coefficients, their average values are normalized according to average porosity values. When calculating the predicted geological and geophysical components of the real subsurface medium for the studied reservoir, the following volumetric model is adopted: V tot ¼ V cl þ V sand þ V dol þ V lm þ V por , where Vtot is the volume of rock enclosed in the space between the two traceable surfaces; Vcl, Vsand, Vdol, and Vlm are the volumes of the clay, sandstone, dolomite, and limestone components within Vtot; and Vpor is the volume of open pores within the reservoir. The model of the open pore volume distribution by fluid components is presented in the form. V por ¼ V h þ V w , where Vh is the volume of hydrocarbons that occupy the space of the open pores Vpor and Vw is the volume of formation water taking the open pore space Vpor. Thus, each component to be extracted is characterized by its properties (through the volume of the solid phase (lithology) and the volume of the pore space filled with the amount of hydrocarbons and formation water, i.e., the liquid phase volume), which in a certain sense reflect the geometry of this component. Here are some examples with the results of the high-resolution seismic data transformation into geological indicators. Consider some of the results obtained in one of the areas of the West Siberian oil and gas province. Two geological and geophysical parameters in the form of the distribution of effective acoustic impedance (Fig. 7.5) and clay content (Fig. 7.6) in the sediments

7.2 Petrophysical Equation System in the High-Resolution Seismic Method

273

Fig. 7.5 Acoustic impedance distribution map of formation B (Verhnedanilov suite)

Fig. 7.6 Predicted average clay content map for formation B (top of Danilov suite): legend seen on Fig. 7.5

of the Upper Danilov subsuite (the lithologic-stratigraphic analogue of the Kimmeridgian-Voganian stage of the Bazhenov formation) are in good agreement with each other. Here, in the south-western part of the work area, the distribution of effective acoustic impedance is characterized by relatively lowered and, in the northeastern half of the area, by increased AI values (Fig. 7.5). Relatively lower values of

274

7 Determination of Dependencies between Geological and. . .

clay content ( 65–75%) are recorded in the north-east and correspondingly increased ( 90–95%)—in the south-western parts of the work area (Fig. 7.6). This zonality in the distribution of the considered geological and geophysical parameters is, first, explained by the sedimentation conditions (in the zone of increased clay formation, deeper-water facies are located, and the zone of reduced clay formation is confined to less deep-water facies). Second, this zonality is related to the placement of areas due to the distribution of various types of geological sections, in particular, Danilov, transitional, and Tutleim types. It is noted that the deposits of the Danilov suite as a whole in the region are rich in oil source, capable of generating bitumen in an amount of about 60% of its initial mass, which generally indicates possible prospects for the petroleum potential of the suite. Another example of transformations—the prediction of some basic geological indicators from high-resolution seismic data—is the results presented as a set of maps for the sediments of the upper part of the Tyumen formation, horizon Т1(Ю2): average values of oil and gas saturation (Fig.7.7), porosity (Fig. 7.8), clay content (Fig. 7.9), and oil- and gas-saturated strata (Fig. 7.10), obtained in the same area as the above materials. The distribution of average oil and gas saturation of the considered horizon sediments is shown in Fig. 7.7. The figure presents the selected contour of the moving portion of hydrocarbons corresponds to 3% and higher (this level was chosen based on the results of GBS data processing and available well test results). The most intensive expressions of oil saturation (7–13%) in the study area are observed as separate local anomalies in different parts covered by oil saturation contours. With a lower level of oil saturation manifestation, anomalous zones located on the peripheral areas of local structures are distinguished—here the level

Fig. 7.7 Predicted average oil and gas saturation map for reservoir formation Т1(Ю2) (the upper part of the Tyumen suite): legend seen on Fig. 7.5

7.2 Petrophysical Equation System in the High-Resolution Seismic Method

275

Fig. 7.8 Predicted average porosity map for reservoir formation Т1(Ю2) (the upper part of the Tyumen suite): legend see on the Figs. 7.5 and 7.7

Fig. 7.9 Predicted average clay content map for reservoir formation Т1(Ю2) (the upper part of the Tyumen suite): legend seen on Figs. 7.5 and 7.7

of oil saturation varies mainly in the range of 3–7%. A distinctive feature of oil saturation anomalies is they are confined not only to the anticline (elevated) areas of the horizon Т1(Ю2) but also to those with a slightly reduced hypsometric level. Such anomalies are located in different parts of the study area. The distribution of oil saturation along research area is associated with the porosity allocation distribution (Fig. 7.8). Judging by the presented materials, the

276

7 Determination of Dependencies between Geological and. . .

Fig. 7.10 Predicted oil-saturated thickness map for reservoir formation Т1 (Ю2) (the upper part of the Tyumen suite): legend seen on the Figs. 7.5 and 7.7

zones with elevated predicted porosity confidently fit into the oil saturation contours. Most often, oil saturation is confined to reservoir rocks with an average porosity of 10–18%. In zones where there are no oil saturation contours, the predicted average values of porosity do not exceed 6–10%. The result of combining the placement of the predictive clay content in reservoirs of the Т1(Ю2) horizon with the oil saturation contours in Fig. 7.9 shows that the distribution of clay content over the area is also rather uneven, and areas with relatively high oil saturation correspond to lower predicted values of clayiness. In such areas, the average clay content vary in the range of 3–9%. Only in certain local areas, placed mainly in the south-eastern part of the area, a somewhat elevated content of clay appears in the reservoir rocks of the horizon, reaching 10–11%. In the rest of the territory, the clayiness reaches its maximum value—13%. These more clayey parts of the section are mainly outside the oil saturation contours. The distribution of the predicted average values of clay content over the research area has an inverse relationship with the peculiarities of the porosity distribution (which is quite logical in terms of the volume content model and distribution of terrigenous sandy-clayey material). At the same time, higher values of porosity (Fig. 7.8) correspond to lower values of clay content (Fig. 7.9). On the map of predicted oil- and gas-saturated sediments—reservoirs of the Т1(Ю2) horizon with superimposed contours of significant average oil saturation—the highest values of the considered parameter are observed in areas with increased oil saturation (Fig. 7.9). On this map, the minimum oil-saturated thickness is considered to be a layer thickness of  1 m. As is known [19, 21], this thickness is the closest in size to the thickness of the so-called elementary layer, which can be determined using seismic data restored using HRS-Geo technology software. The

7.2 Petrophysical Equation System in the High-Resolution Seismic Method

277

highest values of oil-saturated thickness are recorded within individual local zones, reaching 2.6–3.2 m (Fig. 7.10). The remaining areas are characterized by lower oil-saturated thickness. Thus, the study of the detailed internal structure of the productive horizons in the sedimentary complex using seismic data on the basis of high-resolution seismic technology—the HRS-Geo—allows estimating the matter composition, reservoir properties, nature and degree of reservoir rock fluid saturation of specific geological objects, strata, formations, local heterogeneities, etc. As can be seen from the above example, in the process of predicting the component of the geological section for productive horizons, there is a generally variable elements of this geological section in the space of the object. In particular, such heterogeneity of the studied section can be due to the complex morphology of the pore space, the unstable content of clay material, the complex mineral composition of the rock matrix, etc. Ultimately, this leads to a change in the wide range of density and volume hydrocarbon content. As an example of using the above described approach for calculating the predicted geological and geophysical components of the real subsurface, we present the result of a resources (reserves) assessment over one of the areas of Western Siberia for the four prospective T1, T2, T3, and T4 layers (Table 7.8). The basis for the calculation of oil and gas resources in the T2 reservoir in the form of a map of the predicted distribution hydrocarbon density is presented in Fig. 7.11. The figure shows the heterogeneity of the distribution of hydrocarbons by area (as opposed to the traditional approach to calculating reserves, where initial averaged constant calculation factors are set, which are estimated from well data under the assumption that the distribution of hydrocarbons is homogeneous within the space occupied by the prospective object). The maps of this type are analyzed in combination with other data and are used to select the optimal location of the reservoir opening points in the design of exploration and production wells. The results of the resource calculations are presented in Table 7.8. This table reflects the resource assessment for the sediments of each of the productive horizons of the Tyumen suite. In particular, in the study area, the volume content of the entire geological substance for the deposits of the Tyumen formation (horizons Т1(Ю2), Т2(Ю3 + Ю4), Т3(Ю5), and Т4(Ю6)) is estimated at 18912.53 • 106 m3. In this total volume clay matter is presented in the amount of 11789.02 • 106 m3 and sandstones 7065.78 • 106 m3. The total open pore volume is 57.731 • 106 m3. The volume of formation water in the pores is 17.270 • 106 m3. The volume of hydrocarbon balance resources for the target sediments in the considered object is Vo ¼ 40.461 • 106 m3. Assuming that about 35% of the oil is in its extractable part, i.e., taking the oil extract factor (OEF) equal to 0.35 and the coefficient of change in the volume of hydrocarbons when they rise to the surface equal to Y ¼ 0.848, the volume of oil to be produced is equal to:

Volume V (106 m3) 4748.99 7207.53 5366.74 1589.27 18,912.53

Vcl (106 m3) 2545.87 4593.69 3520.62 1128.84 11,789.02

Vsand (106 m3) 2188.21 2584.08 1835.29 458.20 7065.78

Vpor (106 m3) 14.912 29.759 10.829 2.231 57.731

Vw (106 m3) 4.037 5.113 6.329 1.791 17.270

Vo (106 m3) 10.875 24.646 4.500 0.440 40.461

Veo(106 m3) 3.228 7.315 1.336 0.131 12.009

mo (106 t) 9.113 20.653 3.771 0.369 33.906

me o (106 t) 2.705 6.130 1.119 0.109 10.063

V is total rock volume; Vcl is clay component volume; Vsand is sandstone component volume; Vpor is pore volume; Vw is formation water component volume; Vo is hydrocarbon volume (total); V eo is extractable hydrocarbon volume (OEF ¼ 0.35, Y ¼ 0.848); mo is hydrocarbon mass (total); meo is extractable hydrocarbon mass (σ 0 ¼ 0.838 g/cm3); OEF is oil extract factor; Y is oil volume change coefficient when lift to surface; σ o is oil density

Formation T1 T2 T3 T4 Total

Table 7.8 Calculation of predicted geological resource component substances in the Jurassic sediments

278 7 Determination of Dependencies between Geological and. . .

7.2 Petrophysical Equation System in the High-Resolution Seismic Method

279

Fig. 7.11 Predicted hydrocarbon distribution density map for the sediments of the reservoir formation Т2(Ю3 + Ю4) (the upper part of the Tyumen suite)

Vo e ¼ Vo  OEF  Y ¼ 40:461  0:35  0:848 ¼ 12:009 km3 Taking the density of oil in the studied object on average equal to σo ¼ 0.838 g/ cm3, the mass of recoverable hydrocarbons in the identified oil-saturated objects is estimated as: mo e ¼ Vo e  σ o ¼ 12:009  0:838 ¼ 10:063 mln:t Thus, high-quality seismic survey, optimal preprocessing of field measurement data, and the correct wave field inversion into the effective acoustic impedance distribution are the basis for a successful prediction of geological and geophysical medium parameters. The authors have developed special systems of petrophysical equations for determining the lithological composition and nature of fluid saturation of reservoir rocks. With an integrated approach to analyzing the results obtained, it is possible to extract the maximum possible amount of reliable geological and geophysical information from the wave field for the purpose of prospecting for promising oil and gas objects, evaluating their characteristics for estimating hydrocarbon reserves and resources, optimal placement of exploration and production wells of oil industry.

7 Determination of Dependencies between Geological and. . .

280

7.3

Summary

The estimation of the various dynamic and kinematic parameters of geological informativeness is based on the establishment of direct and inverse relationships between the seismic and well field geophysical characteristics of the thin-layer section. At the final stage of the complex interpretation of the observation materials, the composition and properties of oil-bearing deposits are predicted in accordance with the technological scheme of dynamic data processing (using the procedures of the HRS-Geo technology). For this purpose, a special system of petrophysical equations for determining the lithological composition and the nature of fluid saturation of reservoir rocks has been developed for a comprehensive analysis of the results obtained. A special system of petrophysical equations is directly used in the complex analysis of the obtained results. On this basis, the extraction of the maximum possible amount of geological and geophysical information from seismic data is achieved for the purpose of searching for promising oil and gas objects, evaluating their characteristics when calculating hydrocarbon reserves and resources, as well as for optimal placement of exploration and production wells. When determining the volume of extracted hydrocarbon resources from the sediments of the studied horizons and formations based on seismic data, both constant (averaging) and variable calculation parameters are used (which are previously determined and used in the examination of the main calculation parameters for categories C1–C3).

References 1. Trofimov, V. L., & Demkiv, B. I. (1981). Postroyeniye prognoznoy skhemy glinistosti po dannym skvazhinnykh nablyudeniy i skorostnogo analiza seysmozapisey OGT (Construction of a predictive clay content scheme based on borehole observations and velocity analysis of CDP seismic records). In Resheniye litologicheskikh zadach i pryamyye poiski zalezhey nefti v Prpyatskoy vpadine metodami geofiziki i geokhimii (Solving lithological problems and direct prospecting for oil deposits in the Pripyat depression using the methods of geophysics and geochemistry). Minsk (pp. 86–100). BelNIGRI. 2. Trofimov, V. L., Lisitsa, A. I., & Krylov, B. A. (1984). Vyyavleniye zavisimostey mezhdu geologo-geofizicheskimi kharakteristikami razreza (Revealing the dependencies between the geological and geophysical characteristics of the section). In Metody razvedochnoy geofiziki v BSSR (Exploration geophysics methods in the BSSR). Minsk (pp. 34–47). BelNIGRI. 3. Trofimov, V. L., Khar'kova, S. V., Lisitsa, A. I., & Krylov, B. A. (1989). Opredeleniye mnogomernykh zavisimostey mezhdu seysmicheskimi i promyslovo-geofizicheskimi kharakteristikami razreza (Determination of multidimensional relationships between seismic and well field-geophysical characteristics). In Collection: Geofizicheskiye issledovaniya glubinnogo stroyeniya territorii Belorussii (Geophysical studies of the Belarus territory deep structure). Minsk (pp. 77–98). BelNIGRI. 4. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., & Mal'tsev, G. A. (2007). Detal'naya otsenka geologicheskikh pokazateley real'noy sredy s primeneniyem tekhnologii vysokorazreshayushchey seysmiki (Detailed assessment of geological indicators of the real medium using high-resolution seismic technology). Geofizika (Geophysics), 4, 157–166.

References

281

5. Ellanskiy, M. M. (1978). Petrofizicheskiye svyazi i kompleksnaya interpretatsiya dannykh promyslovoy geofiziki (Petrophysical dependencies and integrated interpretation of well production geophysics data) (p. 215). M., Nedra. 6. Trofimov, V. L., & Shevchenko, T. A. (1989). Izucheniye vliyaniya geologicheskikh kharakteristik razreza na yego akusticheskiye parametry i dinamiku seysmicheskoy zapisi: Geofizicheskiye issledovaniya glubinnogo stroyeniya territorii Belorussii (Study of the influence of the geological characteristics on its acoustic parameters and the dynamics of seismic recording: Geophysical studies of the deep structure of the territory of Belarus). In Geofizicheskiye issledovaniya glubinnogo stroyeniya territorii Belorussii (Geophysical studies of the Belarus territory deep structure). Minsk (pp. 123–130). BelNIGRI. 7. Dzh, S. (1980). Lineynyy regressionnyy analiz (Linear regression analysis) (p. 456). M., Mir. 8. Programmnoye obespecheniye EVM. (1983). Computer software. Issue 44 (p. 163). Institute of Mathematics of the Academy of Sciences of the BSSR, Belarusian State University. 9. Trofimov, V. L., & Lisitsa, A. I. (1986). Opredeleniye skorostnykh kharakteristik prodol'nykh i poperechnykh voln i svyazannykh s nimi parametrov razreza po dannym PM VSP (Determination of velocity characteristics of longitudinal and transverse waves and related parameters of the section according to PM VSP data). In Collection: Novyye rezul'taty geofizicheskikh issledovaniy v Belorussii (New results of geophysical research in Belarus) (pp. 82–93). Minsk. 10. Trofimov, V. L., & Khaziev, F. F. (2004). Prognozirovaniye dinamiki napryazhennogo sostoyaniya massiva gornykh porod po dannym trekhkomponentnykh skvazhinnykh issledovaniy (Predicting the dynamics of the rock mass stress state based on the data of threecomponent borehole studies). Tekhnologii seysmorazvedki (Seismic Technologies), 1, 50–56. 11. Sil'via, M. T., & Robinson, E. A. (1983). Obratnaya fil'tratsiya geofizicheskikh vremennykh ryadov pri razvedke na neft' i gaz 1 3 (Inverse filtering of geophysical time series in oil and gas exploration) (p. 247). M., Nedra. 12. Klushin, S. V., Trofimov, V. L., & Isayenko, M. N. (1984). i dr. Osnovnyye rezul'taty i perspektivy razvitiya polyarizatsionnogo metoda seysmorazvedki v BSSR (The main results and prospects for the development of the polarization method of seismic prospecting in the BSSR). In Geofizicheskiye issledovaniya na neft' v Belorusskiy SSR (Geophysical exploration for oil in the Byelorussian SSR) (pp. 31–60). Nauka i tekhnika (Science and Technology). 13. Sаttlеgger, J. W. (1964). Series for three-dimensional migration in reflection seismic interpretation. Geopyshysical Prosperity, 12(1), 115–134. 14. Zalyayev, N. Z. (1984). Vliyaniye litologii i plastovykh uglevodorodov na geofizicheskiye parametry (Influence of lithology and reservoir hydrocarbons on geophysical parameters). In “Metodika i rezul'taty geologo-geofizicheskikh issledovaniy v Pripyatskom progibe” (Methodology and results of geological and geophysical research in the Pripyat trough) (Nauka i tekhnika (Science and Technology)) (pp. 61–74). Minsk. 15. Zalyayev, N. Z. (1990). Metodika avtomatizirovannoy interpretatsii geofizicheskikh issledovaniy skvazhin (Technique of automated interpretation of well logging). Minsk. University Edition, p. 142. 16. Trofimov, V. L., Khaziev, F. F., Milashin, V. A., et al. (2007). Avtomatizirovannaya obrabotka i interpretatsiya dannykh GIS dlya obnaruzheniya nefteperspektivnykh obyektov metodami vysokorazreshayushchey seysmiki (Automated processing and interpretation of well logging data for the detection of oil-prospective objects by high-resolution seismic methods). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 54–66. 17. Trofimov, V. L., Milashin, V. A., & Khaziev, F. F. (2009). Spetsial'naya obrabotka i interpretatsiya dannykh seysmicheskikh nablyudeniy v slozhnykh geologicheskikh usloviyakh metodom vyokorazreshayushchey seysmiki (Special processing and interpretation of seismic observation data in difficult geological conditions by the method of high-resolution seismics). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 36–50.

282

7 Determination of Dependencies between Geological and. . .

18. Trofimov, V. L., Khaziev, F. F., & Shkol'nik, S. A. (2014). Sovershenstvovaniye metodiki prognozirovaniya geologicheskikh pokazateley metodom vysokorazreshayushchey seysmiki (Improvement of the methodology for predicting geological indicators by the method of highresolution seismic). Ekspozitsiya Neft' Gaz (Oil and gas exposition), 6(38), 13–19. 19. Trofimov, V. L., Milashin, V. A., Khaziev, F. F. et al. (2004). Resheniye zadach neftyanoy geologii v razlichnykh rayonakh Zapadnoy Sibiri metodami vysokorazreshayushchey seysmiki (Solving the problems of oil geology in various regions of Western Siberia by high-resolution seismic methods). VII Scientific and Practical Conference “Ways of realizing the oil and gas potential of the Khanty-Mansi Autonomous Okrug”. pp. 26–45. 20. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2008). Opredeleniye geologogeofizicheskikh parametrov real'noy sredy metodom vysokorazreshayushchey seysmiki (Determination of geological and geophysical parameters of the real medium by the high-resolution seismic method). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 25–30. 21. Khaziev, F. F., & Trofimov, V. L. (2003). Model'nyye issledovaniya rezul'tatov resheniya obratnoy dinamicheskoy zadachi seysmiki (Model studies of the solving inverse dynamic seismic problem results). Spetsial'nyy vypusk Geofizika: Tekhnologii seysmorazvedki-II (Geophysics, special edition of “Seismic Technologies”). pp. 27–37.

Chapter 8

Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural Province

Abstract The results of a complex interpretation of the seismic observation materials obtained using the developed HRS-Geo technology in various seismic and geological conditions for different regions of Russia and abroad are presented. In the conditions of the Volga-Ural oil and gas province, the prospects of oil and gas potential are largely associated with the deposits of the terrigenous Devonian, which is confirmed by the presence of a significant number of oil and gas fields in them. To assess the reliability of oil-promising objects identified within one of the Volga-Ural province areas, an independent analysis (with the participation of the customer’s specialists) of the convergence of the drilling data and the results of the work presented by the authors was carried out. The obtained coefficient of convergence of the results was at least 0.8. In an area of the Orenburg region in the eastern part of the Kama-Kinel uncompensated trough system, a detailed study of Carboniferous, Upper and Middle Devonian deposits was implemented. In the course of the performed research and further testing of the identified oil-promising objects, data confirming the results of the prediction made using the HRS-Geo technology were obtained. On the territory of the Samara region, the assessment of geological indicators in productive and promising deposits of the Middle and Lower Carboniferous, Upper and Middle Devonian was performed. The authors limited themselves to a comparative analysis of the obtained results of forecasting various geological indicators for different horizons by combining the contours of oil saturation and a comprehensive analysis of the considered predictive geological and geophysical parameters. At the same time, all the obtained prediction information for the target intervals was used.

The HRS-Geo technology developed and described in detail in Chaps. 1–7 (with all the components interrelated with it, methods for transforming seismic data, and GBS and its application possibilities) was used by the authors for the complex interpretation of seismic data obtained in various regions of Russia and abroad. The results reached in this case are discussed in this section. The process of extracting from the seismic data the necessary information about the geological structure and the desired geological indicators is one of the main elements of the overall process of integrated geological interpretation of geophysical data. This process includes the analysis of a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_8

283

284

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

variety of geological and geophysical data and the work of a number of automated procedures performed in a certain sequence [1]. The main task of complex interpretation is the analysis of all information about the subsurface environment, in other words, the construction of medium interpretational model set, reflecting all those features of the geological structure of the real environment and geological history, which together determine the oil and gas prospects. To present the interpretation results in a relative brief form below in each of the subsections characterizing the results in a particular geological setting, the authors followed a certain sequence (some scheme) of their presentation: 1. Geological environment in which the desired oil prospective objects are located (structural tectonic and geodynamic features of the structure, distinctive geological relations of objects distribution, sedimentation conditions of the deposits under study, the formation of pore space, and oil and gas potential of thin-layer reservoirs). 2. Used geophysical material—2D or 3D seismic and well logging data on the wells available for the period of the survey. 3. Goals and objectives of the research. 4. The main results obtained on the basis of using the HRS-Geo technology, which were the material for a detailed integrated interpretation, in particular: – 2D lines and 3D cubes of effective reflectivity (RC) and acoustic impedance (AI) for studying the detailed internal structure of a real geological medium with a vertical resolution equal to the discretization step of the seismic record over time; – The results of automated processing of well log data in the form of lithologic stratigraphic well columns, which are necessary for interpreting highresolution seismic data, prospecting, and industrial exploration of oil prospective objects; – Lithofacies replacements of deposits, zones of heterogeneity in productive and potentially productive horizons (thin layers), conditions for the formation of sediments (sedimentation) by clustering and predicting various groups of facies in target deposits; – Predicted the most probable distribution of geological indicators in the studied areas—effective acoustic impedance, clay content, sandiness (carbonate content), porosity, oil saturation, and hydrocarbon distribution density in productive and prospective formations, allowing to establish relationships of reservoir rock location, screen cap rocks and oil and gas saturation explored areas, and other features of the geological structure. 5. Conclusions and prospects for the formulation of further exploration work. The authors have accumulated more than 25 years of experience in the specialized processing and complex interpretation of seismic and GBS data in various regions of Russia (Volga-Ural, Western and Eastern Siberia and the Timan-Pechora provinces, the South Dagestan shelf of the Caspian Sea, the Boundary Cenozoic Basin of Sakhalin) and abroad (Western Shetland and Sierra Leone—in the conditions of

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

285

the deep-water shelf of the Atlantic, the Pripyat Trough, the states of Saudi Arabia, Bulgaria, Cuba, and Myanmar (Burma)). Practically all the studied oil prospective objects using the HRS-Geo technology software obtained positive results, confirmed further by the results of drilling deep wells. A small part of the results obtained with this is presented in this chapter. These results show, in general, a qualitatively new, high level of seismic surveys in the prospecting and exploration of oil and gas deposits. The Volga-Ural oil and gas province (OGP), as is known [2–4], was the main source of raw materials in Russia in the 1940–1960s of the last century. Large deposits here are generally drilled and developed. Relatively dense drilling systems showed significant heterogeneity of reservoirs and fluid seals in the entire interval of the studied section—from the weathering crust of the basement to the Carboniferous deposits. The oil and gas potential in this region is currently largely associated with deposits of terrigenous Devonian, which is confirmed by the presence of a significant amount of oil and gas deposits in them. As it is known, the deposits of the oil-andgas-bearing terrigenous Devonian stratum turn out to be the most difficult for studying by seismic research methods. 1. Let us briefly discuss the structural features of the crystalline foundation. It is known that the basement surface is a system of alternating elevated and relatively submerged portions of the northwestern strike composed of super-crystal Archaean and Proterozoic complexes, consisting of magmatic formations of basic and acidic composition and their derivatives [5]. Relatively elevated areas form ridges, in within more ancient rocks, are exposed, represented by gabbronorites, enderbits, chernokites, and high-alumina pyroxene-biotite gneisses, whose absolute age is 2 billion years. Interridge areas are composed of younger Archean high-alumina and alumina garnet gneisses, biotite, biotite-amphibolite, biotite-cordierite plagiogneisses, and amphibolites in association with plagiogranite, diorites, and other metamorphosed granitoids. Their absolute age is 1.5–1.9 billion years [6]. The selected rock complexes are parallelized, respectively, with the Otradnen and Bolshechremshan Archean series. The southern part of the Zhigulev-Orenburg massif is dominated by microcline granites, the absolute age of which is estimated at 1.3–1.8 billion years and corresponds to the Lower and Middle Proterozoic. These spatial boundaries are closely associated with Lower Proterozoic metasandstone rocks with which they in the internal structure of the basement perform a negative form, identified as the Buzuluk depression. In the north of the region, the rocks of the crystalline basement comprise the southeastern end of the Tatar massif and the Pashkin ledge, separated by the Tuimazin-Bavlin graben. The graben is made by Riphean-Vendian deposits. To the east, the surface of the basement is experiencing an intense dive, compensated for by Riphean-Vendian and further to the east by Ordovician and Silurian sediments. The sustained long-term immersion of the platform edge, which captured its entire eastern edge, has formed a large negative structure—the Kama-Ufa pericarton trough. Most researchers recognize the block nature of the crystalline basement due to disjunctive faults. However, the fault continuation into the sedimentary layer in

286

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

some cases was denied [5]. A number of researchers believe that this phenomenon is extremely rare. Recently, disjunctive dislocations of the sedimentary cover have been established in many regions of the Tatarstan territory. 2. The presence of a developed system of faults complicating the sedimentary cover, and especially the surface of the crystalline basement, is confirmed by abrupt changes in the sediments thickness of the terrigenous-carbonate Devonian within the study site. In the north of the region and on the territory of Tatarstan, as a result of oil-prospecting drilling, narrow linear grabens of large extent (up to 50–100 km) were traced, within which a sharp increase in the thickness of the Givetian stage deposits and especially of the Kynov (or Kynovian) horizon is observed. Refereeing by the sharp increase in the thickness of the terrigenous Devonian sediments, shifts with amplitude up to 30–100 m occurred, which controlled the grabens for disjunctive dislocation, often accompanied by volcanic activities [5]. In the Devonian, the graben-like troughs were part of a huge general stretching zone of the Earth’s crust. Distortions controlling grabens should be attributed to faults, which is confirmed by the large layer of packs lost in the terrigenous Devonian in intervals of a number of wells located in close proximity to near-fault zones. The period of trough establishment, the formation of which is associated with ancient fault zones of the basement, was relatively short and refers to the Pashiisk time (at in this time, there was a radical structure reconstruction of the Devonian sediments) [7]. As a result, in the modern plan, the troughs located above the top of the Pashiisk horizon have smooth and vague forms. Another group of disturbances directly recorded in well sections is usually characterized by the layer repetition, the formation of sharp horsts in the absence of conjugate grabens, and the presence of overthrust covers. These dislocations can be traced both in Vendian and Devonian and in the Carboniferous and Permian sediments. These faults are not associated with facies changes or fluctuations in the thickness of the sediments in which they are traced. This indicates a later epoch of their formation. Thus, one and the same territory, covering essentially the entire Volga-Ural region, experienced a change in tension (the Devonian period) over time by compression (Middle Carboniferous, Mesozoic) with the formation of various genetic types of distortions—in the first phase of stretching, faults and grabens were laid; in the second, late, phase of compression, there was an intensive formation of upthrows, upthrow thrusts, horsts, and inversion structures, which arise mainly over the ancient Riphean and Devonian grabens. Identifying disjunctive faults of the crystalline basement and sedimentary cover is of great practical importance in assessing the prospects for oil bearing of a certain area, as they indicate tectonically weakened zones along which migration, as well as destruction, redistribution, and formation of oilfields could occur. 3. According to regional studies in the Volga-Ural region, a clearly expressed disconformity in the next deposition cycle on rocks of different ages with continental interruptions in sedimentation can be traced at the stage boundaries [8]. Less expressed disconformity associated with interruptions in sedimentation are

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

287

highlighted at horizon boundaries, which are characterized by a change in transgressive and regressive phases. The regional seal for the deposits of the Devonian terrigenous complex is formed by terrigenous and terrigenous-carbonate rocks mostly of the Frasnian stage. The main sealing formation usually form clay, carbonate, and clay-carbonate rocks in Kynov, Sargay, Semiluk (Domanik), and the Burreg (Mendym) horizons. In some areas the lower part of a regional shield and permeable terrigenous Devonian complex is overlapped by the carbonate deposits of the Burregian horizon [9]. In the central part of the Volga-Ural province, the lower part of the Kynovian horizon is made sandy, with the result that in some areas the top of the permeable terrigenous stratum rises to the middle part of the Kynov or Sargay horizons and on others the regional seal is complicated by the Kynov reservoir. In the body of a regional screen (fluid seal) formation, bituminous clay-carbonate rocks of the Domanik are widespread. 4. The formation of non-anticlinal traps in the Devonian terrigenous complex is generally associated here with the productive strata of the DIV Vorobiov and DIII Ardatov layers and the DI and the D0 Pashiisk horizon. Oil and gas in the sandy reservoir DIV in Vorobiov and DIII in the Ardatov layers in the work areas have not yet been proven. These strata attract the attention of geologists as possibly productive. In the top of the terrigenous Devonian, its main productive layers DI and D0 of the Pashiisk horizon lie. In most parts of the territory, they are separated from the lower Starooskol horizon by a clay or clay-carbonate pack of the Mullin horizon. 5. Starooskol horizon in the Volga-Ural province is quite widespread. In different parts of the province, it is represented by various types of sediments. Analogs of the Starooskol horizon and the underlying Vorobyev horizon are differentiated by Mikryukov M.F. and Timergazin K.R. under the notation “Ardatov layers” [8]. With the most complex structure, the Starooskol horizon is subdivided into three parts: the lower (sandy-argillite), the middle (carbonate), and the upper (aleurite-argillite). In some areas, the lower part of the Starooskol horizon consists of a basal sandy layer, covering its aleuritic claystone and sandstone layer DIII, gradually alternating with clayey siltstone and then argillite. The middle part, known as “middle limestone,” has a different thickness and is represented by gray clayey limestone, often organic detritus, sometimes secondary dolomite. The upper argillite part of the Starooskol horizon, as well as the lower, sandy-argillite part, is characterized by a peculiar structure. At its base lies a layer of argillite calcareous, dark gray, gradually replacing limestone. Above a thin interlayer of argillite stands out, enriched with aleuritic material. 6. The Mullin horizon in the central part of the province contains a complex of sediments, at the base of which lies a sandy or aleuritic stratum DII and a pack of clay-aleuritic rocks, usually the upper or middle part of which is confined to the so-called black limestone (dark gray limestone and brownish gray, fine-grained, and organogenous-clastic, with interlayers of black clay and secretions of pyrite). Within the limits of uplifts, significant facies changes of the horizon are noted. The limestone reservoir also changes its petrographic composition; it is replaced by

288

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

calcareous argillite within the slopes of the arch and within the arch itself with aleuritic argillite [8]. 7. After a short break in sedimentation (the end of the Mullin time of the Givetian age), sands and siltstones began to be deposited in the southeast of the Russian platform, less often clayey aleurite sediments with the remnants of the Pashiisk time of the Frasnian age. The sea in the Pashiisk time was with a sharply low-salinity water. According to Nalivkin D.V., these sediments originated in the alluvial plain [10]. A typical section of the Pashiisk horizon, which is the oldest in the Upper Devonian in the studied region, is composed of a fine-grained stratum at the base, sometimes of silty sandstone of small thickness (6–7 m). Up the section, it is replaced by a clayey siltstone of greenish-gray color with siderite spherulites. Above it stands out a pack of silty argillites interbedded with sandy siltstones. The upper part of the horizon is represented by clayey siltstones, interbedded with aleurolites and mudstones, upward in the section, alternating by interbedding clayey limestone, calcareous clays, and clayey aleurolites. The productive part of the Pashiisk horizon in the northern part of the region is divided by Petrovskaya A.N. and Varfolomeeva T.P. into eight sandy and clayey-aleuritic layers separating them [8]. A feature of the Pashiisk deposits in the study area is the confinement of the most thick and coarse sandy sediments to its base. In a number of areas in the uppermost part of the horizon, at the base of the “upper limestone” carbonate sediments, a sandy layer of considerable thickness appears (basal horizon). In general, repeated traces of erosion and leaching of previously deposited sediments are characteristic of the Pashiisk horizon, indicating a significant water mobility in the shallow desalinated sea basin (coastal-marine regime). 8. The deposits of the Kynov horizon in the study region are divided into three parts. The so-called lower Kynov limestone (LKL), which covers its mudstones and underlying sandstone D0, is assigned to the lower part of the horizon. In the north of the Volga-Ural region, the “lower Kynov limestone” is completely or almost completely replaced by aleurite-clay rocks [8]. The middle part of the Kynov horizon, which lies transgressively on the underlying sediments of the Upper Devonian, is characterized by significant facies changes in the petrographic composition of the rocks. The characteristic marine fauna complex of the middle part of the horizon is confined to the so-called middle Kynov limestone (MKL), which is underlain (in most parts of the territory) by sandy or clay-silt sediments of the Mikhailov stratum Dk. The upper part of the Kynov horizon on a large territory of the Volga-Ural region consists of a base of limestone layers—“upper Kynov limestone” (UKL, “ajax” benchmark) and the clay-limestone deposits, often heavily enriched in organic matter, sometimes with layers of bituminous shale. In the limestone, there are interlayers of black mudstone with the fauna of the Domanik type. 9. Studies, carried out on the adjacent territory established features of the formation and modern structure of the Tournaisian- Kynovian stratum of the southeast of the Russian platform, which consist in a special ratio of different-age structures and

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

289

controlling the productivity of Devonian, Carboniferous, and other sediments. In particular, the following relations were revealed for the Bolshekinel swell (as the most studied) [11]: 1. Between the local domes, complicating the structural plans of the Pashiisk horizon in the Upper Devonian and the Tournaisian stage in the Lower Carboniferous, there are inverse relationships. 2. The maximum thicknesses of the Pashiisk-Tournaisian sediment (stratum) correspond to the highest hypsometric positions of the Tournaisian stage top; to the minimum thicknesses—the highest position of the Pashiisk horizon top. 3. As a rule, the maximum productivity of the Tournaisian stage and other overlying horizons, the occurrence of which is usually in conformity with the Tournaisian ones, is usually confined to areas of increased thicknesses of this rock complexes. 4. The maximum productivity of the Pashiisk and other horizons of the terrigenous Devonian is associated with the areas of reduced thicknesses of the TournaisianKynovian sediment complex. At the same time, one rather important situation, it was found out that in all cases of high productivity of the Pashiisk layers, as a rule, the layers of the Tournaisian layer are unproductive or weakly productive. At the same time, with very high reservoir productivity of the Tournaisian stage, Devonian deposits are completely unproductive or their productivity is low. Relationships of this type, characteristic of the south-eastern part of the Russian platform, are confirmed in one form or another in the works of some researchers [12]. 10. The Pashiisk time was characterized by an unstable (marine) sedimentation regime, due to the uneven and increasing spread of transgression associated with the activation of tectonic movements at the beginning of the Late Devonian [13]. An analysis of the composition and structure of the Pashiisk sediments and their relationship with the underlying strata in different parts of the considered strata of the territory allows us to state that the pre-Pashiisk land was low and probably hollow inclined toward the south. Therefore, the slightest oscillatory movements led to the flooding or shallowing of vast spaces. The consequence of this was the formation of relatively thin terrigenous sediments, characterized by changes in the lithological composition in area and profile. The main direction of the Pashiisk basin transgression from the south to the north of the Ural-Volga region determined the existence of areas: (1) the most stable shallow water shelf in the south, (2) unsustainable marine sedimentation in the central regions, and (3) in areas of the coastal plain, which at times turned into an internal shallow water shelf. 11. When analyzing the location of various oil deposit types in the Kynov deposits of the Ural-Volga region, we can note the dependence of the distribution of deposits on the facial-paleogeographic conditions of deposits accumulation. The facial-paleogeographic zonality of the Kynov time differs markedly from that of the Pashiisk [13]. Kynov deposits are formed under the conditions of the developing transgression of the Lower Frasnian basin. The lithological composition of the Kynov horizon deposits is more diverse than that of the Pashiisk formations, and, at the same time, it is well maintained along

290

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

strike. It is represented mainly by clay rocks with a subordinate development of siltstone, sandstone, and limestone. The latter are rich in various marine fauna. Oil deposits are widespread in the Kynov horizon, but are low capacity. The lithological variability of aleurolite-sandy rocks and the wide distribution of sandy lenses in the context of Kynov deposits are favorable factors for the formation of structural lithological and lithological types of oil deposits in them. As noted above, the aleurolite-clay composition of the terrigenous formations of the Kynov horizon led to its shielding properties as a regional sealing formation for the Pashiisk horizon deposits. Thus, the location of the area to the region of wide fault development in the presence of reliable shielding formations and reservoirs in the section is a favorable factor for the accumulation of migrated hydrocarbons. The category of promising objects in the study region can be attributed to traps of consedimentary origin, which are the coating structures of reef bodies (in particular, they can be formed by replacing carbonate reservoirs with more dense differences). Tectonically screened traps in terrigenous Devonian sediments, controlled by graben-shaped troughs or extended tectonic disturbances such as a normal fault, stratigraphically shielded traps in the terrigenous Devonian stratum, and lithological (accumulative type) and structural-lithological (in the presence of anticline) and lithological (in the presence of anticline) traps, are of great interest in the Kynov and Pashiisk horizons [14]. From the point of view of the prospecting and exploration methods of productive objects in the conditions of the Volga-Ural oil and gas province as a whole, the last of the above listed are the most difficult for prospecting, i.e., complex-sealing traps in terrigenous and terrigenous-carbonate sediments of the Devonian, whose structural plan has a buried character. It should be noted here that the relevance of studying these traps does not decrease with time (rather, it increases), since the oil deposits in such sediments are characterized by high productivity, as well as favorable chemical composition.

8.1

Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan).

The subsection discusses the features of the formation and geological structure of the most important oil perspective strata of a sedimentary cover in the study area— within the southeastern part of the Republic of Tatarstan, located on the western slope of the South Tatar arch. Materials showing the capabilities of the seismic method using the HRS-Geo technology for detecting hydrocarbon accumulations under conditions of the complex geological structure of terrigenous Devonian deposits are presented. The complex geological structure of the oil perspective objects in these sediments, as well as the lack of knowledge about the conditions for the formation of oil and gas accumulations and the factors controlling them,

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan).

291

predetermined a very low searchable information content of studying such objects— the efficiency of geological exploration in their search and exploration here was about 15%. The relatively low informativity of studying Devonian terrigenous formations is largely due to the use of only “wave seismic exploration” in the conditions of the Volga-Ural. Moreover, the geological section of the terrigenous Devonian stratum often turns out to be so complicated that even according to the results of drilling, it is not always possible to establish the genetic nature of seismic anomalies [7, 15]. The study region, has more than 50 relatively small oilfields, located to the west of the largest oilfields of the Volga-Ural province: Romashkin and Novoyelkhov [16, 17]. For these deposits, the main resources are with the cover structures of the Frasnian-Famennian reef facies associated. Specifically, the HRS-Geo technology was used on seismic surveys and GBS obtained within blocks IV, V, and VI of the studied area. At the same time, data on ten seismic lines and four deep wells located near the studied profiles were processed. Initial seismic materials were obtained using nonexplosive sources of elastic oscillation excitation of the “Vibroseis” type, and the required set of standard logging data was used for the wells being processed: radioactive (GR, NL), electric (resistivity, SP), and partially sonic (SN).

8.1.1

Automated Processing and Interpretation of GBS Materials

The features of the geological structure of the terrigenous Devonian section are considered on the basis of the processing results of well logging data of borehole 550, which is located almost in the center of the research area. These results are in the form of a lithological-stratigraphic column with corresponding characteristics of reservoir rocks presented (Fig. 8.1). The well 550 opened a section composed mainly of alternating sediments of terrigenous, terrigenous-carbonate, and carbonate facies that are heterogeneous in structure. Terrigenous Devonian deposits begin with the alternation of sandy-clay and carbonate formations of Semiluk (D3sm) (1720–1733 m), Sargay (D3sr) (1733–1738 m), and partially (in the upper half) Kynov (D3kn) (1738–1767.5 m) horizons. In this part of the section, the most permeable formations are in sandstones, clayey sandstones, and mudstones distinguished. Among them are clearly marked productive oil-bearing strata of the Kynov horizon (in the depth interval of 1742–1750 and 1760–1764.5 m with oil saturation factor of Ko ¼ 25–55%). They are characterized by the following filtration-capacitance parameters: Kp.eff ¼ 12–16%, ka ¼ 3–10 mD, kw ¼ 0.2–1.1 mD, and ko ¼ 0.15–0.25 mD. In sediments of the Pashiisk (D3psh) horizon in the lower part of Frasnian stage, also confidently identified, an oil-saturated reservoir sandstone stands out (in the depth interval of 1767.5–1783.5 m). It is characterized by the following parameters

292

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.1 Lithostratigraphic column with saturation distribution in rock collectors along the Ymashinsk well 550 (Onbysk area)

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan).

293

of productivity: Ko ¼ 15–40%, Kp.eff. ¼ 16–18%, ka ¼ 8–19 mD, kw ¼ 1–2.8 mD, and ko ¼ 0.15–0.7 mD. Further, in the sediments of the Mullin (D2ml) and Starooskol (D2st) horizons of the Givetian (D2gv) stage and eluvium in the depth interval of 1783.5–1838 m, alternation of clays, argillites, sandstones, and clayey sandstones with rare layers of black bituminous limestone and marl appears. The most clayey areas here are the intervals in the sediments of the Mullin and the upper parts of the Starooskol horizon with Ccl ¼ 18–35%. Relatively thin water-saturated interlayers of sandstones and aleurolites in the intervals of depths of 1786–1795 (with insignificant signs of oil saturation), 1801.5–1803, 1819–1827, and 1829–1837 m are the least clayey. The filtration-capacitive parameters of these interlayers are characterized by Kp.eff. ¼ 9–16%, ka ¼ 1–9 mD, kw ¼ 0.1–1.1 mD, and ko  0.05 mD.

8.1.2

Geological Structure Prediction Along Reference Profiles

In accordance with the methodological basis and technological processing scheme described in Chap. 4, the volume of the results obtained is significant, which is quite natural with sufficiently correct extraction of geological information about the material composition of the studied sediments from the dynamics of seismic record based on the use of high-resolution seismic technology. As an example, a brief description of the results of data processing and automated interpretation obtained from one of the seismic lines is given below. The original time section of the CDP, built on the basis of preliminary processing according to a special graph (Sect. 4.3), on the basis of which the corresponding technological transformations were carried out, is shown in Fig. 8.2. A visual

Fig. 8.2 The CDP time section along the seismic line 992126, Onbysk area, Volga-Ural oil and gas province

294

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.3 The effective acoustic impedance along the seismic line 992126, Onbysk area, Volga-Ural oil and gas province

analysis of the seismic wave field structure provides some general ideas about the features of the geological structure of the real environment in the vertical plane under study. In the general structure of the seismic wave field, the combination of the sedimentary and postsedimentary features of the accumulation and formation of deposits, associated with tectonic processes, reefogenic formations of carbonate sequences, etc., are relatively clearly fixed. As a result of solving the inverse dynamic problem for the productive part of the section—Devonian and the lower half of Carboniferous deposits—an effective acoustic impedance section was constructed (Fig. 8.3). This section (free from the influence of the elastic oscillation source, i.e., the seismic wave process) shows the various layer ratios of a thin-layer section with high and low AI values. With an accuracy of the discretization step of seismic recording over time (Δt ¼ 2 ms), the layering geometry is reliably fixed over almost all thin strata and layers of the studied section. In accordance with the developed technological scheme for processing and interpreting seismic data using a high-resolution seismic technique, before predicting the geological parameters of the section (characterizing the material composition, reservoir properties, and fluid character), the section was adjusted to the effective reflection coefficients and acoustic impedance for the studied sediments (Fig. 8.3) that directly obtained from log data in well 550 (Fig. 8.1). After that, the prediction of the desired geological indicators was carried out over the entire productive sediments. The prediction results are in the form of traditional clay content, effective porosity, oil saturation (Figs. 8.4–8.6), and prognosis lithologicstratigraphic columns with fluid-type distributions for the given vertical sections in the productive part (Fig. 8.7) presented. The results of the prediction allow us to note the following. By the nature of the predictive clay content distribution (Fig. 8.4), it can be seen that most of the clayer parts of the section are the deposits of Vereiskian vr, Tulean-

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan).

295

Fig. 8.4 The predicted clayiness along the seismic line 992126, Onbysk area, Volga-Ural oil and gas province

Fig. 8.5 The predicted effective porosity along the seismic line 992126, Onbysk area, Volga-Ural oil and gas province

Bobrikov tl + bb horizons, and terrigenous Devonian stratum (from the Semiluk sm horizon to the crystalline basement surface fund). The smallest clay content is noted in carbonate rock deposits—in Bashkirian b deposits, Serpukhovian srp horizons, Oksk ok supra-horizon, and Aleksin horizon al (which is highlighted only in this area), as well as in sediments of the Upper Tournaisian t2 substage, and Lower Tournaisian and Upper Famennian t1 + fm2, the Lower Famennian fm1, and the Upper Frasnian fr3 substages, including deposits of the lower part of the Upper Frasnian substage—the Domanovich horizon dm. According to the features of the predicted effective porosity distribution as a whole over the entire productive sediments interval (Fig. 8.5), the most porous

296

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.6 The predicted oil saturation along the seismic line 992126, Onbysk area, Volga-Ural oil and gas province

intervals are associated with sediments of the Vereiskian horizon, the Bashkirian, and Upper Tournaisian layers, as well as sediments of terrigenous Devonian, first of all of the Kynov kn, Pashiisk psh, and Mullin + Starooskol ml + st horizons. The main, most significant oil-saturated intervals of the studied strata are primarily associated with these sections. In the deposits of the Kynov horizon, the oil-saturated intervals are distinguished with a time of 0.71–0.75 s (Fig. 8.6). Oil-saturated strata of sandstones and siltstones with rather high productivity parameters stand out fairly confidently—these clay intervals are characterized by lower values of clay content (Fig. 8.4), increased effective porosity (Fig. 8.5), and oil saturation (Fig. 8.6). On the predicted lithologic stratigraphic columns for the indicated intervals, multilayer oil-bearing objects are clearly visible (Fig. 8.7). The most representative of such objects, in the middle part of the line (near pk 29–39), coincides with the anticlinal uplift on the surface of the terrigenous Devonian, which is controlled by a raised block in the crystalline basement. This oil deposit in the sediments of the Kynov horizon is confirmed by the well 550. It is worthwhile to point out the presence of dynamically expressed information associated with the manifestation of the expected oil-water contact (OWC), as shown in the oil saturation section (Fig. 8.6). In the deposits of the Pashiisk horizon, sufficiently confident oil-saturated objects (reservoir layers), which are characterized by increased prediction productivity, are allocated with a time of 0.740–0.765 s (Figs. 8.6 and 8.7). Oil-saturated objects of the Kynov and Pashiisk horizons are in most cases hydrodynamically interrelated (Fig. 8.7).

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan).

297

Fig. 8.7 The predicted lithologic-stratigraphic columns with fluid saturation distribution along the fragment of seismic line 992126, Onbysk area, Volga-Ural oil and gas province

8.1.3

Predicted Geological Indicators Based on HRS, GBS, and Area Drilling Data

On the basis of the predicted parameters identified along sections of each of the ten seismic lines within the deposits of the Kynov and Pashiisk horizons, the maps of the geological indicators for the reservoir deposits are constructed (Figs. 8.8–8.13). On the oil saturation map (Fig. 8.8), the contour of the mobile portion of hydrocarbons in reservoirs of the Pashiisk horizon is shown along an isoline of 10%, above which the most significant oil saturation areas are highlighted. Below this level (up to 7%), a contour is shown for the lower saturation level, which, on the one hand, fixes the position of a zone with an unmoveable part of, on the other hand, zones that cover (by the nature of the hydrocarbon distribution corresponding forecast effective porosity over the studied area) significant oil-saturated areas— the desired oil-saturated objects. These areas that cover a contour with moving hydrocarbons can be combined by various attributes or properties. The basis for combining them into a single contour (even such objects that are hydrodynamically “separated”) is the presence of zones on the study area, through which layers are distributed, characterized as an unproductive reservoir (oil is stationary in this development technology) or a non-collector (oil is physically stationary), but which have approximately the same reduced filtration and capacitance properties. Accordingly, in the sediments of the Kynov and Pashiisk horizons, a whole set of oil-saturated objects (sites of oil saturation with movable hydrocarbons) was identified. In the deposits of the Kynov horizon, 17 (with numbers 1 k–17 k) such sites are identified, within their limits the oil content reaches 10–18% of the studied real medium volume; 19 (with numbers 1p–19p) similar areas were identified in the Pashiisk deposits, within which the oil content reaches 10–20% of the medium volume (Fig. 8.8). Having these data, it is easy to classify the identified

298

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.8 The map of predicted oil saturation values in reservoir layers of the Pashiisk formation

Fig. 8.9 The map of predicted porosity in reservoir layers of the Pashiisk formation

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan). Fig. 8.10 The map of predicted clayiness in reservoir layers of the Pashiisk formation

Fig. 8.11 The map of predicted oil-saturated thickness in reservoir layers of the Pashiisk formation

299

300

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.12 The map of lower-level oil saturation contours in the reservoir layers of Kynov and Pashiisk formations

Fig. 8.13 The map of valuable-level oil saturation contours in the reservoir layers of Kynov and Pashiisk formations

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan).

301

oil-saturated objects by the oil saturation intensity, for example, according to its change ranges: 16–20%, objects 5p, 15p, and 16p; 12–16%, objects 1p, 3p, 8p, 10p, 13p, 18p, and 19p, and 10–12%, objects 2p, 4p, 6p, 7p, 9p, 11p, 12p, 14p, and 17p. Combined maps of predicted average values of oil saturation in reservoirs of the Pashiisk horizon with an effective porosity map show the presence of a direct relation between them (Fig. 8.9). Oil saturation is confined to reservoirs with an average effective porosity of at least 14–16%. However, there are a number of anomalous zones (regions) where fairly high values of effective porosity are noted, but they are not oil saturated. The result of combining the distribution of the predictive values of clay content with the oil saturation contours (Fig. 8.10) shows that the distribution of clays in the area under study in the Pashiisk sediments is uneven (as well as in the sediments of the Kynov horizon). At the same time, there is a clear dependence: almost all of the identified oil-saturated objects correspond to lower predictive values of clay content (in these places, the average values of clay content generally vary by 20–35% in stratum, while in the rest of the territory 35–55%). The distribution nature of the predicted effective porosity is inversely related to the predictive clay content distribution: the increased porosity (Fig. 8.9) are characterized by lower clay content (Fig. 8.10). The same relationship is also established for the deposits of the Kynov horizon. A map of the predicted oil-saturated thicknesses of the Pashiisk horizon deposits, combined with the contours of the oil saturation intensity for the identified oil-saturated objects, is presented in Fig. 8.11. On this map, the minimum oil-saturated thickness of the studied deposits corresponds to a formation thickness of 2.5 m. It is possible to determine this thickness from seismic data, which is restored using the HRS-Geo technology (when seismic records are converted into acoustic impedance curves). Using the oil-saturated thickness (ost) of the Pashiisk horizon reservoirs, it is possible to classify the identified oil-saturated objects by their values into the following groups: 1. 2. 3. 4.

Objects 5p (in separate areas), 13p, and 18p with ost 15–20 m; Objects 5p (in separate areas) 10p, 11p, 16p, and 17p with ost 10–15 m; Objects 1p, 2p, 3p, 4p, 6p, 12p, 14p, 15p, and 19p with ost 5–10 m; Objects 7p, 8p, and 9p with ost 2.5–5 m.

Obviously, these data should also be taken into account when setting the priority and subsequent exploration and production wells of deep drilling.

8.1.4

Oil-Saturated Object Distribution in the Research Area

It is important for practical and scientific purposes to assess the relative position of the contours characterizing the distribution of both stationary and mobile parts of hydrocarbons for the deposits of the Kynov and Pashiisk horizons. Two maps were constructed, one of which compares the contours of the lower level of oil saturation

302

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

(Fig. 8.12), where the position of the zones both moveable and stationary hydrocarbons fractions are fixed and, on the other hand, a comparison of the most significant oil saturation (Fig. 8.13), which defined the position of the zones with only one movable portion of the hydrocarbons in Kynov and Pashiisk horizon reservoirs. On the map of the combined contours (Fig. 8.12), the lower level of saturation is clearly visible in the overlapping regions of Kynov and Pashiisk sediments. Obviously, these areas are the most likely ones for which there is a hydrodynamic connection of productive objects lying in these sediments (especially since, as noted above, the geological structure of the Pashiisk and Kynov rock formations can be characterized as a single sedimentation cycle). These sites of contour overlap can be considered as a zero approximation to a certain optimal spatial distribution of oil-perspective objects in the Kynov and Pashiisk deposits. The optimal distribution of oil-saturated objects simultaneously in both sediments is presented on the combined contour maps of significant oil saturation (along the contour of 10% of the average hydrocarbon content in the reservoir under study) (Fig. 8.13). The map distinctly shows how productive objects are distributed relative to each other. The sites of contour overlap of the most significant oil saturation in these sediments are the optimal areas for deep wells (considering the oil-saturated thickness of each of the horizons, as well as the oil saturation level—the content of oil in the reservoir rock for each of the objects under consideration). Presented in Fig. 8.13, combined contours can be identified as the most promising areas of the overlap—10k-15p; 13k-16p; 3k-3p; 1k-2p; 2k-5p, and 6k-5p. Oilfield objects identified in such a way in the Devonian terrigenous complex (in the Kynov and Pashiisk deposits) on the eastern part of the study region using the HRS-Geo technology are of significant oil-prospecting interest for setting up exploration drilling there. To determine the role of the tectonic factor on the location of potential oil deposits in the terrigenous Devonian, a set of paleostructural maps was additionally constructed along the top of the Pashiisk deposits. Each of them was formed at the end of the corresponding geological epoch—from the Kynov to the Vereiskian period. As a result of such paleoreconstruction of the sedimentation process of the Pashiisk deposits and the subsequent process of overlying sediments accumulation, a complete picture was obtained from the structural reorganization of the area under study in time along the Pashiisk horizon, with which the formation of traps in these sediments is associated. The analysis of paleostructural schemes showed that the area under study is characterized by a sharply differentiated structure and asynchrony of tectonic movements in time. This predetermined the discrepancy between the structural plans in the sediments under study, which reflects the paleotectonic conditions of the paleotrap formation in the reservoirs of the Pashiisk horizon. Thus, under the conditions of the Kynov basin, the most submerged parts of the bottom (the top of the deposits of the Pashiisk horizon) appear in the central part of the area—in the point of the wells 550 and 581 and the contours of the oil-saturated object 15p. At the same time, the maximum basin immersion, reaching 27.5–30.0 m, is noted. All other oil saturation contours are characterized by significantly lower depths of the Kynov basin—from 3 to 15 m.

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan).

303

The asynchrony of tectonic movements of the basement block structure in different parts of the study area during the Burreg period affected the structural plan of the Pashiisk horizon top in such a way that a significant portion of the area turned out to be relatively more submerged compared to the relative hypsometry of the Kynov basin relief. Against this background, the contours of oil saturation turned out to be relatively elevated areas at the end of the Burreg time: 9p, 11p, 14p, 18p, and 19p. Approximately according to the same scheme, the tectonic motions of the basement blocks took place in time, fixed at the end of the Late Tournaisian, but with a significantly different distribution of the elevated and submerged sections of the Pashiisk horizon top. Here, the relatively elevated areas coincide with the contours of the oil saturation of the Pashiisk reservoirs—1p, 13p (partially), 16p, 17p, and 18p. An analysis of the reviewed materials as a whole showed that a significant part of the identified oil-perspective objects in different geological epochs underwent structural reorganizations, and they could turn into epigenetic sealed traps or reform.

8.1.5

Evaluation of the HRS-Geo Technology Use Effectiveness on the Studied Promising Objects

To assess the reliability of oil prospective objects identified within the eastern part of the Onbian area based on the use of HRS-Geo technology, “TATECH” experts implemented an independent analysis of the convergence of drilling results and the results presented by the authors [18]. The analysis of the result convergence was performed separately for the deposits of the Kynov and Pashiisk horizons (Table 8.1). The convergence coefficient of the results turned out to be at least 0.8 (more precisely, for the area under study, 0.82). Thus, using the HRS-Geo technology for the processed borehole and land seismic materials on one of the areas located on the western slope of the South Tatar arch of the RT, the results of the prediction of the geological structure features and material composition of the productive deposits are obtained. This is consistently implemented in sections of seismic lines and in the whole area of research based on the use of appropriate prediction maps for target objects—deposits of the Pashiisk and Kynov horizons. A number of promising areas have been established in reservoirs of the Kynov and Pashiisk horizons. The experience gained in geological and geophysical research as a whole allows us to recommend more extensive research using HRS-Geo technology to solve the subtle and complex problems of petroleum geology aimed at studying the detailed internal structure of terrigenous Devonian sediments in the southeastern part of the Republic of Tatarstan, since the technology used in the seismogeological conditions under consideration has the prospect of active development and application in the general methodology and conceptual approaches of predicting oil resources of the RT.

No reservoir 1.6 No reservoir 1.0 2.0 2.0 No reservoir 3.8 No reservoir No reservoir 1.0 1.2 1.6

1.2 1.4 2.0 1.2 1.0 2.0 3.1

1522.5 1526.2 1528.1 1523.4 1514.5 1519.6 1518.1 1517.8

1519.2 1534.8 1531.7 1519.9

1523.2 1520.7 1527.5 1518.7 1527.1 1542.0 1528.4

257 421 422 448 510 550 551 581 593 595 914 11167 11178

11272 11275 11288 11449 11467 11484 11489

Wellno 23 248

Kynov formation Formation Product top, m interval, m 1512.6 2.6 1527.4 1.4

Water Res. o Res. o Res. o 50.0 58.9 Water

78.1 Res. o 78.1

Water

80.0 Water 81.2

Water

Saturation, % Water 79.5

Water + oil Not defined

9831

973

6604

Oil + water

Production, t

+

Prediction + The anomaly edge + + + + + + +  + + Line edge + Psh deposit edge   + + + 1524.4 1523.0 1534.5 1522.4 1534.0 1546.7 1532.4

1529.5 1528.0 1529.1 1527.0 1517.2 1523.0 1520.5 1522.0 1526.6 1522.2 1533.0 1537.0 1523.6 1.2 4.8 13.4 7.2 15.4 1.6 10.6

8.6 12.2 3.4 4.0 6.2 12.8 9.0 6.4 10.0 5.6 5.0 5.2 6.4

Pashiisk formation Formation Product top, m interval, m 1515.0 9.0 1535.7 1.8

Table 8.1 The convergence analysis of drilling and using HRS-Geo technology results on Onbysk oilfield

Water Water Water Water Water Water Water

Water Water Water 77.4 Water Water Water Water Water Water Water Water Water

Saturation, % Water Water

5196

Production, t

+ + + + + + +

+   + + + + + + + + + 

Prediction + +

304 8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

2068

1006

+  +  + 82%

1530.6 1516.8 1529.0 1527.6 1532.2

Note: the + sign indicates a positive result of the prediction with the use of HRS-Geo technology

11493 1528.3 2.4 Res. o 11542 1510.9 1.2 60.4 11548 1523.7 2.7 Res. o 11553 1522.5 2.6 81.9 11568 1529.8 1.0 Water Convergence of the predicted and drilling results 3.5 10.5 5.7 4.5 6.2

Water Water Water Water Water

+  + + +

8.1 Complex Reservoir of the Pashiisk and Kynovian Horizons Study (Tatarstan). 305

306

8.2

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Detailed Study of Carboniferous, Upper and Middle Devonian Deposits (Orenburg Region)

This subsection presents the results of the complex interpretation of well and land seismic observations from one of the regions of the Volga-Ural oil and gas province. They are obtained using a special processing graph and HRS-Geo technology software. During the field periods 1984–1988, 2D CDP prospecting seismic surveys were performed on the study area in order to investigate in detail the geological structure of one of the oilfields and adjacent territories along reflectors confined to the Vereiskian, Bashkirian, Bobrikov, Tournaisian, Pashiisk, Biisk, and Koiven deposits. Exploration wells drilled in this area and seismic surveys carried out here allowed geologists to understand to a certain extent the features of the geological structure and the oil-saturated objects identified here. The rationale for the seismic exploration performed by the authors was that this area belongs to the eastern part of the Kama-Kinel system of uncompensated troughs. Here in the eastern periclinal part of the Mogutov arch (according to crystalline basement deposits) located within the Buzuluk depression, in accordance with the predicted estimate of the hydrocarbon potential, the density of geological oil reserves turns out to be quite high. The peculiarity of the geological structure is that the target productive strata and individual layers, which have a local distribution in the work area, are characterized by relatively small oil-saturated thickness, varying within 0.4–8.5 m (with an average thickness of  3 m) which in the section alternate with shielding layers or strata of different thickness. At the same time, the most important properties of the target deposits being studied are the distinct zonality of productive packs and the complex nature of their distribution in the area of works [19, 20]. Within the scope of wave seismic prospecting, it is almost impossible to fully ensure the positive prediction results of the lithological composition and reservoir properties of the above thin-layer real media (not to mention the prediction of the nature and degree of fluid saturation of reservoir rocks). The solution of kinematic (structural) seismic exploration tasks in these geological conditions is also not highly searchable. The implementation of such subtle problems of oil and gas geology can only be effective with the help of modern technologies that provide the construction of geological models of a real medium with the highest possible resolution.

8.2.1

Processing and Interpretation of GBS Materials

Processing and automated interpretation of GBS data was carried out to form a detailed geoacoustic model of the medium containing information on the distribution of acoustic velocities, density, lithology, reservoir properties, and oil and gas

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

307

Fig. 8.14 Litho-stratigraphic columns along the wells 4 (Lower–Middle Carboniferous interval) (a) and 11 (Upper Devonian crystalline basement interval) (b)

saturation of reservoir rocks in the depth interval under study (see Sect. 5.1 and [21– 23]). An example of such transformations results is shown in Fig. 8.14. Here are the results from the two wells. In the upper part carbonate sediments of the Bashkirian stage (C2b) along the depth interval of 1842–1865 m of the well 4 (Fig. 8.14a) a number of relatively thin permeable water-saturated interlayers is observed with the following reservoir parameters: porosity KP  5–7%, absolute permeability ka  1–2 mD, and phase permeability for the water kw  0.1–0.2 mD. The deposits of Bobrikov (C1bb) and Radayev (C1rd) horizons, occurring in the interval of 2465–2580 m and represented mainly by terrigenous sandy-clay formations, are associated with oil- and water-saturated permeable sections. The layers of permeable sandstones alternating with strongly clayed intervals are characterized by the following geological indicators: the clay content varies within Ccl  22–50% and oil saturation coefficient is Ko  30–40%; filtration-capacitive parameters of productive layers have the following values: Kp  14–20%, ka  4–32 mD, kw  0.4–3.7 mD, and phase permeability for oil ko  0.4–1.6 mD. Tests performed here

308

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

in the depth range of 2492–2496 m gave an oil flow rate of Qo ¼ 4.5 m3/day. However, the majority of oil-saturated interlayers remained untested. On the transition from carbonate to terrigenous deposits of the Upper Devonian of the well 11 (Fig. 8.14b), the formation of carbonates (lower part of Middle Frasnian substage (Mendym and Domanik horizons) and Sargay layer) is almost impermeable sealed screen, capable of holding hydrocarbons in the underlying reservoirs of terrigenous Devonian. The latter have both clay and clay differences, including carbonate ones (in the zones of active tectogenesis and secondary rock transformation, these differences are good flow conductors of hydrocarbons). Terrigenous Devonian (the Kynov (D3kn) and Pashiisk (D3psh) horizons of the lower substage of the Frasnian stage (D3fr), Mullin (D2ml), Starooskol (D2st), and upper and middle parts of the Vorobiov (D2vb) horizons of the Givetian stage (D2gv)) in the 3211–3357m depth interval are characterized by the alternation of sandy-clayey formations (sandstones, aleurolites, clays, and marls) with rare layers of black bituminous limestone and marls. The productive reservoir interlayer in the considered part of terrigenous Devonian deposits of the borehole 11 is characterized by the following reservoir parameters: Kp  8–20%, ka  5–25 mD, kw  0.8–3.8 mD, and ko  0.1–0.5 mD. For two sections of the Pashiisk deposits, there are well test results (in the depth ranges of 3221–3225 m and 3252–3260 m), which showed the presence of oil flow rate of Qo ¼ 0.12 m3/day (in the first interval) and formation water of Qw ¼ 17.3 m3/day (in the second depth interval). Occurring in the depth interval of 3357–3419 m, sediments of the lower part of Vorobiov (D2vb), Afonin (D2af (ДV’‘layer)), and Biisk (D2bs (ДV’ layer)) strata are mainly represented by carbonates—brownish-gray, fine-crystalline limestone, some clayer sites with stylolite seams, and made of limestone and carbonaceous material. Here, permeable carbonate interlayers with Kp  4–6%, small in thickness and in porosity, are fixed. Deposits of the Koiven (D2cv (ДVI formation)) (possibly and partially Takatin D2tk) horizons in the interval of 3419–3451 m are mainly represented by heterogeneous terrigenous formations (clays, sandstones, and siltstones with sparse interlayers of dark gray, strongly clayer, hidden crystalline, dense limestone clastic rocks). In this part of the section, two permeable water-saturated sandstones in the intervals of 3423–3428 and 3440–3444 m are confidently distinguished. The filtration-capacitive parameters of these layers are characterized by the following values: Kp  18–21%, ka  28–35 mD, kw  4–6 mD, and ko  0.6 mD. The geological structure of the crystalline basement (starting from a depth of 3451 m) involves complexes of metamorphic and igneous rocks of different material composition, mainly of Archean age. Within these complexes, rocks of the granitegneiss formation are developed.

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

8.2.2

309

Geological Structure Prediction along Reference Lines

The obtained results of GBS data processing were used in the interpretation of CDP time sections and the results of seismic record inversion in the form of effective acoustic impedance and reflectivities. Figure 8.15a shows a fragment of the original time section, obtained after preliminary seismic data processing on a special graph (see Sect. 4.3 and [24]); Fig. 8.15b shows a fragment of the effective acoustic impedance. For the stratigraphic binding of the reflecting horizons to the corresponding geological boundaries of the studied section, we used the ΔТ curves of the sonic logging (SN), which were previously reduced to the timescale. Such SN curves with their geological breakdowns, superimposed on the vertical sections effective acoustic impedance (AI), fit into this section, making it relatively easy to identify the corresponding lithologic stratigraphic horizons. The prediction results for the considered line in the form of predicted distributions of the clayiness, sandiness, carbonate content, porosity, and water, and oil saturations are shown in Fig. 8.15c–h. The predicted lithologic-stratigraphic columns with fluid-type distributions for the given vertical profiles in the productive part of the section with interval of 50 m along the profile are shown in Fig. 8.15i. Estimating the complex of promising sediments as a whole, it should be noted that it is rather strongly clayed (Fig. 8.15c). Among the entire spectrum of interlaying horizons and layers, the greatest clay formation (in the figure this is a thick bluegreen palette) is observed in the intervals of emergence of the Vereiskian C2vr (А3) sediments, Yelkhov C1el, Mullin, Starooskol, and Vorobiov strata (the content of clay material here varies between 75 and 95%). In the terrigenous sediments of the Bobrikov suite C1bb (Б2), Radayev C1rd, Pashiisk D3psh (Д0, Д1), and Koiven D2cv-tk (ДVI) horizons and the weathering crust (w.c.) of the crystalline basement, the content of the clayey material is much less (here average Ccl ranges 40–75%). Carbonate deposits are characterized by much less clay formations. These are primarily the deposits of the Bashkirian C2b and the Namurian C1n stages, the Serpukhovian C1srp substage, the Oksk supra-horizon С1ok, and the Tulean horizon С1tl and also Famennian D3fm, Middle and Upper Frasnian stages and AffoninBiisk-Kaltseol D2af + bs + cl horizons (here Сcl varies in the range of 5–15%). The sandiness of the section, as is known, determines the placement of reservoir development zones (Fig. 8.15d). The reservoir properties as predicted porosity for the sediments in discussion are presented in Fig. 8.15f. According to the nature of the distribution, the most porous are those among them that have a high content of sandiness. These are layers of sandstones and siltstones in sediments of predominantly coastal-marine and continental genesis (C2vr (A3), C1bb (B2), C1rd, D3psh (Д0, Д1), D2cv-tk (ДVI), and basement weathering crust (w.c.)). The porosity of these deposits is characterized by an uneven distribution along the lateral direction. In general, the porosity for sediments of these horizons is characterized by values of 14–22% (Fig. 8.15f).

Fig. 8.15 Input and prognosis seismic sections along the line 058802: a fragment of the time section as a result of a special processing graph use (a), the section of effective acoustic impedance (b); the prognosis results for the sediment of the Middle Carboniferous—crystalline basement interval: clayiness (c), sandiness (d), carbonate content (e), porosity (f), water saturation (g), oil saturation (h), and prognosis lithostratigraphic columns for the specified vertical sections (i)

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

Fig. 8.15 (continued)

311

312

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.15 (continued)

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

313

The distribution of the predicted carbonate values for the considered line 058802 is shown in Fig. 8.15e. Its elevated values are characteristic for the deposits of the Bashkirian and Namurian stages, the Serpukhovian substage, the Oksk suprahorizon, and the Tulean horizon, as well as the Tournaisian, Famennian, Middle and Upper Frasnian stages, and the Afonin-Biisk-Kaltseol horizon. As can be seen from Fig. 8.15e, Ccb. are in the range of 75–95%. By the nature of the predicted porosity distribution as a whole over the entire studied interval of productive sediments shown in Fig. 8.15f, the intervals of the terrigenous section are the most porous, associated primarily with the deposits of the Pashiisk and Koiven horizons. The specified intervals of productive sediments with fairly good reservoir properties are filled with fluids, in particular, formation water (Fig. 8.15g) and hydrocarbons: oil + gas (Fig. 8.15h). The most significant oil-saturated areas of the studied productive strata are directly related with these intervals. It should be mentioned that the studied productive deposits of the Devonian and the lower half of Carboniferous are characterized by structure heterogeneity and uneven distribution oil and gas resources. This is clearly seen both on the predicted values of the lithologic component, reservoir properties, the nature and degree of reservoir rocks saturation with fluids (Fig. 8.15c–h), and the predicted lithologic columns with fluid-type distributions for the given vertical sections, as shown in Fig. 8.15i (with a step along the line in 50 m). From it, in particular, it is clear that the development of reservoirs along the profile is zonal, most often lenticular. The most representative intervals of the horizons are C2vr (А3), C2b (А4), C1bb (Б2), C1t (Т1), D3psh (Д0, Д1), D2af + bs + cl, and w.c. crystalline basement, and partly C1rd and D2cv (ДVI) for oil saturation and lateral traceability are distinguished in local arched areas, as well as on the slopes of anticlinal structures, where there is an increase in oil-saturated thickness and reservoir properties (Fig. 8.15h). In general, for these deposits, the oil saturation in the section is intermittent. Porosity in them varies in a relatively wide range of Kp  5–20% (Fig. 8.15f), and the coefficient of oil and gas saturation is Ko  40–90% (Fig. 8.15i). The average oil saturation in the sediments of these horizons changes within fairly wide limits— 5–25% (Fig. 8.15h). For a generalized presentation of the prediction results of lithology, reservoir properties, and fluid composition obtained from seismic data, it is convenient to reduce them into columns, similarly to traditional well lithologic stratigraphic columns and to display the predicted parameters in the form of lithologic-fluid columns with a predetermined step along the seismic line (Fig. 8.15i). In accordance with the prediction results (shown in Fig. 8.15i) in the different parts of the line under consideration and in different time intervals, it can be seen that in the prescribed vertical sections (in the form of predicted lithologic-fluid columns recorded along the line with step 50 m) are completely conditioned, there appears to be oil-saturated intervals in almost all the above productive and prospective deposits. It should be especially noted that at certain intervals of the productive horizons (both in terrigenous and carbonate sediments), there are no obvious oil-saturated intervals. This is despite the fact that in the well section (the vertical section of which

314

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

is located close to the vertical plane of the seismic section under study), it is possible to trace relatively thin (not exceeding 1–3 m) oil-saturated interlayers. The obtained results indicate that seismic exploration in this particular geological situation does not always reveal the oil saturation of all thin objects (having specific values of productivity, filtration-capacitance characteristics, and thickness ratios with their enclosing medium according to well log data), equal to 2 ms. Of particular interest are materials characterizing the geological structure of the deposits of the uppermost part of the crystalline basement (foundation) in connection with the possibility of obtaining here industrial inflows of hydrocarbons, primarily from fractured and weathered zones (disintegration and leaching zones)—terrigenous formations of the basement weathering crust. The possibility of studying on this basis the nature of the crystalline base near-surface part articulation with the underlying sediments of the sedimentary cover and identifying here the formation conditions (or predicting criteria) of lithological, lithologic-stratigraphic, and combined traps is also important. Thus, a comprehensive analysis of the prediction results obtained using the HRS-Geo technology substantially complements traditional seismic information with geological indicators that are very important for petroleum geology.

8.2.3

Prediction Geological Indicators According to HRS, GBS Data, and Area Drilling

The basis of the structural construction formation and areal predicting of geological and geophysical parameters were the results obtained along profiles. Bashkirian stage. Along the top of the A4 productive layer of the constructed structural map (Fig. 8.16a), it is possible to judge the structure of potential petroleum reservoirs in the sediments of the upper part of the Carboniferous system and middle series of the Bashkirian stage. The surface morphology of these deposits has a generally inherited development pattern with the underlying horizons of Carboniferous and Devonian. Within the contour of the plot, the structural forms of the identified elevations are conventionally contoured with the isohyptic of 1665 m. With this absolute elevation, all local structures stand out confidently. The amplitude of the elevations varies in the range of 15–25 m. Local elevations along these deposits are separated from each other by saddles with a relatively complex shape of their location in the plan. It should be noted that in the sediments of the upper part of the Bashkirian stage with proven oil content (according to well test data from a number of existing wells), there may be new potential oil and gas objects in the conditions of development zones of porous-cavernous-fractured reservoirs. The reservoirs here are dolomitic limestones, which are almost universally fixed in the borehole sections. The results of areal predicting based on seismic data of the most important geological and geophysical indicators in sediments of the considered horizon are

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

315

Fig. 8.16 The results of mapping along the deposits of Bashkirian stage (C2b): the structural map along the sediment top (a) and the prognosis map of oil and gas saturation (b)

316

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

presented by the map of predicted average values of oil saturation in Fig. 8.16b. Considering the oil saturation degree of the C2b horizon deposits, possible oil manifestations using well data are estimated: the results of the core material analysis and the well log data processing and testing at the appropriate borehole intervals. On the basis of taking into account the specific type of reservoir in the sediments under study, their pore structure, the level of the moving part of hydrocarbons, was set to 4% and higher. Accordingly, the level for the fixed part of hydrocarbons is assumed to be 4% and below (at this level significant oil-saturated areas are covered—the desired oil and gas objects). As a result, contours with possible signs of oil saturation were highlighted (in Fig. 8.16b, they are shown in red closed lines with berg strokes). Oil-saturated fields are fixed in the form of separate local zones distributed evenly over the entire research area. Most of them are hydrodynamically interconnected. A number of small sites (with lower oil saturation levels) are fragmented, located in different parts of the work area. Some zones are characterized by fairly high oil saturation values, reaching values of 6–8%. It should be noted that a significant part of the wells (in particular, wells 3, 6, 9, 10, 11, 15, and 126) is outside the contours of the most significant oil saturation. Bobrikov horizon. The relief of the upper part of Б2 formation sediments of the Visean stage (C1v) Yasnopoliana supra-horizon (C1ja) of the Carboniferous middle series is characterized by the structural map (Fig. 8.17a), which reflects the potential hydrocarbon reservoirs structure in the Bobrikov horizon (C1bb) deposits. On the presented map, within the scope of the works, one can clearly see the complex structure of the revealed zones of elevations. Reservoirs here are aleurolites and sandstones with fine and medium grains with inclusions of plant residues that are found in the well sections (Fig. 8.14a). The tectonic structure of this horizon also retains the features of underlying sedimentary strata inheritance. The structural zone of the identified uplifts as a whole can be delineated with an iso-gypsum of 2280 m. From this absolute elevation, the local elevations (structures) stand out confidently, and the amplitude of the uplifts varies within 10–15 m. On the map of predicted average values of oil saturation in sediments of the horizon under consideration, the contour of the movable hydrocarbons, and for the sediments of horizon C2b (A4) is conditionally drawn along an isoline equal to 4% (Fig. 8.17b). The most intense anomalies of oil saturation are distributed over the area rather unevenly. Two relatively large contours with significant oil saturation are located in the area of wells 5 and 4 (the level of oil saturation is within 4–7%). All other wells are located beyond the limits of oil saturation. In the north and northeast is a series of anomalous oil saturation zones (two of which are located near wells 7, 6, 15, and 128). These contours are directly confined to the structural uplift zone located in the northern part of the area (the level at some points here reaches  10%). Pashiisk horizon. The presented structural map of the top the Pashiisk horizon within the work area clearly shows their complex tectonic structure in the form of sublatitudinal zones of uplifts separated by saddles with a relatively complex shape

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

317

Fig. 8.17 The results of mapping along the deposits of Bobrikov suite (C1bb): the structural map along the sediment top (a) and the prognosis map of oil and gas saturation (b)

318

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

of their location in the plan (Fig. 8.18a). In deposits of the Pashiisk horizon with proven oil content (according to test data from wells 11, 14, and 125), in principle, there may be additional potential oil and gas objects. Reservoirs here are aleurolites and sandstones with fine and medium grain with inclusions of plant residues that are found in well sections (Fig. 8.14b). The cover screen for the Pashiisk reservoirs are the clay formations of the Kynov horizon and the Domanik-type limestone of the Sargay horizon. The revealed structural zone of the uplifts on the Pashiisk horizon as a whole is contoured by iso-gypsum of 3060 m (Fig. 8.18a). Local elevations (structures) stand out with confidence; the amplitude of elevations is within 10–40 m. On the map of predicted average values of oil saturation for the Pashiisk horizon, zones with a movable hydrocarbons are distributed over the area with a certain regularity—the largest oil saturation anomalies are located along the strike of the vaulted parts of positive structures (Fig. 8.18b). At the same time, individual oil-saturated places are hydrodynamically interconnected. A number of small areas (with lower oil saturation levels) are fragmented, located in different parts of the work area. Oil-saturated zones are characterized by oil saturation values reaching up to 7–9%. Within the site in a sublatitudinal direction (from west to east) in the position of the wellheads 14, 11, 126, and 125, three anomalies are observed. The first of them covers wells 14 and 11. The other two are located to the southeast of wells 126 and 125. The sufficiently long oil saturation anomalies cover the well 128. All of them are confined to the arched sections of the northern zone of the uplifts stretched in a sublatitudinal direction. A series of local oil saturation anomalies is also located to the south of the work area. However, these anomalies are recorded only on single sites of a small number of seismic profiles (Fig. 8.18b). The porosity distribution in the research field turns out to be difficult (Fig. 8.18c). In the study area, zones with increased porosity (varying in the range of 4–12%) are unevenly distributed—separate sets of such zones are located in the center, north, and south of the area. The confinement of such zones (reaching values of 8–12%) to the oil saturation contours is observed. A significant decrease in porosity (with a range of changes of 4–8%) is observed outside the contours of oil manifestations. A map of predicted oil-saturated sediment thicknesses of the D3psh horizon (Д0, Д1) with superimposed contours of average oil saturation is presented in Fig. 8.18d. The highest values of the parameter under consideration are observed in areas with high oil saturation. The values of oil-saturated thickness vary in the range of 2.5–12.5 m. The distribution of the contours of these thicknesses here is in good agreement with the contours of oil saturation and with the isoline shape of increased average values of oil saturation inside these contours (Fig. 8.18d). It should be noted a feature that shows the heterogeneity of the studied section is due to the complex morphology of the pore space, the nonconstant content of the clay material, the complex mineral composition of the matrix, etc. This ultimately leads to a difference in the wide range of density distribution and bulk hydrocarbon content. If all the above processing and complex interpretation of well and land seismic observations over the survey area are taken as a certain element of the

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

319

Fig. 8.18 The results of mapping along the deposits of Pashiisk horizon (D3 psh (Д0, Д1)): the structural map along the sediment top (a), prognosis maps of oil and gas saturation (b), porosity (c), oil-saturated thickness (d), and hydrocarbon distribution density (e)

320

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.18 (continued)

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

321

Fig. 8.18 (continued)

“prospecting filter” [9], then an estimate of the predicted hydrocarbon distribution density can be taken as some result of applying such a filter to a specific actual geological and geophysical material. It should be borne in mind that when calculating hydrocarbon resources and the component of the geological substance in the identified contours of significant oil saturation, specific variable values should be directly taken into account. These are the porosity coefficient, effective oil-saturated thickness, oil saturation coefficient of the reservoir pore space, etc. This is usually done using the volumetric method formulas at each of the calculated points (more precisely, for each of the seismic CMP traces of the prediction geological parameters in the vertical section). Such a heterogeneous distribution of the geological substance components (including hydrocarbon resources) is primarily due to the complex heterogeneous structure of the target research objects. If we take one of the above variables as a basis, in particular, the oil-saturated thickness of the prospective horizon, then we can easily see that within the volume space occupied only by this parameter, besides the hydrocarbons themselves, there is also a lithological component, as well as pore space, fully or partially filled with hydrocarbons and formation water. In order to find only the share of oil—hydrocarbons (without lithology and water saturation portion) in linear units of measurement, all components of the geological substance, including the hydrocarbons themselves, must be normalized in selected units of measurement, for example, at the depth scale

322

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

(in the same units, which is the oil saturation thickness, i.e., in meter units). Then from the total volume of the component of the geological substance, everything that is not related to hydrocarbons should be subtracted. Ultimately, this residue will characterize the oil volume portion (in the depth scale units). This procedure of normalization and recalculation of the volume content of the geological matter components into the depth scale is implemented in the form of an algorithm and a computer program. Based on their use, prediction hydrocarbon (oil) distribution density maps for deposits of productive and prospective horizons were calculated and constructed. It should be emphasized that the results of the predicted hydrocarbon (oil) distribution density for productive layers are sets of cylinders with curvilinear (in work area) contours of their vertical walls. Such hypothetical cylinders of the real geological environment are filled with hydrocarbons—oil. At the same time, the height of the walls of a single curvilinear cylinder for each of the objects of the studied sediment complex is different. It varies in the range of 0.1–0.2 m. The results of constructing such a prediction parameter for deposits of the D3psh reservoir (Д0, Д1) are presented in Fig. 8.18e. In accordance with the thickness scale of the oil deposit reservoir, its change range is within 0.0–1.4 m (with a height of the reservoir walls equal to 0.2 m). The greatest predicted hydrocarbon distribution density is characterized by the contours of oil saturation, located mainly within the areas of the Pashiisk reservoir development. Zones thus obtained with increased values of the predicted hydrocarbon distribution density over each of the horizons under consideration are taken into account when determining promising points for subsequent prospecting. This takes into consideration the full range of other geological indicators. Taking into account the above, the location of each of the project exploratory wells is determined for the opening of the predicted oil-saturated objects in the work area, as well as possible versions for the subsequent operation of these objects. Another important application of predicted hydrocarbon distribution density maps is their use in calculating hydrocarbon reserves and resources (the result of integrating the predicted hydrocarbon distribution density (2D coordinate function h (x,y)) is the value of reserves and resources of promising objects). Such an approach, in our opinion, turns out to be more reasonable and accurate, and, consequently, more reliable than the traditional volumetric method of calculation, which introduces the assumption of horizontal homogeneity of the calculated parameters.

8.2.4

Comparison of Oil Saturation Contours

Comparing the oil saturation contours identified using the HRS-Geo technology data with similar contours for the sediments under consideration, but built by other geophysical organizations, clearly shows the difference in oil saturation contours obtained by using fundamentally different approaches to predicting oil and gas saturation zones (Fig. 8.19a–c). One of such approaches to oil-bearing prediction,

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

323

Fig. 8.19 HRS-Geo-generated oil saturation outlines compared with oil saturation outlines prepared by other geophysical organizations for deposits: Bashkirian stage (C2b) (a), Bobrikov suite (C1bb) (b), and Pashiisk horizon (D3 psh (Д0, Д1)) (c)

324

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.19 (continued)

the results of which are presented in the work of other specialized organizations, is based mainly on using data from traditional wave seismic surveys, primarily on the use of a structural factor (the external and internal contours of oil-bearing potential are contoured over the area of the anticlinal structure isolines; additional information is also used on the zones of lithological replacement of reservoirs by a non-collector, most often obtained from well data). The second approach to predicting oil-bearing technology (HRS-Geo technology) is based on the use of subtle dynamic features of seismic record, namely, on high-resolution sections— sections of effective acoustic impedance (AI) and deep integration of high-resolution seismic materials with GBS data. From this comparison, the limitations of the first and the possibilities of the second of the noted approaches are quite clearly manifested. There is only a partial coincidence of the shape of the oil saturation contours obtained from the first and second approaches to the prediction of oil bearing (Fig. 8.19a–c). In terms of the contours’ compared number, these differences are quite significant (in the second approach to the oil content prediction, the number of oil saturation contours is much larger). Thus, the work done on the study of the detailed internal structure of the target horizons and layers using high-resolution seismic techniques, HRS-Geo technology, made it possible to obtain representative prediction results in the form of the geological and geophysical parameters of the real environment that are most important for prospecting and industrial exploration. In order to solve the subtle and complex problems of petroleum geology, aimed at studying the deposits of the oil

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

325

Fig. 8.20 Oil saturation outline comparison obtained by different geophysical organization for the sediments: Vereiskian horizon C2vr, Bashkirian stage C2b, Bobrikov suite C1bb, Tournaisian stage C1t, D3 Pashiisk horizon psh, D2

and gas complex of the Lower and Middle Carboniferous and Middle and Upper Devonian in the territory of the considered region, it is recommended to provide for more extensive research using the developed HRS-Geo technology. Search for oil perspective objects. The rationale and geological precondition for further prospecting there and to develop the identified oil objects is a multi-bed oil saturation of productive horizons and layers, which confirmed the results of the drilling of several exploration wells and the results of the layer well test, in particular, in Vereiskian horizon, Bashkirian stage, Bobrikov horizon, Tournasian stage, and Pashiisk and Koiven horizons. Plots (contours) of oil deposits, which are identified by the results of previous studies, are presented here as oil perspective objects for further development (Fig. 8.20). However, there were obtained results of comprehensive studies, which largely clarify and complement the results of previous studies [25]. Using the HRS-Geo technology, as well as a relatively large number of exploration wells, the results of predicting geological and geophysical parameters were obtained, which increased the efficiency of the work carried out on the basis of extracting from seismic data additional geological information about the lithological composition, reservoir properties, and the nature and degree of rock reservoir

326

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.21 Litho-stratigraphic column along the well 125 in Upper Devonian - crystalline basement interval (legend on the Fig. 8.14)

saturation with fluids [25]. An example of the GBS data processing and automated interpretation for well 125 in the interval of the most productive part occurrence and the terrigenous Devonian stratum is shown in Fig. 8.21. For practical purposes, it is important to jointly evaluate oil saturation contours, which gives an idea of the predicted (productive and prospective) area distribution in the sediments. It is assumed that at the final stage of research, the used oil saturation contours to a certain extent “include” the results of a comprehensive analysis and prediction of various geological indicators extracted from seismic data for the productive objects under consideration. The results of oil saturation prediction thus obtained for various horizons and formations by combining their oil saturation contours are shown in Fig. 8.22a–c. Here, in addition to obtaining the results of a comprehensive analysis of these oil saturation contours, the most promising areas for further prospecting are determined. Specifically, for the optimal opening of the predicted oil-saturated objects within the combined oil saturation contours for the Vereiskian horizon deposits, the Bashkirian stage, and the Bobrikov suite (Fig. 8.22a); Radayev suite of the Tournaisian stage and the Pashiisk horizon (Fig. 8.22b); the lower part of the Vorobiev, Afonin, and Biysk horizons and the Koiven horizon and the upper part (А–А1) of the crystalline basement (Fig. 8.22c), seven prospective sites (points) have been established. The

Fig. 8.22 Significant hydrocarbon saturation outline map for reservoir formations of: Vereiskian (C2vr), Bashkirian (C2b) stages and Bobrikov suite (C1bb) (a), Radayev suite (C1rd), Tournaisian stage (C1t) and Pashiisk horizon (D3 psh (Д0, ДI)) (b), lower portion of Vorobiov (D2vb), Afonin (D2 af (ДV’‘)), Biisk (D2bs (ДV’)) horizons, Koiven horizon (D2cv (ДVI)), and crystalline basement upper portion (А–А1) (c)

328

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.22 (continued)

sequence of numbers of these points is set to reflect the decrease in their representativeness (priority)—the smaller number corresponds to a greater degree of priority. There are a number of overlapping oil saturation contours that may have some practical interest for prospecting (but with slightly lower priority). The placement of oil saturation contours. From Figs. 8.22, 8.23, and 8.24, it can be seen that many of the oil saturation outlines (for different reservoirs) are both in the contour of the site and beyond the contour. At the same time, the boundaries of the site themselves turn out to be intersecting these border areas of oil saturation. This picture is practically observed, as in Fig. 8.22, where the contours of oil saturation are distributed by increasing the depth of research, and in Figs. 8.23 and 8.24, where only the largest oil saturation contours are concentrated. In the latter case, these are contours for the deposits of the Bashkirian stage and Tournaisian and Pashiisk horizons (obviously, this set of three oil perspective objects should also have the highest volumes of predicted hydrocarbon resources, which is confirmed by our calculations). On the basis of the materials received, the authors considered the version of the subsequent development of the identified oil prospective objects according to the HRS-Geo technology. Thus, on the maps of combined contours of predicted oil saturation by deposits of the three largest oil prospective objects (based on deposits of the Bashkirian stage

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

329

Fig. 8.23 Significant hydrocarbon saturation outline map for reservoir formations of Bashkirian (C2b) stage, Tournaisian stage (C1t) and Pashiisk horizon (D3 psh (Д0, ДI)) with the contour of the project addition to the study area

and Tournaisian and Pashiisk horizons), the highest volumes of predicted hydrocarbon resources were obtained (0.519, 0.484, and 0.513 million tons), and the location schemes of promising points were constructed using exploratory wells that already exist in the area for optimal opening of identified oil prospective objects (Fig. 8.25a– b). These diagrams show the well number, the direction of horizontal deviation, and the deviation (in meters) of each of the perspective points from the wellhead to the optimum opening of the productive horizon (the number of the opening point is indicated with a stroke) and also indicates the distance between these points. In addition to these well location schemes, a special table was prepared with the main geological and technical characteristics for each of the outlined prospective points. For each of the points in this table, the wellhead number; target horizon or stage, which is advisable to open; borehole deflection direction; wellhead coordinates; opening point number (with one or two strokes); coordinates of the horizon opening point; horizontal distance between points {x,y} and {x’,y’}; the absolute elevation of the target horizon at the opening point; and prediction indicators (i.e.,

330

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.24 Schematic showing proposed license area extensions for additional studies with integral oil saturation outlines

horizon thickness average values of oil saturation, porosity, oil-saturated thickness, hydrocarbon distribution density, area of oil-saturated anomaly, and recoverable resources (in million tons)) are given.

8.2.5

The Results of the Identified Object Opening

In accordance with the foregoing results of the oil-bearing prediction, the authors recommended the opening of the identified objects using the indicated diagrams of deviations of the existing boreholes (Fig. 8.25a–b) and a detailed table of geological and technical characteristics for each of the outlined prospective points [14]. At the same time, for the section of the well 125 (Fig. 8.21), the authors recommended to

8.2 Detailed Study of Carboniferous, Upper and Middle Devonian. . .

331

Fig. 8.25 Prospects for further geological and technical measures using the existing wells of the study area to open the identified oil objects in sediments: the Tournaisian stage C1t and the Pashiisk horizon (D3 psh (Д0, ДI)) (a), the Bashkir stage C2b (b)

additionally well test the upper part of the Koiven horizon in the depth interval of 3400–3405 m. In this part of the section, as shown in Fig. 8.21, as a result of processing and automated interpretation of the GBS diagrams, an oil-saturated formation with an oil saturation coefficient Ko  75–80% was fixed. As can be seen from the figure, the well test here was conducted about 10 m below the specified interval. Koiven deposits were tested in the range of 3409–3412 m (3246.1  3249.1 m), from which an inflow of net oil of 18 m3/day was obtained when the level of 910–850 m was restored. The authors recommended to perform an additional perforation of the borehole № 125 in the depth range of 3398–3406 m in order to open the upper oil-saturated interlayer lying in the depth range of 3400–3404 m. In conclusion, we note that in the process of further testing of identified oil perspective objects in the area under study, data were obtained, confirming the results of the prediction made using the HRS-Geo technology [25, 26]. In particular: (1) Reuse of the wellbore 126 in the form of an inclined well—its deviation in the northeastern direction indicated above (pk 51 of line 518822)—provided an influx of hydrocarbons from sediments with a flow rate of 30 tons/day at a bottomhole pressure of 144 atm; (2) Retesting of the well 125 in the interval of the Kojven sediment occurrence in the recommended depth range of 3400–3405 m gave a positive result—the influx of hydrocarbons with an increased flow rate.

332

8.3

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Geological Indicator Estimation in Productive and Prospective Middle and Lower Carboniferous and Upper and Middle Devonian Sediments (Samara Region)

In [27–29], the authors showed the possibility of complex processing and interpretation of well and 3D CDP land seismic data, obtained from one of the areas of the Volga-Ural oil and gas province. In [27], as an example of studying the detailed geological structure of productive sediments of the Middle and Lower Carboniferous (Vereiskian and Bashkirian, Bobrikov, and Tournaisian horizons, respectively), as well as horizons of the Devonian terrigenous stratum, a set of effective acoustic impedance, clay, sand content, porosity, water saturation, and oil and gas saturation sections was considered. This is done in one of the composite profiles, passing through the vertical sections of the boreholes. For a number of wells, the processing and automated interpretation of GBS data was carried out by integrating geophysical parameters into a single geological information system through functional transformations of geophysical parameters (Sect. 5.1 and [23, 28]). For the tops of all productive and perspective horizons (suites) and the surface of the crystalline basement, structural constructions were also performed. The materials obtained in this way formed the basis for the further predicting of geological and geophysical parameters in the sediments of the Lower and Middle Carboniferous and terrigenous Devonian under consideration. The work [28] presents the results of the prediction of geological and geophysical parameters for the deposits of the Vereiskian suite (C2vr) and the upper part of the Bashkirian stage (C2b) of the Middle Carboniferous, and in [29] for the deposits of the Bobrikov suite (C1bb) and the upper part for the Lower Carboniferous and Tournaisian stage (C1t). For prospecting and exploration, these deposits in the study area are among the major petroleum complexes. As the initial data for the prediction, we used the results of solving an inverse dynamic seismic problem— reconstructed (from seismic records) 3D cubes of effective reflectivity (RC) and acoustic impedance (AI), which have vertical resolution equal to the sample rate of seismic record over time, i.e., Δt ¼ 0.002 c. A distinctive feature of such processing was the use of the seismic inversion procedure for the original seismograms and the final seismic records migrated within the 3D cube. The materials presented in this subsection are a further development of predicting various geological indicators using high-resolution seismic data obtained applying the software of the HRS-Geo technology [1, 24, 30, 31]. Utilizing practically the same borehole and initial seismic data as in [27–29], the authors performed studies on the deposits of the Devonian terrigenous sequence, in particular, on the Kynov (D3kn), Pashiisk (D3psh), and Ardatov (D2ar) horizons. On this basis, the results of complex dynamic processing and interpretation of well and land seismic data, which characterize the geological features of the study area structure, are presented below. The purpose of the research is the formation of a detailed geoacoustic model containing information on the distribution of the lithological composition, reservoir

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

333

properties, and oil and gas saturation of reservoir rocks in the interval under study (in sediments of terrigenous Devonian stratum).

8.3.1

Automated Processing and Interpretation of GBS Data

GBS materials recorded on magnetic media in the corresponding LAS formats were represented by a set of standard logging methods. These are data from radioactive logging (GR, NL), electrometry (AL, SP, IL, LL), caliper (CAL), and acoustic (SN (ΔT) logging methods. For productive and prospective well cross sections, there were also test results that were taken into account when evaluating the results of automated processing of GBS materials directly during the interpretation process and when setting the parameters for specific intervals. The results of such processing for a number of wells in the area of research are given in [25]. As an example of such results, lithologic-stratigraphic columns of well sections are shown for the deposits of Moscovian (C2m) and Bashkirian (C2b) stages of the Middle Carboniferous (Fig. 8.26), Bobrikov horizon (C1bb) and the upper part of the Tournaisian stage (C1t) (Fig. 8.27), and deposits of terrigenous Devonian stratum (Fig. 8.28). The processing of well log data on the presented wells was performed using a specially developed WPS system, which is a component of the HRS-Geo technology (see Sect. 5.1). Permeable interlayers in sediments of Middle Carboniferous with a thickness of 2–7 m stand out in the middle and mainly in the lower parts of the deposits of the Vereiskian horizon (C2vr) of the Moscovian stage (C2m) (Fig. 8.26). Oil-saturated interlayers of this horizon are represented by terrigenous formations—aleurolites, sands, and sandstones. Throughout this interval, the alternation of sandy-silt rocks with clay rocks is fixed. Moreover, the clay intervals are somewhat more thick than sand-silt interlayers. In these clay intervals, the content of the clay material is Ccl  80–100%. The permeable intervals of the Vereiskian horizon are characterized by the values of oil saturation coefficients of Ko  70–80% and porosity of Kp  12–18%. In the lower part of the sediments of the considered horizon, well testing was performed. Practically in all wells, the inflow of pure oil with gas volumes of 0.54–1.0 m3 was obtained (Fig. 8.26). The deposits of the Bashkirian stage (C2b), represented by carbonate rocks, are characterized by a substantial heterogeneity of the geological structure. This nonuniformity is manifested both in reservoir properties, the lithology of oil-saturated carbonates, and in oil-saturated thicknesses. At the same time, the permeable carbonate interlayers of different thickness and porosity are fixed at the very top of the stage. For them, porosity is characterized by Kp  5–15% and oil saturation of Ko  90%. The testing performed for this section interval showed the inflow of fluids: pure oil and oil with a gas volume of 0.18–0.57 m3 (Fig. 8.26). At the same time, a higher oil saturation is associated with that part of the section, where the content of the dolomite component is approximately twice the content of limestone.

334

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.26 Litho-stratigraphic column with distribution of character and the degree of reservoir rock fluid saturation along the well 200. Bobrikov horizon and Taurnian stage sediment interval (legend on Fig. 8.14)

For productive sediments of the Lower Carboniferous, individual permeable interlayers with a thickness of 1–5 m stand out quite confidently in both the middle and lower parts of the target sediments—the Bobrikov horizon (C1bb) of the Yasnopoliana supra-horizon (C1ja) (Fig. 8.27). At the same time, heterogeneous permeable formations of sandstones are characterized as oil saturated. Impermeable intervals are mostly heavily clay-covered rocks. Clay content in oil-saturated intervals varies within Ccl  5–10% and in impermeable clay intervals, Ccl  80–100%. The porosity for oil-saturated interlayers is characterized by Kp  14–20% and oil saturation of Ko  70–90%. During the testing performed in the well 200 in the depth range of 1255–1260 m, the inflow of liquid of 27.2 m3 was obtained; pure oil of 13.1 m3 was produced; and in the depth interval of 1263–1267 m, pure oil was obtained.

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

335

Fig. 8.27 Litho-stratigraphic column with distribution of character and the degree of reservoir rock fluid saturation along the well 200. Moscovian and Bashkirian stage sediment interval (legend on Fig. 8.14)

The deposits of the Tournaisian stage (C1t), represented by carbonate rocks, are also characterized by a significant heterogeneity of the geological structure. They consist of gray, crystalline, dense, and porous limestones, in some places clayey and also carbonate rocks of transitional composition. Among the limestones, biodetritus, biomorphic detritus, and pelitomorphic predominate, interlayers of dolomite and anhydrite are also found [32]. At the same time, the heterogeneity of these carbonate rocks is manifested both in reservoir properties, lithology (in terms of the volume content of limestone and dolomite) of oil-saturated carbonates, and in oil-saturated thicknesses. At the same time, the permeable carbonate interlayers of different thickness and porosity are fixed at the very top of the stage. Their porosity is characterized by the values of Kp  5–15% and oil saturation of Ko  90% (Fig. 8.27). In the process of testing performed in the section of this well (in the depth range of 1275–1292 m), the first time received a flow of liquid of 21.1 m3, from it pure oil of 5 m3, and the second time received a flow of pure oil of 1.4 m3. Deposits of the terrigenous Devonian stratum are represented by sandy-clayey formations of the Kynov (D3kn) and Pashiisk (D3psh) horizons of the lower substage of the Frasnian stage (D3fr), Mullin (D2ml), Ardatov (D2ar), and upper and lower parts of the Vorobiov (D2vb) horizons of the Givetian stage (D2gv) (Fig. 8.28). These deposits are characterized by the alternation of sandy-clayey formations (sandstones, aleurolites, clays, and marls) with rare layers of black bituminous limestone and marls. The most clayey areas here are the intervals in the Kynov, Pashiisk, Mullin, and upper parts of the Ardatov and Vorobiov horizons. Here the

336

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.28 Litho-stratigraphic column with the distribution of the character and degree of reservoir rock fluid saturation of the Devonian terrigenous strata along the well 160 (legend on Fig. 8.14)

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

337

content of the clay material reaches the values of Ccl  80–100%. Relatively thin layers of sandstones and siltstones in the Pashiisk, Ardatov, and Vorobiov horizons are less clayey. The genetically (often hydrodynamically) interrelated permeable layers of horizons are composed mainly of well-sorted quartz sandstones and siltstones. Quite confidently, a number of permeable water-saturated strata and interlayers in the sandstones of the Kynov horizon at depths of 1936–1967 m; Pashiisk horizon at depths of 1967–1981 and 1994–2003 m; Mullin and Ardatov horizons at depths of 2048–2065 m, and the Vorobiov horizon at depths of 2072–2080 m stand out. The porosity in these intervals of the section varies in the range Kp  10–21%. Within other sandy-silt parting, relatively high values of clay content are observed. There is no oil saturation in almost all permeable intervals of the section, except for the two uppermost interlayers in the sediments of the Kynov horizon, occurring at depths of 1936–1948 m—signs of oil saturation “break through” here. Testing in sediments of the terrigenous Devonian stratum was performed at depth intervals of 1938.2–1934.8 and 1935.6–1936.6 m. The following results were obtained: for the first interval, the absence of fluid influx and, for the second interval, the influx of saline solution of formation water with an oil film were obtained (Fig. 8.28).

8.3.2

Prediction of Geological Structure along Reference Profiles

The methodological features of the basic software and methodological tool application for processing and interpreting systems in order to solve the problems of predicting the geological and geophysical parameters that are most important for prospecting and industrial exploration are described above in Chaps. 1–7. As an example of their application on the materials of the considered sites for a composite profile passing through vertical well sections (200, 272, 271, 201, and 160), optimized sections obtained after applying a special graph for processing and predicting the main set of geological indicators are presented (Fig. 8.29a–f). In the interpretation process, the studied reflectors of productive and prospective sediments of the Lower and Middle Carboniferous and Middle and Upper Devonian were correlated on the sections of the AI and RC. In accordance with their lithological-stratigraphic confinement, they were assigned indices [33] after the procedure of binding these boundaries to the corresponding geological boundaries. The use of seismic data preprocessing using a special graph made it possible to obtain time 3D cubes of the wave field (WF) that are optimized for maintaining the dynamics of seismic record and cubes of effective acoustic impedance (AI) and reflectivity (RC). Using these data and the results of processing GBS data for almost all wells, a direct prediction of the desired geological indicators in the target sediments was carried out. To control the results of the prediction, let us dwell on the obtained trace-by-trace prediction of the lithological composition—clay content

Fig. 8.29 Seismic section for the sediments of the Lower and Middle Carboniferous and the Middle and Upper Devonian along the line passing through the wells: the input time section (a), effective acoustic impedance (b), the results of clayiness prediction (c), sandiness (d), porosity (e), oil and gas saturation (f)

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

Fig. 8.29 (continued)

339

340

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

(Fig. 8.29c), sandiness (Fig. 8.29d), porosity (Fig. 8.29e), and oil saturation (Fig. 8.29f). The prediction results show that the clayization of the section is most pronounced in the terrigenous formations (in Fig. 8.29c, a dark-green-yellow palette). Among the entire spectrum of the interbedded horizons and layers, the greatest clay formation is observed in the intervals of deposits occurrence of Vereiskian (C2vr), Bobrikov (C2bb), and the terrigenous Devonian strata: Sargay (D3sr), Kynov (D3kn), Pashiisk (D3psh), Mullin (D2ml), Ardatov (D2ar), and Vorobiov (D2vb) horizons, as well as in the sediments of the Bavlin series of the Proterozoic (PR2bv). The content of clay material here ranges from 40 to 95%. As is known, clay formations, on the one hand, have the maximum ability to save sedimentation signs (they prevent vigorous flow of postsedimentation processes due to their relatively low permeability), and on the other hand, their shielding properties control the formation, preservation, size, location, and phase composition of hydrocarbons. At the same time, the intervals of the section, represented by carbonate sediments, are characterized by significantly smaller clay values. The sandiness determines the placement of reservoir development zones in the appropriate sedimentation conditions (Fig. 8.29d). By the nature of the porosity distribution Fig. 8.29e) over the entire interval of the sediments studied, the most porous intervals are those of the section, in which there is also an increased content of sandiness. These are layers of sandstones and siltstones in sediments of predominantly coastal-marine and continental genesis (C2vr, C1bb, D3kn, D3psh, and weathering crust (w.c.) of the crystalline basement). A feature of these deposits is that almost throughout the section, their porosity is characterized by an uneven distribution along the lateral direction. The average values of porosity for deposits of these horizons are characterized by values of 8–18% (Fig. 8.29e). By the nature of the predicted porosity distribution as a whole over the entire studied interval of productive sediments, the most porous intervals are the terrigenous sections associated with the Kynov (D3kn), Pashiisk (D3psh), and Ardatov (D2ar) deposits. The specified intervals of productive sediments with fairly good reservoir properties are filled with fluids—formation water and hydrocarbons: oil + gas (Fig. 8.29f). The most significant oil-saturated areas of the studied productive and prospective strata are directly related to these intervals. At the same time, the most representative areas (in terms of oil saturation and lateral traceability) of the studied section appear in the occurrence interval of the C2vr, C2b, C1bb, C1t, D3kn, D3psh, and D2ar horizons, as well as in part D2ml and D2vb. They are more clearly distinguished on local arched sections, as well as on the slopes of anticlinal structures, where there is an increase in oil-saturated thicknesses and reservoir properties (Fig. 8.29c). In general, for these deposits, the oil saturation is intermittent in the section. The average values of oil saturation vary widely—5–20% (Fig. 8.29f).

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

8.3.3

341

The Results of Structural Constructions

For the study area, the necessary structural constructions were carried out for the main litho-stratigraphic subdivisions of the sedimentary complex. In particular, the maps of isochrones, structural maps, and interval time maps for the surfaces of almost all the target intervals of the section, starting with the deposits of the Vereiskian horizon (C2vr), Bashkirian stage (C2b), etc. and ending with the surface of the crystalline basement (AR), have been constructed [27]. As an example, we consider a small part of these construction results. For the deposits of the Pashiisk horizon (D3psh), the tectonic structure is displayed on a structural map (Fig. 8.30, a), from which the structure of potential petroleum reservoirs in the sediments of one of the main productive terrigenous Devonian horizons is visible. In its sediments, in the presence of reservoir development zones and overlying their layers of fluid seals, in principle, it is possible to have additional potential oil and gas objects, since its oil-bearing capacity is proved according to the testing of wells located in the adjacent territory. The deposits of the horizon are known to be composed of sandstones, siltstones, transitional in composition (sandstone-aleurolite or aleurolite-sandstone), sometimes gravelites, thin layers of clay, marl, and even less often limestone. Light gray to white sandstones and quartz, usually friable, porous, and fine-grained (sometimes in interlayers medium-grained), with clayey cement, are well sorted or silty. Reservoirs here are fine- and medium-grained aleurolites and sandstones with inclusions of plant residues that are found in the well sections (Fig. 8.28). The cover screen for the Pashiisk reservoirs are the clay formations of the Kynov horizon and the Domanik-type limestone of the Sargay horizon. In general, the tectonic structure of the Pashiisk horizon retains the features of the inheritance of the lower-lying terrigenous Devonian sedimentary formations and the crystalline basement. Structural zones of uplifts in the whole Pashiisk horizon are delineated: isohyptic 1870 m (in the area of the well location 200 elevation amplitude 12.5 m); iso-gypsum 1852.5 m (in the area of the location of the well 201 lifting amplitude 5 m); and iso-gypsum 1785 m (in the area of the location of the well 160 amplitude of lifting 10 m) (Fig. 8.30a). Along with the marked anticline structures, there are a number of positive isometric structures in the area. One of the most representative of them is located in the southwest of well 160. It is contoured with an iso-gypsum of 1792.5 m and has an amplitude of 20 m. On the distribution of equal level outlines Δt(x, y) ¼ const on the isopach map— interval times between the reflectors D3kn and D3psh (Fig. 8.30b), changing on the work site within 27  15 ms. It is clear that the basin area in Devonian Lower Frasnian age history was characterized by a turbulent tectonic regime. As a result, zones (local areas) have accumulated here with both relatively low Δt(x,y) values (within 12–27 ms) and anomalously high Δt(x,y) values (between 27 and 42 ms). Anomalies with the most increased values of Δt(x,y) are located in the southwestern half of the work area. In general, the nature of the anomalies is due to the presence of

342

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.30 Mapping for deposits: structural along the top Pashiisk horizon (D3psh) (a), time interval between the reflectors D3psh and D2ar (b), structural for the surface of the crystalline basement (AR) (c)

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

343

Fig. 8.30 (continued)

accumulative sandy bodies, as well as sections of the sanding section associated with sand bars or fans. For the top of the crystalline basement deposits, the structural map is presented in Fig. 8.30c. The behavior of this surface, as well as on the results of previous structural constructions, is distinguished by the considerable complexity of the tectonic structure. The complex system of tectonic faults introduces a block character into the structural form of the surface under consideration. The crystalline basement formations are largely dislocated, crushed, and deformed into flexo-, horstone, and graben-like folds, fragmented by deep faults (including small amplitude), which split the rocks in different directions. To identify the features of the such disturbance distribution, the authors used fault projections, confidently distinguished on the reference lines (along the typical CDP traverse profiles) of the 3D cube of effective acoustic impedance and the cube of reflectivity and the cube coherence map of the wave field amplitudes along the paleoisochronic surface of the crystalline basement and the distribution of isolines of the t0(x,y) level—an isochrone map—on which flexure-like zones are relatively well traced (areas of thickening isoline) with different structural features of the real medium. Taking into account these data, a complex system of interrelated tectonic faults was formed, inheriting and uniting along with large fault tectonic ruptures and shear displacements of a smaller scale and amplitudes. It is quite obvious that both through

344

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

tectonic faults (originating within the crystalline basement) and rootless faults may be reflected here. The most elevated part of the AR horizon is located within the northeastern part of the area. There are several local elevations of isometric and elongated shape in the plan. These uplifts can be contoured with iso-gypsum (1900  1910.5 m) and their amplitude varies from 10 to 35 m (Fig. 8.30c). Within the southwestern half of the work area (in the submerged part of the site along the surface of AR), two local positive structures stand out. One of them (with relatively small linear dimensions) is located in the vicinity of the well 201; the other (larger) is about 1.9 km to the northeast of the wellhead 201. The amplitude of the first is 5 m, and the second is  15 m.

8.3.4

Volume Structural Tectonic Model of the Real Medium

Structural maps along the surfaces of the main lithologic and stratigraphic subdivisions of the Lower and Middle Carboniferous and Middle and Upper Devonian, as well as formations of the crystalline basement, were used to construct a 3D structural tectonic model of the real medium under study (Fig. 8.31). The resulting model in axonometric projection characterizes the mutual arrangement of structural surfaces, allows you to see the relationship of tectonic dislocations at different depths, and evaluates the growth dynamics of the amplitudes of positive local structures and tectonic faults. On this model, one can clearly see the coherency of different intervals of the sedimentary complex with the structural features of the basement. Flexural complications in the sediments of the Carboniferous and Devonian sedimentary complexes correspond to faults in the basement, along which the crystalline base blocks movement. The maximum amplitudes of faults along the vertical are observed along the deepest horizon AR and the minimum in the sediments of the horizons D2ml, D2ar, and D2vb (Fig. 8.31). The amplitudes and magnitudes of anticlinal and positive tectonically screened structures increase with depth (this can be seen from the presented 3D model and the ranges of changes in absolute depths shown on the respective depth scales for each of the structural surfaces). It should be noted that the depths of the surfaces in almost all horizons (from C2k to AR) were controlled by data on the deep wells available in the work area. The most illuminated representative in terms of structural forms informativeness are elevated sections of the research area—they are associated with the part of the area that was drilled by deep wells. Insufficiently illuminated drilling is a section of a sedimentary complex in its depressed areas, where various type nonstructural traps can be formed due to the sharp facies zonality of the litho-stratigraphic subdivisions of the complex in areas not compensated by sedimentation of palaeodepressions.

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

345

Fig. 8.31 Three-dimensional image of structural maps along the horizons of the sedimentary cover and the surface of the crystal basement

8.3.5

Area Prognosis: Geological and Geophysical Indicators in the Sediments of the Kynov Horizon

For the deposits of the Kynov horizon (D3kn) of the lower substage of the Frasnian stage (D3fr), the prediction results are represented by a set of geological and

346

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

geophysical indicators in the form of corresponding value distribution maps (Fig. 8.32): effective acoustic impedance (a), oil and gas saturation (b), coherence of the wave field amplitude cube along the paleoisochronous surface of the D3kn horizon (c), porosity (e), oil-saturated thickness (f), and distribution density of hydrocarbons (g). A structural map of the sediments top with oil and gas contours (d) is also given. On the basis of taking into account the specific type of reservoir in the sediments under consideration and the structure of their pore space, the level of the mobile part of hydrocarbons was set to 5% and higher. Accordingly, the level for the fixed part of hydrocarbons is also taken at 5% and below (at this level, significant oil saturated areas and the desired oil and gas objects are directly covered). As a result, contours with possible signs of oil saturation were identified (in Fig. 8.32b, they are shown in red closed lines with berg strokes). Both relatively large areas with an average value of oil saturation and small-sized contours, which can be connected or not connected with each other by the lowest level of oil saturation, were identified. On the distribution map of the mean values of effective acoustic impedance (AI), the parameter in question generally has a mosaic and uneven pattern of distribution over the area (Fig. 8.32a). Here a large number of relatively small anomalies of increased AI values alternate with the same kind of decreased AI anomalies. There is a tendency of a greater concentration of the latter in the southeastern part of the work area. Under the conditions of terrigenous-argillaceous rock occurrence in the horizon sediments, it can be assumed that, within the limits of the plots of increased values, the effective acoustic impedance (AI) is the most sandy section of the D3kn horizon sediments. Plots of the area with lower AI values are found to be confined to the most clayey intervals of the studied section. Such a pattern in the distribution of low and high values of effective acoustic impedance corresponds to the existing ideas about the distribution of geological (including acoustic) heterogeneities in this part of the section. The average values of the oil and gas saturation of the considered horizon deposits D3kn (Fig. 8.32, b) are generally high. Identified local areas of oil and gas saturation are characterized by average values of oil and gas saturation, reaching 7–9% of volume of the reservoir D3kn. In general, the bulk of oil saturation anomalies in the area is confined to the northeast half of the work area. At the same time, in the southwestern part of the region, the anomalies of oil saturation are much smaller in their linear dimensions, and, moreover, they can be traced separately, as hydrodynamically unrelated to each other. On the coherence map of the wave field amplitude cube along the paleoisochronous surface of the D3kn horizon (Fig. 8.32, c), combined with the oil saturation contour, a certain zonality is observed in the distribution of increased irregularity (darker spots) and increased regularity (areas of light colored areas) over the area seismic record. Here, there is some correlation between the coherence anomalies of the seismic record and the oil saturation contours. A distinctive feature of the seismic recording coherence cube anomalies is that the conditions of increased record regularity (areas of the lightest color) are the most oil-saturated.

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

347

Fig. 8.32 The maps for the Kynov horizon deposits (D3kn): effective acoustic impedance (a), oil and gas saturation (b), the coherence of the wave field cube amplitudes along paleoisochronous surface (c), structural map along the sediment top with contours of oil and gas saturation (d), porosity (e), oil-saturated thickness (f), hydrocarbon distribution density (g)

348

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.32 (continued)

Figure 8.32d provides a comparison of the constructed structural map along the top of the Kynov horizon (D3kn) with the oil saturation contours. It is plainly seen from this figure that the oil saturation zones within the northeastern part of the area are mainly associated with uplifts—the anticlinal structures of the Kynov deposits. Separate sections of oil saturation zones are confined to the slopes of the uplifts. In general, for the deposits in question, the most probable is the presence of lithologically screened oil objects. The distribution of oil saturation along the research area is associated with the distribution of porosity (Fig. 8.32e). The oil saturation contours in the sediments of the D3kn horizon almost completely fit into areas with high porosity values. Within the limits of oil saturation contours, reservoir beds are characterized by average values of porosity equal to 12–18%. In those areas, which are characterized by relatively low values of porosity (10–13%), the contours of oil saturation are practically absent. A map of predicted oil- and gas-saturated sediment thickness of the D3kn horizon with superimposed contours of average oil saturation values is presented in Fig. 8.32f. The highest values of the considered parameter are observed in areas with high oil saturation. The largest values of oil-saturated thickness are fixed within the local contours (zones) of the most significant oil saturation. This is mainly the northeast half of the research area. In these zones, the values of oil-saturated thickness vary in the range of 3–12 m. Previously, the determination of the oil portion—hydrocarbons—(without a fraction of lithology and water saturation) in linear units of measure [1, 25, 31] was substantiated in sufficient detail. This procedure of normalization and recalculation of the geological substance components volume content at the depth scale is implemented as an algorithm and a program, and based on their use, the maps of the predicted distribution density of hydrocarbons (oil) for the D3kn horizon (Fig. 8.32g) are obtained. For the deposits of the D3kn horizon, the map of the predicted distribution density of hydrocarbons is presented in accordance with the given scale of the thickness of

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

349

the oil reservoir; the band of its change is in the range of 0.0–1.5 m (with the height of the walls of a single curvilinear cylinder (reservoir) equal to 0.1 m). As can be seen from these results, the most predicted distribution density of hydrocarbon raw materials is characterized by the parts of the area located within the northeast part. Here, the largest number of areas (zones) with maxima of the anomalies of the predicted HC distribution density is fixed (Fig. 8.32g). The size, configuration, and intensity of these abnormal areas are different. The largest of them are confined to the area of the southeastern border of the area.

8.3.6

Area Prognosis: Geological and Geophysical Indicators in the Sediments of the Pashiisk Horizon

For the deposits of the Pashiisk horizon D3psh of the lower substage of the Frasnian stage (D3fr), the prediction results are represented by a set of areal geological and geophysical indicators in Fig. 8.33a–f. These figures show the distributions of predicted parameters by area: effective acoustic impedance (a), average oil and gas saturation (b), cube coherence of the wave field amplitudes along the paleoisochronous surface of the D3psh horizon (c), porosity (e), and hydrocarbon distribution density (f). The structural map along the top of deposits with oil and gas contours (d) is also considered. On the distribution map of effective acoustic impedance (AI) mean values, the parameter in question generally has an uneven distribution pattern over the area (Fig. 8.33a). Anomalies with the highest AI values are located mainly in the northeast half of the territory. Anomalies of AI values with medium intensity are confidently traced in the southwestern part of the area. Since terrigenous-clayey rocks mainly lie in the Pashiisk deposits, it is quite obvious that the high areas of effective acoustic impedance (AI) more closely correspond to the sand fraction of the studied section of the D3psh horizon. Plots of area with the distribution of low AI values are found to be confined to the most clayey intervals of the studied section. The average values of oil and gas saturation of the considered horizon D3psh (Fig. 8.33b) are generally relatively high. Identified local areas of oil and gas saturation are characterized by average values of oil and gas saturation in the volume of the reservoir D3psh, reaching values of 8–12%. The main volume of oil saturation anomalies in the area is confined to the northeast half of the work area. At the same time, in the southwestern part of the area, the anomalies of oil saturation are much smaller in their linear dimensions. In addition, they are traced in the southern part of the southwestern half of the area separately and hydrodynamically unrelated to each other. On the coherence map of the wave field amplitude cube along the paleoisochronous surface of the Pashiisk horizon D3psh (Fig. 8.33c), combined with the contour of oil saturation, there is a well-defined zoning in the area distribution of increased irregularity (spots of darker color) and increased regularity (areas

350

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.33 The maps for the Pashiisk horizon deposits (D3psh): effective acoustic impedance (a), oil and gas saturation (b), the coherence of the wave field cube amplitudes along paleoisochronous surface (c), structural map along the sediment top with contours of oil and gas saturation (d), porosity (e), hydrocarbon distribution density (f)

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

351

of light colored) of the seismic record. There is also a correlation between the coherence anomalies of the seismic record and the oil saturation contours. This is especially evident in the northeast half of the work site. Approximately the same picture is observed in the southernmost part of the site. Areas of increased record regularity (parts with the lightest color) are the most oil-saturated. Figure 8.33d gives a comparison of the constructed structural map along top of the Pashiisk horizon (D3psh) with oil saturation contours. It can be seen from the picture presented that the oil saturation zones within the northeastern part of the area are mainly associated with uplifts, the anticlinal structures of the Pashiisk sediments. Separate sections of oil saturation zones are confined to the slopes of the uplifts. In general, for the deposits in question, the most probable is the presence of lithologically shielded oil objects. The distribution of oil saturation by research area is also associated with the porosity distribution (Fig. 8.33e). At the same time, the oil saturation contours in the D3psh horizon deposits almost completely fit into the areas with high porosity. Within the limits of oil saturation contours, reservoir beds are characterized by average values of porosity equal to 13–18%. On those areas, which are characterized by relatively low values of porosity (10–13%), the contours of oil saturation, as a rule, are absent. The map of the predicted distribution density of hydrocarbons over the Pashiisk deposits is presented in Fig. 8.33f. In accordance with the given scale of the oil reservoir thickness, the range of its change is within 0.0–1.8 m (with a wall height of a single curvilinear cylinder—a reservoir of 0.2 m). The overall situation with the predicted hydrocarbon density distribution in the deposits of the Pashiisk horizon is in many respects similar to that for the sediments of the above-examined Kynov horizon. Here in the Pashiisk deposits, the main volume of anomalous areas is also concentrated in the northeastern part of the area, as well as near its southeastern boundary. It should be noted that almost all the predicted anomalies are located outside the vertical intervals of the well sections 200, 201, and 160.

8.3.7

Area Prognosis: Geological and Geophysical Indicators in the Sediments of the Ardatov Horizon

For the Ardatov horizon D2ar deposits of the Givetian stage (D2gv), the prediction results are represented by a set of geological and geophysical indicators (Fig. 8.34a– d). Distributions of the predicted parameters are given: effective acoustic impedance (a), average oil and gas saturation (b), cube coherence of the wave field amplitudes along the paleoisochronous surface of the D2ar horizon (c), and porosity (d). On the distribution map of the mean values of the effective acoustic impedance (AI), the parameter in discussion is generally distributed rather unevenly over the area (Fig. 8.34a). Anomalies with the highest AI values are located mainly in the

352

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.34 The maps for the Ardatov horizon deposits (D2ar): effective acoustic impedance (a), oil and gas saturation (b), coherence of the wave field cube amplitudes along paleoisochronous surface (c), porosity (d)

northeast half of the area. There is also a certain zonality of the distribution of AI values. The rest of the territory is characterized by relatively low values of effective acoustic impedance, including the southwestern part of the area. It is quite natural that more sandy fractions of the studied section of the D2ar horizon will correspond to areas of high values of effective acoustic impedance (AI). Plots of the zone with the distribution of low AI values are found to be confined to the most clayey intervals of the studied section. Average values of oil and gas saturation of the Ardatov D2ar horizon (Fig. 8.34b) are generally relatively high. Identified local areas (zones) of oil and gas saturation are characterized by average values in the volume of the reservoir D2ar, reaching 8–11%. The main volume of oil saturation anomalies in the region is confined to the northeast half of the work area. In the southwestern part of the area, the anomalies of oil saturation are practically absent.

8.3 Geological Indicator Estimation in Productive and Prospective Middle. . .

353

On the coherence map of the wave field amplitude cube along the paleoisochronous surface of the Ardatov horizon D2ar (Fig. 8.34c), combined with the contour of oil saturation, a definite zonality is observed in the distribution of increased irregularity sites (spots of darker color) and regularity (areas of lightcolored areas) of the seismic record. In the northeastern half of the work area, there is a certain correlation between the coherence anomalies of the seismic record and the oil saturation contours. In the southern part of the area, oil saturation anomalies of practical interest are not traceable. The distribution of oil saturation along research area as a whole is related to the porosity one (Fig. 8.34d). At the same time, the oil saturation contours in the D2ar horizon deposits almost completely fit into the areas with increased porosity values. Within the limits of oil saturation contours, reservoir beds are characterized by average values of porosity equal to 10–16% (this is mainly the northeast part of the region). In the rest of the area, as can be seen from Fig. 8.34d, changes in porosity are observed for areas of oil saturation and areas with no oil saturation (here the range of porosity change is within 2–16%). Obviously, those areas that are located outside the oil saturation contours are water-saturated. The anomalies obtained in this way were taken into account directly when determining promising points and designing the location of recommended wells for opening up oil prospective objects in the work area.

8.3.8

Prospective Areas for Optimal Opening of Predicted Oil-Saturated Objects

For practical purposes, it is important to jointly assess the oil saturation contours, which gives an idea of the spread of prospective (productive and prospective) areas for sediments of a number of horizons discussed above. Such an assessment can be performed at the final stage of research based on a comprehensive analysis of various geological indicators extracted from seismic data. At this research stage, the authors limited themselves to a comparative analysis of predicting various geological indicators results for different horizons (layers) by combining their oil saturation contours and a comprehensive analysis of the predicted geological and geophysical parameters discussed above (with this analysis, naturally, all obtained prediction information is used for target intervals). On this basis, in the first approximation, optimal areas are outlined for further prospecting. The results of combining the contours of significant oil saturation for the productive Kynov D3kn, Pashiisk D3psh, and Ardatov D2ar horizons of the terrigenous Devonian stratum are presented in Fig. 8.35. Here, along the set of all the predicted geological indicators considered above within the combined oil saturation contours along these horizons, a list of locations of recommended wells is also given based on

354

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

Fig. 8.35 Significant hydrocarbon saturation outline map for reservoir formations of Kynov (D3kn), Pashiisk (D3psh), and Ardatov (D2ar) horizons

the determination of representative of the most promising areas over the entire research region. For the immediate opening of oil and gas deposits in the sediments of these horizons, only within the northeastern part of the area (the search area) are designated with prospective points with numbers 6, 7, 8, 11, and 13. Outside this area there are established perspective points with numbers 9, 10, 12, and 14. Perspective points with numbers 1–5 were already outlined by the authors earlier when considering similar prediction indicators for the deposits of the Vereiskian horizon (C2vr) and Bashkirian stage (C2b), Bobrikov suite (C1bb), and Tournaisian stage (C1t) [28, 29]. Practically for each of the oil prospective objects in the area of promising sections (prospective points), as noted above, overlapping of oil saturation contours with a higher distribution density of hydrocarbons is observed. Each of these points is determined based on the most optimal combination of predictive geological indicators. In determining promising lithological-screened objects, first of all, the maximum ratio of average values of oil and gas saturation, porosity, oil-saturated

8.4 Summary

355

thickness, etc., as well as hydrocarbon resources, were taken into account. For oil prospective objects of structural type (arched deposits) in the study area (among them, the most characteristic are deposits of the Lower and Middle Carboniferous [28, 29]), in addition to the predicted geological and geophysical parameters, the structural factor was also fully taken into consideration. Summarizing the results obtained, it should be emphasized that among the entire set of oil and gas contours (irregular and especially isometric form in the plan), presented in Fig. 8.35, the selected nine perspective points mentioned above are presented as first priority. In addition to these points (sites), there are still a number of oil and gas saturation contours, which may also be of practical interest for prospecting.

8.4

Summary

For the Volga-Ural oil and gas province, the results obtained in the southeastern part of the Republic of Tatarstan and the Orenburg and Samara regions are presented. On the western slope of the South Tatar Arch, high-resolution seismic data were used to obtain prediction results in the form of trace-by-trace values of clay content, porosity, oil saturation, and forecast lithological-stratigraphic columns with a fluidtype distribution in specified vertical sections. The maps of oil saturation, porosity, and oil-saturated thicknesses along these horizons show the contours of the moveable and stationary parts of hydrocarbons with the identification of the most significant oil saturation areas. It is recommended to conduct more extensive research using the HRS-Geo technology in order to solve the subtle and complex problems of petroleum geology in the study area. Positive results of the prediction of the lithological composition, reservoir properties, and the nature and degree of fluid saturation of reservoirs were obtained on the territory of the Orenburg region. The repeated use of the well bore 126 in the form of an inclined well—its deviation in the north-east direction—provided an influx of hydrocarbons from the deposits with a flow rate of 30 t/day at a bottom-hole pressure of 144 atm. Repeated testing of well 125 in the range of deposits of the Koiven horizon in the depth range recommended by the authors gave a positive result—an influx of hydrocarbons with an increased flow rate. For all target horizons in the area of the Samara region, prediction geological and geophysical indicators were formed, confirmed in the process of further prospecting. The maximum ratio of the average values of oil and gas saturation, porosity, and oil-saturated thicknesses, as well as the estimation of hydrocarbon resources, was taken into account when identifying promising objects of lithologically shielded and structural-lithological types.

356

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

References 1. Trofimov, V., Khaziev, F., & Trofimova, A. (2018). Tekhnologiya VRS-Geo. Izucheniye nefteperspektivnykh obyektov metodom vysokorazreshayushchey seysmiki (HRS-Geo technology. Study of oil-prospective objects by the method of highresolution seismic). Oil & Gas Journal Russia, 1–2(123), 28–35. 2. Safonov, A. S., Kondrat'yeva, O. O., & Fedotova, O. V. (2011). Poisk neantiklinal'nykh lovushek uglevodorodov metodami seysmorazvedki (Search for non-anticlinal hydrocarbon traps using seismic exploration methods). M., Nauchnyy mir. p. 512. 3. Khanin, A. A. (1973). Porody-kollektory nefti i gaza neftegazonosnykh provintsiy SSSR (Oil and gas reservoir rocks of the USSR oil and gas provinces) (p. 304). Nedra. 4. Muslimov, R., Nenarokov Yu, S., & Khalabuda, E. P. (1990). i dr. Metodicheskiye priyemy poiskov slozhnopostroyennykh lovushek v Tatarii (Methodological techniques of searching for complex traps in Tatarstan). Collection: Metodika poiskov i razvedki neftegazonosnykh ob"yektov netraditsionnogo tipa (Methods for prospecting and exploration of oil and gas objects of unconventional type). M., Nauka. pp. 117–130. 5. Fayzullin, L. D., Pavlov, M. V., Valeyev, R. N., et al. (1974). Dizyunktivnyye narusheniya kristallicheskogo fundamenta i ikh otrazheniye v osadochnoy tolshche Tatarii (Disjunctive faults of the crystalline basement and their reflection in the sedimentary strata of Tataria). Geologiya nefti i gaza (Geology of oil and gas), 3, 34–37. 6. Kaydalov, V. I., Konovalov, V. V., & Shenderovich, D. M. (1982). Izucheniye paleorel'yefa dosrednedevonskoy poverkhnosti Orenburgskoy oblasti s tsel'yu vyyavleniya zon rasprostraneniya lovushek neantiklinal'nogo tipa (Paleorelief study of the pre- Middle Devonian surface of the Orenburg region in order to identify the zones of non-anticlinal traps distribution). Geologiya nefti i gaza (Geology of oil and gas), 2, 28–32. 7. Nikolishin Ye, D. (1998). Osobennosti razmeshcheniya i metodika poiskov zalezhey nefti v zonakh razvitiya devonskikh grabenooobraznykh progibov Tatarstana (Peculiarities of placement and methods of prospecting for oil deposits in the development zones of the Devonian graben-like troughs of Tatarstan). Collection: Opyt razvedki i razrabotki Romashkinskogo i drugikh krupnykh neftyanykh mestorozhdeniy Volgo-Kamskogo regiona (Experience in exploration and development of Romashkino and other large oil fields in the Volga-Kama region). Trudy nauch.-prakt. konfer., posvyashchennoy 50-letiyu otkrytiya devonskoy nefti Romashkinskogo mestorozhdeniya, Leninogorsk (Proceedings of the scientific-practical conference dedicated to the 50th anniversary of the Devonian oil of the Romashkinskoye field discovery, Leninogorsk). Kazan', “Novoye Znaniye”. pp. 373–380. 8. Tikhomirov, S. V. (1995). Etapy osadkonakopleniya devona Russkoy platformy i obshchiye voprosy razvitiya i stroyeniya stratisfery (Devonian sedimentation stages of the Russian Platform and general issues of development and structure of the stratosphere) (p. 446). Nedra. 9. Shpil'man, V. I. (1982). Kolichestvennyy prognoz neftegazonosnosti (Quantitative prediction of oil and gas content). (p. 215). Nedra. 10. Ivanov, A. M. (1976). Kompleksnoye izucheniye karbonatnykh porod v kollektorakh nefti i gaza (Comprehensive study of carbonate rocks in oil and gas reservoirs). Nedra. 11. Zhukov, I. M. (1980). Ispol'zovaniye neftegazokontroliruyushchey roli nekotorykh tolshch v kachestve osnovy povysheniya effektivnosti poiskov nefti i gaza (Using the oil and gas control role of certain strata as the basis for increasing the efficiency of oil and gas exploration). Geologiya nefti i gaza (Geology of oil and gas), 10, 42–45. 12. Zimin Yu, G. (1981). Lovushki neantiklinal'nogo tipa nizhnekamennougol'noy terrigennoy tolshchi Mukhanovo- Yerokhovskogo progiba (The non-anticlinal type traps of the Lower Carboniferous terrigenous strata of the Mukhanovo- Erokhovsky trough). Geologiya nefti i gaza (Geology of oil and gas), 6, 20–24. 13. Guseynov, A. A., Kaleda, G. A., Samvelov, R. G., et al. (1978). Litologicheskiye, stratigraficheskiye i kombinirovannyye lovushki nefti i gaza (Lithological, stratigraphic and combined oil and gas traps). Nedra.

References

357

14. Khachatryan, R. O., Blyumentsvayg, V. I., & Khludnev, V. F. (1988). Povysheniye effektivnosti seysmorazvedki pri poiskakh skopleniy UV v Volgo-Ural'skoy provintsii (Improving the efficiency of seismic prospecting in the search for hydrocarbon accumulations in the Volga-Ural province). Geologiya nefti i gaza (Geology of oil and gas), 11, 41–46. 15. Romm Ye, S. (1985). Strukturnyye modeli porovogo prostranstva gornykh porod (Structural models of the rocks pore space) (p. 240). L. Nedra. 16. Badamshin, E. Z., Batyrbayeva, A. A., Lebedev, N. P., et al. (1997). Geologicheskiye predposylki poiskov rukavoobraznykh uglevodorodnykh zalezhey v Srednem Povolzh'ye (Geological prerequisites for prospecting for brook-like hydrocarbon deposits in the Middle Volga region). Geologiya nefti i gaza (Geology of oil and gas), 8, 20–25. 17. Cheredov, G. M. (1997). Zakonomernosti strukturoobrazovaniya i razmeshcheniya neftyanykh zalezhey na zapadnom sklone Yuzhno-Tatarskogo svoda. Geologiya nefti i gaza (Regularities of structure formation and placement of oil deposits on the western slope of the South Tatar arch). № 6. pp. 22–27. 18. Milashin, V. A., Trofimov, V. L., Khaziev, F. F., & Pisetskiy, V. B. (2002). Obnaruzheniye slozhnykh nefteperspektivnykh obyektov v otlozheniyakh terrigennogo devona na yugovostoke Tatarstana metodami vysokorazreshayushchey seysmiki (Detection of complex oilperspective objects in the terrigenous Devonian sediments in the southeast of Tatarstan by highresolution seismic methods). Geofizika (Geophysics), 2, 11–18. 19. Kuznetsov, V. G. (1992). Prirodnyye rezervuary nefti i gaza karbonatnykh otlozheniy (Natural oil and gas reservoirs of carbonate deposits) (p. 241). Nedra. 20. Leonova Ye, A. (1998). Osnovnyye napravleniya poiska lovushek netraditsionnogo tipa v devonskikh otlozheniyakh Orenburgskoy oblasti. Geologiya nefti i gaza (The main search directions for traps of unconventional type in the Devonian deposits of the Orenburg region). № 6. pp. 27–33. 21. Zalyayev, N. Z. (1981). Kompleksnaya interpretatsiya geofizicheskikh parametrov funktsional'nymi preobrazovaniyami s pomoshch'yu EVM (Complex interpretation of geophysical parameters by functional transformations using a computer) (p. 150). BelNIGRI. 22. Zalyayev, N. Z. (1990). Metodika avtomatizirovannoy interpretatsii geofizicheskikh issledovaniy skvazhin (Technique of automated interpretation of well logging). Minsk. University Publishing House. p. 142. 23. Trofimov, V. L., Khaziev, F. F., Milashin, V. A., et al. (2007). Avtomatizirovannaya obrabotka i interpretatsiya dannykh GIS dlya obnaruzheniya nefteperspektivnykh obyektov metodami vysokorazreshayushchey seysmiki (Automated processing and interpretation of well logging data for the detection of oil-prospective objects by high-resolution seismic methods). Tekhnologii seysmorazvedki, 2, 54–66. 24. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., et al. (2009). Spetsial'naya obrabotka i interpretatsiya dannykh seysmicheskikh nablyudeniy v slozhnykh geologicheskikh usloviyakh metodom vyokorazreshayushchey seysmiki (Special processing and interpretation of seismic observation data in difficult geological conditions by the method of high-resolution seismics). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 36–50. 25. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., & Mal'tsev, G. A. (2007). Detal'naya otsenka geologicheskikh pokazateley real'noy sredy s primeneniyem tekhnologii vysokorazreshayushchey seysmiki (Detailed assessment of geological indicators of the real medium using high-resolution seismic technology). Geofizika (Geophysics), 4, 157–166. 26. Khaziev, F. F., Trofimov, V. L., Milashin, V. A., & Mal'tsev, G. A. (2007). Perspektivy optimal'nogo razmeshcheniya geologorazvedochnykh rabot s primeneniyem seysmicheskikh dannykh vysokogo razresheniya (Prospects for the optimal location of exploration works using high-resolution seismic data). Geofizika (Geophysics), 4, 167–175. 27. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., & Ponamarev, S. A. (2009). Vozmozhnosti kolichestvennogo prognoza geologicheskikh pokazateley metodami vysokorazreshayushchey seysmiki (Possibilities of quantitative prediction of geological indicators by high-resolution seismic methods). Neft'. Gaz. Novatsii (Oil Gas Innovations), 2, 11–26.

358

8 Detailed Interpretation of High-Resolution Seismic Data in the Volga-Ural. . .

28. Milashin, V. A., Trofimov, V. L., Khaziev, F. F., & Ponamarev, S. A. (2009). Prognozirovaniye geologicheskikh pokazateley v produktivnykh gorizontakh srednego karbona metodom vysokorazreshayushchey seysmiki (Prediction of geological indicators in productive horizons of the middle carboniferous by the method of high-resolution seismic). Neft'. Gaz. Novatsii (Oil. Gas. Innovations), 3, 6–15. 29. Khaziev, F. F., Trofimov, V. L., Milashin, V. A., & Ponamarev, S. A. (2009). Detal'naya otsenka geologicheskikh pokazateley produktivnykh gorizontov nizhnego karbona po seysmicheskim dannym vysokogo razresheniya (Detailed assessment of productive horizons geological indicators of the lower carboniferous from high-resolution seismic data). Neft'. Gaz. Novatsii (Oil Gas Innovations), 8, 12–23. 30. Trofimov, V. L., Khaziev, F. F., & Shkol'nik, S. A. (2014). Sovershenstvovaniye metodiki prognozirovaniya geologicheskikh pokazateley metodom vysokorazreshayushchey seysmiki (Improvement of the methodology for predicting geological indicators by the method of highresolution seismic). Ekspozitsiya Neft' Gaz (Oil and Gas Exposition), 6(38), 13–19. 31. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2008). Opredeleniye geologogeofizicheskikh parametrov real'noy sredy metodom vysokorazreshayushchey seysmiki (Determination of geological and geophysical parameters of the real medium by the high-resolution seismic method). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 25–30. 32. Nesterov, I. I., & Shpil'man, V. I. (1987). Teoriya neftegazonakopleniya (Oil and gas accumulation theory) (p. 232). Nedra. 33. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., et al. (2008). Izucheniye stroyeniya i otsenka obstanovok osadkonakopleniya tonkosloistoy real'noy sredy metodami VRS-Geo i GIS (Study of the structure and assessment of sedimentation environments of a thin-layered real medium using HRS-Geo and GBS). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 57–67.

Chapter 9

Examples of HRS-Geo Technology Used in Other Regions

Abstract Examples of the HRS-Geo technology use in the Timan-Pechora province, Western Siberia, and Saudi Arabia are given. Due to the in-depth complex interpretation using the HRS-Geo technology, GBS, and drilling data, a number of new perspective areas in reservoir formations were identified, and the structure of the geological environment, the contours of the oil content, and the oil prospects of the real section were significantly refined. On the territory of the Timan-Pechora province, oil-promising objects of structural, tectonical, and lithological shielded types were identified in the Upper Devonian reservoir formations. The features of the structure and development of buried river systems (paleoriver incisions, boundaries of erosion sides, structures of internal sediment filling of paleochannels, etc.) are studied. High efficiency of the acoustic inhomogeneities’ prediction of the real environment is achieved due to the use of the high-resolution seismics method. On the territory of Western Siberia, oil-prospective objects of structural, tectonical, and lithological shielded types in the reservoir layers of the Jurassic sediment complex and the top part of the Pre-Jurassic basement were studied. The maximum ratio of the average oil saturation, porosity, oil-saturated thicknesses, and oil saturation coefficients, as well as the resources of hydrocarbon raw materials, was taken into account when determining prospective objects of lithological shielded and structural-lithological types. For oil-prospective objects of structural type, in addition to the specified prediction geological and geophysical parameters, the structural factor was also taken into account. An example of solving the problem of petroleum geology on the basis of seismic observations obtained in complex seismic and geological conditions of the Unayzah formation deposits (Saudi Arabia) is given. Here, relatively long and intense oil saturation of the desired sections in the sediments of the Unayzah A, Unayzah B, partially Unayzah A siltstone, and Unayzah C horizons is fairly confidently identified. Here, too, the structural-tectonical factor that controls the preservation of the local accumulation of hydrocarbons is confidently manifested. As a result of further research (after predicting the geological indicators performed by the authors, based on the materials of two seismic profiles), the discovery of a hydrocarbon field with resources of more than 100 million tons of conventional fuel was announced here [www.lukoil-overseas.ru].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2_9

359

360

9 Examples of HRS-Geo Technology Used in Other Regions

The application of the high-resolution seismic method, as noted above, was carried out in various regions of Russia and abroad. In this subsection, in addition to the above results of using HRS-interpretation technology, examples are given with materials of integrated interpretation in three more areas located in the TimanPechora province [1], Western Siberia [2, 3], and Saudi Arabia [4]. Due to the more in-depth integrated interpretation of the HRS-Geo technology, GBS data, and the results of the facies analysis of high-resolution data, a number of new promising areas in reservoirs are revealed; the structure of the geological medium, the oil bearing contours, and the oil prospect of the actual section are significantly clarified.

9.1

Structural-, Tectonical-, and Lithological-Shielded Oil-Perspective Object Identification in Reservoirs of the Upper Devonian (Timan-Pechora Province)

This subsection provides examples of identifying paleoriver incision and filling them in complex geological conditions, which in a seismic wave field can be easily confused with other promising objects that are not related to real geological objects [5]. The problem of studying the development zones of the buried river systems using seismic survey methods has so far been complex. This is despite the fact that from a geological point of view, the schematic diagrams of the river system structure and the sand bodies formed during the spread of water flows in modern and old river valleys are relatively well studied [6–8]. In these works, various settings for sedimentation in paleoriver bed (conditions of their formation and development), issues of buried river system elements recognition (deep erosion manifestations) according to a set of diagnostic signs, emerging differences in the hypersometry of the coastal parts of the paleoriver channels with adjacent sediments, hydrodynamic regimes correlation with tectonic movements activation in the study area, etc. are considered. However, despite the fact that the problem of studying modern river systems has a relatively good geological base and study, the problem of prospecting for the ancient river systems, the study of erosion boards, and the investigation of the filling channels internal structure (incised fluvial valleys structure) within the ancient river paleovalleys using seismic data remain poorly understood. The difficulty of identifying the structural features of incised valleys and their filling elements is due to the fact that when studying “seismic images” of buried river systems one has to deal with interference wave fields, whose resolution does not allow one to judge the structural features of such objects, which are often of oil prospect. As one example of the study of such structures, Fig. 9.1 depicts a productive object that was originally misinterpreted. Here, the 3D seismic wave field anomaly was identified according to the CDP seismic records, which is related to “intrashelf banks” or “single-phase reef formations” objects (i.e., as a result of . . .tracing the

9.1 Structural-, Tectonical-, and Lithological-Shielded Oil-Perspective. . .

361

Fig. 9.1 The nature of the wave pattern in the zone of supposed biohermal formations of Middle Famennian age (a) and their display on the map of maximum positive amplitudes (b)

mid-Famennian bioherm construction using the maximum positive amplitude attribute map). An error in the interpretation of these materials here was obtained for quite objective reasons, as a result of the mapping of a complexly constructed potentially productive object in the structure of a complex wave field, which, as is well known, has the universal manifestation of the reflected wave interference effect. Without special software tools that allow assessing the contributions of various thinlayer acoustic heterogeneities into the structure of an interference seismic wave field, it is difficult to navigate the existing superposition of local seismic responses from each of the target geological heterogeneities [9]. On two vertical sections—CDP time sections along inlines 270 and 250, highlighted features—diagnostic signs of possible bioherm construction on the map of maximum positive amplitudes manifested themselves in the form of an extended carbonate reef formation (Fig. 9.1b). Presented in Fig. 9.1, the anomaly formed near the surface of the upper substage of the Famennian stage sediments (confined to the top of the C–D horizon deposits) was interpreted differently by the authors. Namely, here, on the site of the indicated anomaly of the wave field, an old river incision (paleoriver channel) with coastal contours changing in plan was quite confidently fixed. This result was obtained using the method of high-resolution seismic in the processing and interpretation of 2D sections and 3D cubes of acoustic impedance (AI) and reflectivity (RC). The necessary information on the use of the high-resolution seismic method and the

362

9 Examples of HRS-Geo Technology Used in Other Regions

technique of its application to actual seismic and well data is described in sufficient detail in Chap. 4 and [10].

9.1.1

Structure Features of the Incised Valley According to High-Resolution Seismic Data

The identified perspective objects are associated with carbonate sediments of the Upper Devonian (Figs. 9.2–9.7). They are in contact with the overlying clay pack, which lies in the lowest part of the Chernyshyn (C1čn) supra-horizon of the Carboniferous Tournaisian (C1t) stage sediments. The positive value boundary on the sections and cubes of effective AI and RC, corresponding to the top of the carbonate sediments of the Upper Devonian, is the reflecting horizon C–D (II–III). It is identified as the top of the Nyumylg horizon (D3nm) deposits of the upper substage of the Famennian Devonian stage. The horizon deposits (the top of which is the C–D reflector) as a whole in the study area controls the pattern of angular unconformity of layers of the Famennian age going under erosion with overlying coal deposits. Before proceeding to the study of the geological structure of the target object— one of the elements of the buried river systems—incised fluvial valleys, let us briefly discuss a question that is not directly characteristic of such objects, but relates to the features of their display in the form of corresponding reflecting boundaries on sections of effective AI and RC. We are talking about the application of the procedure of stratigraphic reflector tying to seismic traces and their identification in AI and RC sections. Such a procedure is included in the list of tasks for

Fig. 9.2 Target reflector identification on the section of effective acoustic impedance along the line pr07 at the site of the well 39 and paleoriver locations: (a) time section, (b) the section of effective acoustic impedance

9.1 Structural-, Tectonical-, and Lithological-Shielded Oil-Perspective. . .

363

Fig. 9.3 Mapping of the paleo-river on the results of various seismic wave field transformations along the line 190 for the deposit interval of the upper part of the Devonian on the section fragments of: (a) wave field, (b) acoustic impedance, (c) reflection coefficients, (d) instantaneous amplitudes, (e) instantaneous phase, (f) instantaneous frequencies

Fig. 9.4 Prospective object A of the horizon C–D in the study area and its imagine on 2D seismic sections: (a) structural map along top of Numylgsky (D3nm) sediment (at the top substage of the Famennian stage); sections along the line pr13: (b) AI, (c) RC, (d) instantaneous phases of RC; sections along the line pr12: (e) AI, (f) RC, (g) instantaneous phases of RC

364 9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.5 The paleoriver structure on the coherence map and fragment sections of effective acoustic impedance (AI) in the sediments of horizons C–D and D3fm2(F5): 3D(S) coherence map generation of the wave field amplitudes along paleoisochronous surface C–D: (a) (C–D) + 5 milliseconds, (b) (C–D) + 15 ms; AI section fragments for inlines: (c) 380, (d) 280, (e) 180, and (f) 80

9.1 Structural-, Tectonical-, and Lithological-Shielded Oil-Perspective. . . 365

366

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.6 Mapping paleochannels of the paleoriver system on maps of wave field amplitude coherence on the site locations of 2D line and 3D(N) and 3D(S) cube sections along paleoisochronous surface C–D

implementing the method for predicting geological and geophysical parameters using the HRS method [10]. An example of the stratigraphic tying of reflectors to seismic traces in the location zone of the well 39 of one of the oilfields located in the study area is shown in Fig. 9.2a (in the case of compliance with the mounted NL curve of standard well logging and elements of the wave field structure). In general, with such a stratigraphic reflection binding, there is a satisfactory correspondence between the synthetic seismogram traces and the actual seismic traces formed from the seismic wave

Fig. 9.7 Display of the internal paleochannel structure, their filling structure, and the relationship with the host medium for the sediments of the horizons C–D and D3fm2(F5) along the directions of lines A-A, B-B, and C-C on the fragments of sections: (a–c) wave field, (d–f) effective acoustic impedance, (g–i) effective reflection coefficients, (j) a fragment of the 3D(N) wave field cube coherence map in the area of the buried river system

9.1 Structural-, Tectonical-, and Lithological-Shielded Oil-Perspective. . . 367

368

9 Examples of HRS-Geo Technology Used in Other Regions

field records along the line pr07 in the vertical plane zone of the well 39. The correspondence of the main reflecting horizon times to the geological boundaries of the lithologic and stratigraphic subdivisions under study is also observed here. Some visible differences between the model and real seismic traces as presented in Figure 9.2a are due to the use of a certain approximation of the velocity curves (as well as density and acoustic impedance) specified by the acoustic model of the section; insufficiently complete consideration of multiple reflections and absorption properties of seismic waves by a layered medium; the difference in the vertical plane of the well section considered from the actual spatial position of the reflected wave propagation in a real medium, etc. For the same reasons, not all the boundaries characterizing the thin layer of the real geological section can be linked to the extremes of the amplitudes of the seismic wave field (both the model wave field and the real one). The stratigraphic linking of the reflectors, formed on the AI sections, is performed using field geophysical materials, borehole, and land seismic data. The application staging of such a processing procedure (in the general sequence of main procedures implementation for processing and interpreting geophysical data) is due to the peculiarities of the seismic-acoustic trace structure of the AI sections, i.e., features that distinguish them from traditional wave field seismic records. This method of identifying the reflecting boundaries in the sections used allows us to avoid a number of inconsistencies associated with the manifestation of distorting effects due to the wave field interference. The application of the high-resolution seismic method implemented in the HRSGeo technology allows identifying and then correlating the reflecting horizons corresponding to the tops of the acoustically heterogeneous target layers in the studied model of the real subsurface. This, in turn, makes it possible to distinguish zones confined to buried river systems—the supposed river valley incisions that lie in conditions that are difficult to decipher not only the wave field (Fig. 9.2a) but also in some cases the field of acoustic impedance (Fig. 9.2b). Here one should emphasize a feature that can “slip away” from the view of interpreters. Seismic wave field in Fig. 9.1, presented on fragments of vertical sections along the lines 250 and 270, was obtained as a result of traditional, standard processing of seismic records (as is known, this does not exclude the use of procedures from the processing graph, which rather strongly distort the dynamics of seismic recording, i.e., making it non-recoverable). The application of the HRS-Geo technology procedures allows the implementation of a special data preprocessing graph, which provides for obtaining recoverable seismic recording dynamics (see Sect. 4.3 and [10]). One of the seismic profiles of the sublatitudinal direction (along the inline 190), which is located on the research area to the south and almost parallel to the line pr07 (discussed in Fig. 9.2), shows an example of paleo-incision on the top of the C–D horizon deposits on the results of various seismic wave field transformations (Fig. 9.3). The structural features of the paleo-incision (which is intersected by the inline 190 in the transverse direction) are presented in the field of the time section (Fig. 9.3a); acoustic impedance (Fig. 9.3b); reflection coefficients (Fig. 9.3c); instantaneous dynamic characteristics—instantaneous amplitudes (Fig. 9.3d);

9.1 Structural-, Tectonical-, and Lithological-Shielded Oil-Perspective. . .

369

instantaneous phases (Fig. 9.3e); and instantaneous frequencies (Fig. 9.3f). It is clearly seen that the paleo-incision coast is confined to intraformational tectonic faults, and they most surely manifest themselves in the cross section of acoustic impedance (Fig. 9.3b). However, taking into consideration that the entire 3D cube of seismic data was preprocessed by a special graph, which ensures obtaining the recoverable dynamics of seismic recording, then all the results of seismic record transformations also stand out confidently and practically without any horizontal displacements or “erosion.” Deposits of the upper part of the C–D horizon in the area under study (where a significant amount of 2D seismic profiles and two seismic data cubes are worked out: one 3D(S) is southern, and the other 3D(N) is northern), as can be seen in Fig. 9.4a, are experiencing a significant complication by paleo-incision, formed by a rather dynamic paleoriver stream, which manifested itself in the upper Famennian time. In Fig. 9.4a, the paleoriver channel contour is highlighted with red lines. It is clearly seen that the sediments of the considered C–D horizon turn out to be rugged not only by the channel of this paleoriver but also by its tributaries in various parts of the 2D and 3D surveys. One of the most interesting in terms of oil and gas potential is the site located in the northern part of the area (directly adjacent to the northeast bend of the paleoriver channel contour, designated by as a perspective object A). A tectonically screened trap is quite confidently fixed on this bend of the paleoriver. The features and character of perspective object A manifestation in the sediments under consideration are presented on fragments of acoustic impedance (AI), reflection coefficient (RC), and instantaneous phase according to RC values for pr13 2D profiles (Fig. 9.4b–d) and pr12 (Fig. 9.4e–g). On these sections to the west of object A, the identified contour of the paleoriver channel together with the accompanying tectonic disturbances is well manifested. On the sections of the RC and instantaneous phase, the possible level of water-oil contact (OWC) is distinguished from the RC values by 2D lines, which, in our opinion, is an additional dynamic sign confirming the presence of a perspective object A. On the 3D(S) coherence map of the wave field amplitude along the paleoisochronic surface of the C–D horizon, the manifested seismic recording irregularity (in the form of increased intensity background dimming) clearly marked the abovementioned paleoriver channel contour and adjacent “branches” of smaller paleo-incisions (Fig. 9.5a, b). Due to the use of dimming lines of seismic recording dynamics (obtained at different levels (+5 ms, +15 ms) from the paleoisochronous surface of the C–D horizon), it is possible to significantly refine the contours of both paleoriver shores and some features of their internal structure (Fig. 9.5c–f). The mapping of the paleochannel contours of the river system on the coherence maps of 3D wave field cubes along the paleoisochronous surface of the C–D horizon and on some AI sections of the 2D lines throughout the study area are shown in Fig. 9.6. In this figure, the paleochannels of river system is clearly visible throughout the entire area. The fragments of sections of acoustic impedance (AI), obtained both from 2D seismic profiles and from specified directions of 3D AI cubes, distinctly distinguish the coastal cliffs of the paleochannels in different parts of the research

370

9 Examples of HRS-Geo Technology Used in Other Regions

area and confidently fix all the tracing contour features of the buried river system under study. To the above, it should be added that the formal transfer to the time sections of the reflecting boundaries (identified in the AI sections) along the C–D horizon and along the lower D3fm2 (Ф5) horizon—the top of the Ф5 stratum of the Famennian Upper Devonian stage, which is also “affected” by the buried channel system—shows that the objects selection and its tracking that are recorded on sections of the AI and RC; on CDP time sections are possible only with a large degree of uncertainty. At the same time, the paleo-incision channel on the CDP time sections can be easily confused with objects of the “intrashelf banks” or “single-phase reef formations” (not to mention the impossibility of defining the assumed OWC in the time sections) as can be seen in Fig. 9.1.

9.1.2

Channel Filling Features of the Buried River System

To get a more complete picture of the detected paleo-incision, located in the northwestern part of the search area, the results of its “scanning” in the form of sections dissecting the 3D(N) cube of seismic data along selected most characteristic directions are given below (Fig.9.7a–i). In this figure, in particular, it shows how possible promising objects (the main paleo-incision and adjacent coastal ledges) manifest themselves on fragments of time (WF) and AI and RC sections in given directions А-А (inline 350 line), B-B, and С-С accordingly. The indicated directions intersect the main channel of the paleo-incision at different angles (including an acute angle along the line A-A and almost at the right angle along the line B-B), as well as along the central axis of the paleo-incision (along the line C-C) (Fig. 9.7j). It is clearly seen how the target object itself can be traced confidently on the AI and RC sections presented (paleo-incision on the AI sections) and its internal filling with sediments (on the RC sections). On the AI sections, the contour of the paleo-incision itself is clearly visible (Fig. 9.7d–e) and, on the RC sections, the sealing formation deposits, i.e., the stratum of the paleochannel filling in its transverse and longitudinal sections (Fig. 9.7g–i). On the AI and RC fragments (Fig. 9.7f–i) of the line formed along the paleoincision central axis (along the C-C line), the profile relief of the paleoriver bottom and varying paleo-incision filling with the sediments are observed. Obvious is the fact of the ambiguity of the mapping of these specified diagnostic features (paleoincision and the fine structure of its filling) on the interference structure of the wave field—on fragments of CDP time sections (Fig. 9.7a–c). To assess the possibility of identifying separate river offsets (“branches”) in the C–D horizon deposits, relatively small (in their linear sizes) paleo-channels directly adjacent to the main, largest paleoriver on section fragments along the A-A directions (Fig. 9.7d) and B-B (Fig. 9.7e), such results are given. Here the “branches” are fixed—relatively shallow paleochannel incisions, as can be clearly seen on a fragment of the wave field dynamics coherence map (Fig. 9.7j). At the same time, on the

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

371

presented section fragments of AI (Fig. 9.7d–e) and RC (Fig. 9.7g–h), relatively small incisions of paleorivers are distinguished with kinematic and dynamic features of reflected waves of smaller forms and values. On a fragment of the time section of the CDP (Fig. 9.7a–c), these features are practically not distinguished (even if this time section is obtained using a special processing graph [10]). The main conclusions of the performed studies are as follows. 1. Under conditions of a complex geological structure of a real subsurface in interference wave fields, the resolution of which is determined by the wave parameters of the seismic record (its amplitude and phase characteristics), it is easy to get erroneous interpretation results. In particular, it is easy to confuse a perspective object (depicted in the wave field structure as a corresponding “seismic image”) directly related to the buried river system (paleo-incision) with an assumed “biohermal structure” or other acoustic heterogeneities of the real medium. 2. In order to unambiguously judge the structural features of the buried river systems, their structural elements in the form of paleo-incision and features of their filling structures, on the one hand, the implementation of a special graph of seismic data processing, which provides obtaining of the restorable seismic recording dynamics, is necessary, and on the other hand, the implementation of the procedure for solving an inverse dynamic seismic problem by numerical methods allows obtaining acoustic impedance (AI) and reflectivity (RC). 3. The analysis of the buried river systems structure and its development (paleoincision, erosion sides boundaries, internal filling structures with paleo-channel sediments, etc.) showed that they are most reliably manifested on 3D materials of seismic data cubes, in contrast to the data obtained on 2D sections of seismic profiles. 4. In order to increase the efficiency of predicting acoustic heterogeneities, such as the old paleoriver channels, the elements of their filling structures, the incisions of the river paleo-valley, the depth of erosion, etc. for a complex of diagnostic features, a comprehensive study of diverse geological information about promising objects, including the kinematic and dynamic characteristics of reflected waves, which are reconstructed using the high-resolution seismic method, is necessary.

9.2

Structural-, Tectonical-, and Lithological-Screened Oil-Perspective Objects in the Jurassic Complex and the Top of the Pre-Jurassic Formation Reservoirs (West Siberian Province)

Geological conditions on the studied area are characterized by a certain zonality of productive sediment distribution, expressed in their complex mosaic character, the widespread development of very thin oil-saturated thicknesses, and disjunctive

372

9 Examples of HRS-Geo Technology Used in Other Regions

tectogenesis and reservoirs characterized by different structure of the pore space. Industrial oil-bearing capacity under such conditions is associated with continental and subcontinental sediments of the Tyumen formation (horizons Т1(Ю2), Т2(Ю3 + Ю4), and Т3(Ю5)). Here, the authors have processed and interpreted well logging data on 102 exploration and production wells [11]. Land-based 3D seismic data of 250 km2 were processed using OMEGA software, HRS-Geo technology, and the GeoGraphix R2004.1, v.8.5 interpretation system [12, 13]. The possibility of forming a thin-layer geological model of Jurassic deposits and pre-Jurassic formations based on the complex dynamic interpretation of highresolution seismic data (3D seismic survey materials), well logging, and drilling is shown. Let us discuss in brief the main stages of automated processing and the interpretation and integrated use of well and land seismic surveys for the purpose of predicting the most important components for geological prospecting and industrial exploration.

9.2.1

Automated Processing and Interpretation of GBS Materials

The processing of well logging data on deep boreholes of the work area was performed using a specially developed WPS system, which is part of the HRS-Geo technology as its component. In this system, at the research stage, a method of processing and automated interpretation was used, based on the integration of geophysical parameters into a single geological information system through functional transformations and described in Sect. 5.1 and [11, 14, 15]. A feature of this system is that with its help, according to geophysical borehole survey (GBS), it is possible to evaluate important geological indicators of the section under study, including reservoir properties (within the framework of an adapted filtration model to actual data and the use of petrophysical relations “core GBS”)—oil and gas saturation of reservoir rocks and to a certain extent the shielding (insulating) properties of seals. The data on the geological marks of lithostratigraphic units (the Jurassic complex and pre-Jurassic formations) for 102 wells located in the contour of a seismic cube 3D survey were preliminarily formed by experts of the “Uraineftegaz.” These materials were directly used in (a) building fragments of lithologic stratigraphic well columns (within the Lower Cretaceous, Jurassic complexes, and the upper part of the pre-Jurassic basement); (b) stratigraphic tying and identification of reflectors formed on high-resolution data—sections of acoustic impedance (AI) and reflectivity (RC); and (c) setting up seismic data (AI and RC values of the 3D cube) on the parameters of the target sediment geoacoustic model of each well and the subsequent prediction of the composition and properties of the section under study.

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

373

An example of processing and automated interpretation of well log data for a number of wells is shown in Fig. 9.8a. Here, in addition to the lithologic stratigraphic column of the well, the set of the original GBS diagrams used directly in their continuous automated processing, and the results of testing, the calculated curves of the absolute (physical) and phase permeabilities for water and oil are also given (graphs ka, kw, and ko). Features of the latter image are that they are represented on a horizontal scale (coordinate) on an uneven logarithmic scale [11]. In addition, for each of the wells being processed, the results are in digital form presented in summary tables. In these tables, the main geological and geophysical parameters for permeable intervals of the well section are presented in the form of pointwise and layer-by-layer parameter determination. These parameters (the desired values of which are on the porosity threshold exceeding Kp
15%) are given as open (Kp) and effective (Keff.p) porosities, oil saturation coefficients (Ko) and water saturation (Kw), absolute coefficients (ka), phase for water (kw) and for oil (ko) permeabilities, and volume clay content (Kcl) and sandiness (Ksand), as well as the original curves of standard (SP, resistivity), induction (IL), radioactive (GR, NL), sonic (SN(ΔT)) logging, and inclinometry (CAL) of the wells [11]. The available well material was widely used by the authors for linking it with seismic data, including for constructing correlation schemes for lithostratigraphic units of Jurassic sediments (their packs and horizons). Such schemes were constructed directly from the sets of results of their processing and interpretation, as shown in Fig. 9.8b. The need for such schemes arose in assessing the nature of changes in the thickness of productive and potentially productive intervals, oil-saturated thicknesses, lithofacial substitutions, pinching, etc. On this basis, the possibilities of the seismic method for identifying the corresponding target reflectors were also evaluated, the patterns of changes in seismic velocities were analyzed, and the consistency of the results of solving kinematic interpretation problems (structural mapping) and interpretation of dynamic seismic data processing with geological and geophysical parameters was evaluated.

9.2.2

Prediction of Geological Structure along Reference Profiles

An example of predicting geological and geophysical parameter results for one of the 3D reference profiles of the seismic data cube for the Lower Cretaceous, Jurassic complexes, and the upper part of the pre-Jurassic basement is shown in Fig. 9.9a–d. These results are presented in the form of traditional values of AI (a), clay content (b), porosity (c), and oil and gas saturation (d). The identified reflectors here are relatively well identifiable and clear. For their identification, data were used on the marks of the corresponding stratigraphic boundaries in existing wells, a priori information about seismic velocities for specific lithostratigraphic units of the

374

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.8 Litho-stratigraphic column with distribution of character and degree of fluid saturation of Jurassic and Lower Cretaceous reservoir rocks along the well 10629 (a), correlation scheme of Jurassic deposits in the well columns (b)

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

375

Fig. 9.8 (continued)

section being studied, as well as specially developed software for linking the reflectors with the corresponding geological boundaries. As can be seen from Fig. 9.9b, the maximum clayization is recorded in the intervals of occurrence of the upper Danilov deposits (horizon Б(Ю0)), the upper part of the lower Danilov subsuite deposits (the horizon П(Ю1)), and in the sediments of the lower part of the Charosoim (upper part horizon includes perspective formation X1–2) suite (clay content here ranges from 70% to 95%). Smaller clay formation is typical for deposits of the prospective reservoir X1–2, productive horizons Т1(Ю2) and Т2(Ю3 + Ю4), partially horizons Т3(Ю5) and Т4(Ю6), and the uppermost part of the pre-Jurassic basement А-А1—here the content of clayey material varies widely from 10% to 70%. The sandstone and aleurolite strata in the sediments of the coastal-sea shallow and continental genesis (horizons Х1–2, Т1(Ю2), Т2(Ю3 + Ю4), Т3(Ю5), Т4(Ю6), and А-А1) are the most porous. For them, porosity is characterized by an uneven distribution along the lateral direction and values of 10–21% (Fig. 9.9c). The intervals of the section with good reservoir properties correspond to the most significant oil-saturated areas of the studied strata (mainly deposits of the productive horizons Т1(Ю2) and Т2(Ю3 + Ю4)). The studied productive deposits of the

376

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.9 Predicted seismic sections along the crossline 2330: acoustic impedance (a), clay (b), porosity (c), and oil and gas saturation (d)

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

377

Fig. 9.9 (continued)

Jurassic complex are characterized by heterogeneity of the structure and uneven distribution density of oil and gas resources, and the development of the reservoir (its geometry) along the profile is quite complex, discontinuous, zonal, and lenticular (Fig. 9.9d ). The detailed internal structure of pre-Jurassic sediments in the profile under consideration is rather strongly dislocated.

378

9.2.3

9 Examples of HRS-Geo Technology Used in Other Regions

Tectonic Disturbance Manifestations in the Research Area

The formation of tectonic fault polygons, as is known, is necessary in structural constructions based on deposits of target horizons. Often, the tracing of tectonic disturbances, like the correlation of reflecting horizons, is an ambiguous process. Such a problem acquires relevance in the complex tectonic structure of the geological medium, for example, when the system of tectonic faults is represented by faults accompanied by numerous short rootless, chaotically distributed disturbances due to horizontal and vertical stresses. Therefore, the authors used data from two independent sources to identify the features of the distribution of such disturbances in the area under study in the horizon under consideration (П): in particular (1) fault projections, confidently distinguished on the reference sections (for typical profiles, CDP cross sections) of 3D cube of effective acoustic impedance (Fig. 9.10a) and (2) the coherence map of the wave field cube along the paleoisochronous surface of this horizon (Fig. 9.10b). Considering these data, a complex system of mutually related tectonic disturbances was finally formed, inheriting and uniting along with large regional faults (submeridional and sublatitudinal stretches) numerous tectonic ruptures and shear movements of a smaller scale and nature. It is quite obvious that both through tectonic faults (originating in the pre-Jurassic complex) and rootless faults that occur in the interval of the target sediments are reflected here. Both those and others have a certain inherited but also have a different nature of distribution and development. Judging by the nature of the wave field amplitude distribution along the paleoisochronous surface of horizon П(Ю1) on the map of the coherence cube and the characteristics of tracing tectonic faults of different nature in the area (Fig. 9.10a, b) one can clearly see the difference in the areas (zones) of these disturbances. Thus, the area of structures located in the west is characterized by the distribution of relatively short, multidirectional faults. This area differs from the central and eastern parts of the area, where depressive structures are also common. The last of the zones are characterized by the presence of several more extended (submeridian directions), structure-forming (but somewhat smaller in number) tectonic disturbances (Fig. 9.10). For a qualitative geological interpretation, it is advisable to compare the results of the structure tectonic elements formation with the dynamic parameters of the reflected waves, namely, with the features of the distribution of average effective acoustic impedance (Fig. 9.10b). As can be seen from the figure, a comparison of such structure components for the sediments of the lower Danilov subsuite (lithologic and stratigraphic analogue of the Abalak suite, Callovian-Oxfordian stage) shows, firstly, the zonality in the distribution of the geologic-geophysical parameter under consideration, due to the corresponding sedimentation conditions. This zoning is associated with the types of the Danilov, transition, and Tutleym geological

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

379

Fig. 9.10 The maps along the top of Lower Danilov subsuite deposits—П horizon: wave field amplitude coherence along the surface with tectonic faults on reference lines (a) and wave field amplitude coherence along the surface with tectonic faults and horizon isochrons (b)

sections, although the horizons of these types are somewhat blurred and not clear enough. Secondly, the relation of the geological structure elements—polygons of tectonic faults with the features of acoustic heterogeneities distribution in this medium—is well correlated. The geological object considered in this example, such as the deposits of the Lower Danilov subsuite, is represented by coastal-

380

9 Examples of HRS-Geo Technology Used in Other Regions

marine sandy-argillaceous deposits (here the complex is rather strongly clayed, composed of black, loose argillites with charred organic residues, with inclusions of glauconitic material, pyrite, and layers of oolitic siderite).

9.2.4

The Results of Structural Constructions

As the initial information for constructing isochron maps, interval time maps (isopachs), and structural maps, the values of the observed times of reflecting horizons stratified with the boundaries of the geological section and polygons of tectonic faults are used (for deposits of those horizons where these disturbances are established). The methodology of structural constructions is based on the fact that calculations of the reflecting horizon depths are carried out from the reference surface, which is traceable during seismic studies as the upper reference reflecting horizon. When interpreting materials, such a reference surface is taken to be the top of one of the overlying horizons—in our case, the top of the Kharosoim suite, with which a previously traced reflector is identified. Structural maps for target reflectors were constructed using the observed times (t0), average velocities, and top depths of the Kharosoim formation (X1) for wells. Structural maps for the top of the underlying horizons and layers and for the surface of the pre-Jurassic base A are constructed by recalculation from the structural plan of the reflecting horizon X1 using the corresponding time thicknesses (Δt) and variable interval velocities. As an example of such structural constructions in Fig. 9.11a–c, structural maps are given: along horizon П (deposits of the Lower Danilov subsuite), along the top of the horizon Т2(Ю3 + Ю4)—one of the main productive strata in the study area (deposits of the Tyumen suite)—and along the surface of the pre-Jurassic basement (horizon A). In this case, we can state the inherited nature of the development of the revealed and refined structures of the second and fourth orders within the considered area. From the constructed structural maps for all horizons, a 3D structural-tectonic model was formed that characterizes the mutual arrangement of structural surfaces and the relationship of tectonic dislocations at different depths of the studied real medium (Fig. 9.11d). It should be noted that surface depths over almost all horizons are controlled by data on available deep wells in the work area. The most studied segments are elevated parts of the research area—most of the drilled deep wells are confined to them. Insufficiently illuminated by drilling is a section of the Jurassic complex in its depressed areas where nonstructural traps of various types can be formed due to the sharp facies zonation of lithostratigraphic units of the complex in paleodepression areas that are not compensated by sedimentation.

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

381

Fig. 9.11 Structural maps along the horizons: П (Lower Danilov subsuite) (a), Т2(Ю3 + Ю4) (Tyumen suite) (b), A (pre-Jurassic basement) (c), and 3D image of structural maps along the horizons of Lower Cretaceous, Jurassic sediments, and the surface of the pre-Jurassic basement (d)

9.2.5

Area Geological Indicators According to HRS, GBS, and Drilling Data

The prediction results for the most important geological and geophysical parameters for the Т2(Ю3 + Ю4) horizon deposits are presented by the maps of average oil and gas saturation (Fig. 9.12a), porosity (Fig. 9.12b), clay content (Fig. 9.12c), oil and gas thicknesses (Fig. 9.12d), and the predicted distribution density of hydrocarbons (Fig. 9.12e). On the map of predicted average values of oil saturation (Fig. 9.12a), oilsaturated areas are fixed by the research area in the form of local zones of different sizes and intensities of oil saturation, confined to the local structures of the revealed second-order arches (uplifts) (most of them are hydrodynamically interconnected). A number of small-sized zones with a relatively lower level of oil saturation are fragmented, located in different parts of the work area. All these zones are

382

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.12 Predicted average oil and gas saturation (a), porosity (b), shale content (c), oil-saturated thickness (d), and hydrocarbon distribution density (e) maps for deposits of Tumen suite

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

383

Fig. 9.12 (continued)

characterized by relatively high values of oil saturation, reaching values of 9–15%. At the same time, the most intense oil and gas saturation anomalies are located in different parts of the identified arch—near local structures. The porosity distribution presented in Fig. 9.12b shows that oil saturation is confined to reservoir formations having an average porosity value of 7.5%–15.0%. The distribution of anomalies with increased porosity is manifested mainly within the second-order structure [16]. It can be assumed that the formation of such anomalies is associated with the tectonic factor, namely, with horizontal shear dislocations and the zones of fracture, compression, and tension associated with them. On the map of the predicted clayiness distribution together with the oil saturation contours, it can be seen that the clay material content in the Т2(Ю3 + Ю4) horizon deposits over the area is rather uneven (Fig. 9.12c). Zones with relatively high values of oil saturation in most cases correspond to lower predicted clay values of the

384

9 Examples of HRS-Geo Technology Used in Other Regions

sediments under consideration. In such areas, the average clayiness for deposits of the Т2(Ю3 + Ю4) horizon generally vary within 4–10%; in the rest of the territory— 10–14%—these more clayer parts are located most often outside the boundaries of oil saturation. On the whole, the allocation of the predicted average clay values over the study area for the Т2(Ю3 + Ю4) horizon deposits has a certain inverse relationship with the features of the porosity distribution. Higher values of porosity (Fig. 9.12b) correspond to lower clayiness (Fig. 9.12c). On the map of prediction oil- and gas-saturated thicknesses with contours of significant average oil saturation, the largest values of the parameter under consideration are observed in areas with increased oil saturation (Fig. 9.12d). However, the largest oil-saturated thicknesses are recorded within individual local zones, reaching values of 4.0–5.6 m. It should be noted that the above values of oil-saturated thicknesses were previously obtained also from the results of well log processing carried out in all wells of the work area. For reservoir layers of horizon Т2(Ю3 + Ю4), 29 wells with oil-saturated thicknesses in the range of 1.2–5.6 m are gathered in the work area (out of 102 wells participating in the processing). In accordance with the calculation of the resources and components of the geological substance in the identified outlines of significant oil saturation, specific variable values of the porosity coefficient, effective oil-saturated thicknesses, and oil saturation coefficient of the reservoir pore space are taken into account. This is done according to the volumetric method formulas in each of the design points (more precisely, in the vertical section along each of the predicted seismic trace). Using the procedure for normalizing all the components of a geological substance, including the hydrocarbons themselves, in the appropriate units of measurement (in particular, in the depth scale, i.e., in the same units as the oil-saturated thickness, in meters), after subtracting components that are not associated with hydrocarbons, the oil volume portion is determined (in units of depth scale). The indicated procedure for normalizing and recalculating the volumetric content of the geological substance components at a depth scale allows us to obtain maps of the predicted distribution density of hydrocarbons (oil) for perspective horizons in the deposits of the Tyumen suite. For the sediments of horizon Т2(Ю3 + Ю4), an example of such a map is given in Fig. 9.12e. In accordance with the oil reservoir thickness scale, the range of its variation here is in the range of 0.0–0.45 m (with a wall height of a single curved cylinder—reservoir equal to 0.05 m). The highest predicted distribution density of hydrocarbons is characterized by oil saturation contours located in the eastern half of the area. Here, a large group of desired parameter anomalies is confidently correlated, stretching along the positive structure of the second order (that part of it, which can be traced from the northwest to southeast in the contour of the studied region). The obtained anomalies of increased density of hydrocarbon distribution both in their linear sizes (configurations) and in their intensity are quite diverse. A significant part of these anomalies is located in areas that have not been studied by wells. In the western part of the area, a number of desired parameter anomalies are smaller in linear dimensions.

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

385

There are also a number of points and surrounding areas (zones) with high values of the predicted distribution density of hydrocarbons, characterized by different sizes and intensities. They should also be taken into consideration when calculating hydrocarbon resources in combination with other oil-saturated objects of the Jurassic complex.

9.2.6

Structure of the Upper Part of the Pre-Jurassic Basement

The deposits of the uppermost part of the pre-Jurassic base—mainly the Turin series of the Triassic on the effective acoustic impedance sections—are distinguished by two reflecting boundaries: “A,” the border associated with the erosion-denudation surface of the pre-Jurassic Permian-Triassic basement, and “A1,” the conditional interface within the pre-Jurassic base conducted 15 ms lower from horizon “A.” Deposits between these boundaries are represented to a large extent by weathering crust formations. Judging by the variety of structural and acoustic features of the deposits of the upper part of the pre-Jurassic formations, we can speak about the very complex nature of the deposits’ structure. In general, it can be stated that the megacomplex of pre-Jurassic rocks is represented by at least two seismic stratigraphic complexes. First, this is a folded, highly dislocated complex, represented by metamorphic and igneous rocks of geosynclinal formations, which correspond to a very disordered or chaotic position of the in-phase axes of the received reflections with different sharply changing angles of inclination of the reflecting horizons, less often, in the absence of reflections. Second, it is an intermediate structural stage (between the crystalline base and sedimentary stratum), represented by weak dislocated or almost horizontally effusive-sedimentary Permian-Triassic rocks, sediments, which corresponds to a slightly wavy or subparallel arrangement of reflecting horizons. At this stage of the study, four maps for horizon deposits (A-A1) were built. These are maps of the effective acoustic impedance distribution (Fig. 9.13a), predicted average values of porosity (Fig. 9.13b), predicted average clay content (Fig. 9.13c), and predicted oil manifestations (indications) in the formations of the near top part of the pre-Jurassic basement of the horizon A-A1 (Fig. 9.13d). On the map of the distribution of effective acoustic impedance (AI), in particular, a certain zoning is visible. Relatively smaller AI values are located in different parts of the area, but the largest of them are in the northwestern and central regions of the search area under consideration (Fig. 9.13a). Higher AI values are distributed in the eastern, northeastern, and southwestern parts (areas) of the site. In addition, in the southern half of the area, two zones are recorded with respect to increased AI values of sublatitudinal strike. With the same regularity, the distribution of predicted average values of porosity (Fig. 9.13b) and clay content (Fig. 9.13c) is also observed. Here, the above zonality

386

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.13 The maps of distribution along the horizon A-A1 (upper part of the pre-Jurassic basement): acoustic impedance (a), porosity (b), clay content (c), and oil saturation signs (d)

in the distribution of these parameters is most clearly traced. It can be assumed that zones of increased porosity (especially those with relatively long submeridian and sublatitudinal strike) are largely due to the increased rock fracturing of the pre-Jurassic basement. The nature of these anomalies and their orientation in a real geological medium, in our opinion, can be associated with the manifestation of fragments of the so-called planetary lineaments here—“power meridians” (Myasnikova G.P. and Zmanovskaya O.I., 2004). Obviously, crushing, fracturing,

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

387

Fig. 9.13 (continued)

decompression zones (linear, arcuate), carbonatization, and sometimes dynamothermal metamorphism and metasomatic transformations are identified to a large extent along the tectonic disturbance zones of the regional plan. The northern, northeastern, and southeastern directions of increased porosity zones can be linked to the manifestation of possible annular anomalies resulting from the action of shear deformations. Note that the elements of the shear dislocation process and the corresponding arched structural forms identified in geomorphology and tectonics were proposed in one of the famous works. Here, the influence of tangential forces during horizontal dislocations causing rotations (torsion) of

388

9 Examples of HRS-Geo Technology Used in Other Regions

structures and their vertical growth in zones of maximum stresses is possible. In the same context, the presence of dominant compression processes in the shear zone along one of the rupture wings, and in the opposite, the signs of tension, which also affect the processes of hydrocarbon accumulation, are emphasized. Elements of such expressed morphological features of arcuate structures can also be those anomalies that are presented by us in the form of parameters of acoustic impedance, porosity, and clay distribution (Fig. 9.13a–c). Within these anomalies, local sections of porosity (fracturing) of the proposed reservoirs—the considered parameters reach average values: porosity of  14–18%, clay content of not exceeding 6–10%. Given the rather difficult situation with the possibility of predicting oil content from a set of seismic, drilling, and well logging data, the authors nevertheless consider it appropriate to present in this subsection the obtained results of predicted oil occurrences—signs of oil in the near top part of the pre-Jurassic base (Fig. 9.13d). It should be noted that there are practically no industrial oil inflows from the section of the pre-Jurassic basement in the area under consideration. There are a number of wells, in the context of pre-Jurassic formations of which, according to the results of processing the well logs, signs of oil saturation were detected. Moreover, reservoir layers according to well logs are most often manifested in metamorphosed rocks with porosity values varying in the range of 8–15%. It should also be noted that there are a number of wells in which, to one degree or another, a water-saturated reservoir is fixed in metamorphosed, effusive, effusivesedimentary, and volcanic-detrital (pyroclastic) rocks. There are also wells located in the northwestern part of the research area, with a water-saturated reservoir in the context of the pre-Jurassic rock complex in which the well tests were carried out. However, in general, the section of the pre-Jurassic rock complex, discovered by most of the wells, according to the results of processing the GBS materials, turns out to be without reservoir layers. This may be due to various reasons. First of all, the informational content of logging methods (usually adapted to define reservoirs mainly in terrigenous sediments) is low for pre-Jurassic formations. These formations are represented, as is known, by a significant variety of effusive, volcanicsedimentary, metamorphic, and weathering crust rocks. In addition, the presence of cavernous, fractured porosity, decompression zones, and mixed types of reservoirs in the section of the pre-Jurassic basement, which are secondary reservoirs, do not allow common methods to single out them unambiguously. The map of predicted oil saturation signs in the top of the pre-Jurassic basement (Fig. 9.13d) shows the revealed anomalies of these signs, the intensity level of which is presented the same as for the overlying productive horizons of the Tyumen suite. The intensity level of the desired geological indicator (oil saturation feature) was determined according to the same calculation scheme that was used to determine the oil saturation for reservoir layers of the Т1(Ю2)-Т4(Ю6) horizons. If we compare the place of local contours of oil saturation signs for the top of the pre-Jurassic basement (Fig. 9.13d) with local uplifts of the second order, presented on the structural map along the surface of horizon A, then it is easy to make sure that almost all the anomalies of oil saturation signs are located on the slopes of these local

9.2 Structural-, Tectonical-, and Lithological-Screened Oil-Perspective. . .

389

uplifts. It seems that it is precisely with these pre-Jurassic sections that the zones of fracturing and decompaction of rocks are associated. In general, for the area under consideration, the predicted signs of oil saturation have average values that vary in the range of 3–9%. Naturally, the revealed anomalies in the signs of oil saturation (Fig. 9.13d) correlate quite well with increased values of porosity (Fig. 9.13b) and reduced values of clay content (Fig. 9.13c). In general, the revealed relations in the distribution of predicted geological and geophysical indicators by pre-Jurassic basis can be used in the further study of oil-perspective objects in these formations, especially when studying disintegration and leaching zones in them.

9.2.7

Perspective Points for Optimal Opening of Predicted Oil-Prospective Objects

Given the evaluative nature of the carried out work, at this research stage, the authors limited themselves to a comparative analysis of the obtained results of oil saturation predicting for different horizons (formations) by combining their oil saturation contours and a comprehensive analysis of the predicted geological and geophysical parameters considered above. In this analysis, all the obtained predictive information for the target sections was used. On this basis, as a first approximation, optimal sections of the area for further search are outlined. According to the set of predicted indicators within the combined oil saturation contours for the considered productive horizons and reservoirs, perspective points are established (the sequence of numbers of these points is set taking into account a decrease in the degree of their priority—a lower number corresponds to a greater degree of representativeness). To open the oil and gas deposits in the sediments of the corresponding horizons and reservoirs, in particular, points were identified at the intersection of the corresponding inline and crossline profiles (Fig. 9.14). Each of the points is determined taking into consideration the most optimal combination of predicted geological indicators. In determining perspective objects of lithologically shielded and structural-lithological types, the maximum ratio of average oil saturation, porosity, oil-saturated thicknesses, and oil saturation coefficients, as well as hydrocarbon resources, was taken into account primarily. For oil-perspective objects of a structural type (arch-type deposits in the study area are typical for deposits of the Tyumen suite), in addition to the predicted geological and geophysical parameters, the structural factor was also taken into account. Moreover, for structural and nonstructural types of traps, for the analysis, the predicted hydrocarbon distribution densities in the contours of significant oil saturation for each of the considered prospective horizons were widely used. In the process of such a research result analysis, ten sections were outlined—perspective points. Some of them are presented in sections of effective acoustic impedance and sections of the distribution of predicted oil saturations (Fig. 9.14). At the same time, the

Fig. 9.14 Acoustic impedance and oil saturation section fragments in the site of recommended wells

390 9 Examples of HRS-Geo Technology Used in Other Regions

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

391

recommended wells (numbers1–5) are confined to facies—sedimentation conditions associated with alluvial deposits that are caused by channel facies. These points, in spite of some ambiguity in the distribution of AI values, in our opinion, correspond to a greater extent to river deposits represented by medium- and fine-grained sands, sometimes with an admixture of gravel grains. Almost under each of the prospective points (recommended by the well) lies the entire sought-for set of the above predicted geological indicators for each of the considered productive horizons. Their location is directly tied to the corresponding set of anomalous values of the predicted geological and geophysical parameters that are necessary for the prospection and industrial exploration of oil-prospective objects and are ultimately aimed at their most efficient development. The last of the recommended wells (numbers 6–10), in accordance with the results of the comprehensive analysis discussed above, are most likely also associated with alluvial deposits but already with a group of river floodplains. As is known, they are formed in a flood in an environment of less active, and most importantly, very unstable hydrodynamics. The sediments characterizing such facies are usually finer-grained and less sorted ( fine-grained sands, clayey, and siltstones), and rapid and irregular lateral substitutions are noted (which is well confirmed by the mismatch of the considered productive intervals of numerous wells). Thus, within the framework of the HRS-Geo technology used, the possibility of dynamic processing the initial seismograms is implemented, which allows expanding the spectrum of the useful signal and increasing the signal-to-noise ratio. The preprocessing graph provides, on the one hand, a high signal-to-noise ratio and, on the other hand, preservation of the necessary identification information about the studied geological indicators in the dynamic features of the seismic record. The seismic data are obtained in the form of a 3D cube carry information, first, about the geometry of the studied geological section and, second, about the various components of the studied geological substance. The performed analysis of the predicted geological and geophysical parameters makes it possible to study the detailed internal structure of oil-prospective objects and outline the most optimal points for their further development.

9.3

Composition and Property of Oil-Perspective Strata Prediction Using High-Resolution Seismic Data (Saudi Arabia)

This subsection provides an example of solving the petroleum geology problem, obtained using observational data recorded under rather difficult seismic and geological conditions of deposits’ occurrence of the Unayzah formation at two intersecting seismic profiles located in one of the foreign regions (Saudi Arabia). It should be noted that in these deposits, the search and exploration of hydrocarbon deposits have significant oil exploration interest.

392

9 Examples of HRS-Geo Technology Used in Other Regions

The obtained seismic materials with a stack fold equal to 240 were processed by the program-methodological tools of the OMEGA system of WesternGeco in accordance with a specially developed graph (Sect. 4.3) [4, 10, 17]. This graph includes the basic procedures for standard processing of materials before stacking, post-stack processing in the time and spectral domains, and migration of the time section. As an automated interpretation of the data, we used methods for studying real geological media (inversion of initial seismograms and resulting time sections; functional transformations of GBS diagrams) implemented in HRS-Geo technology.

9.3.1

Geological Structure of the Study Area

The main objective of the research is to demonstrate the possibilities of using the software and methodological tools of the HRS-Geo technology to identify oil and gas deposits in traps of the combined and non-anticlinal type—structural-lithological, tectonically, and lithologically shielded—directly associated with deposits of the Unayzah formation. The composition of these deposits includes a whole set of relatively low-porous (on average 6–7%) heterogeneous sandstone formations. Each of these layers separately along their entire length along the study line has extremely low values of effective thickness, the identification of which using only wave seismic methods is quite problematic. Such relatively thin acoustically heterogeneous layers to a greater or lesser extent “respond” to the dynamics of seismic record, but for well-known objective reasons of an interference nature, cannot be detected within the scope of using only the wave field structure. The previously implemented geological and geophysical researches in the study area allowed local and foreign geologists to establish a number of fundamentally new features of the geological structure. In particular, it was established that the most important property of the Unayzah oil prospect under study is the distinct zoning of productive packs, expressed in the extremely complex mosaic nature of their distribution within the fields already known on the adjacent territory. The most widespread in terms of the work area is characterized by productive formations of the Unayzah study thickness (Unayzah A, A Siltstone, B, C). Obviously, they were formed as a result of the development of marine transgression, which spread along the flooded valleys of ancient rivers and other low parts of the paleorelief, primarily due to the processing of substrate rocks composed of terrigenous formations, as well as products of the removal of temporary flows and abrasion of the ancient coastline. In accordance with the existing ideas about natural reservoirs and structural features of search objects in the research region, at the stages of regional and zonal predicting of target deposits, geologists constructed a scheme for the migration of hydrocarbons from oil source sediments to the storage reservoir (Fig. 9.15a) and a paleosection according to GBS data for productive horizons (Unayzah A, A Siltstone, B, C) (Fig. 9.15b). A preliminary assessment of the structure of the study objects by one of the considered lines is also presented by them as a model of the certain local structure, depicted as a schematic seismic-geological section

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

393

Fig. 9.15 Natural reservoir sediments of the Unayzah formation: (a) hydrocarbon migration scheme from source deposits to accumulation reservoir, (b) paleosection for the productive formations according to well data

394

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.16 Schematic geoseismic section according to line 7984

(Fig. 9.16). Due to such preliminary development of schemes and models for the formation of hydrocarbon accumulations on these materials, as a first approximation, an idea was obtained of the conditions of oil formation and accumulation. In particular, tectonic dependencies of the perspective objects’ location; the nature of the target horizons’ occurrence and the presence of a trap (reservoir and shielding stratum); the main features of migration, concentration, and conservation of hydrocarbon accumulations; the presence of an oil- and gas-generating formation (Qusaiba), etc. are reflected here (Figs. 9.15 and 9.16). It is advisable to add a sufficiency condition to these necessary components (conditions)—geological indicators of the oil and gas potential of the predicted object, which is to correlate in time the processes that led to the formation of reservoirs, traps, and generation and emigration of hydrocarbons. It is also obvious that, under adverse natural factors, hydrocarbon generation and emigration could also occur during the absence of traps. A more or less reasonable answer to the last of the conditions—the sufficiency condition—can be obtained from the materials of seismic studies. To clarify the specific geological situation, as noted above, observational data on two seismic profiles located on the studied area were processed and interpreted. A quantitative prediction of various components of the studied geological section was made by the authors based on the integrated use of high-resolution seismic, drilling, and well logging data.

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

9.3.2

395

Processing and Interpretation of Deep Well GBS Data

One of the important components of the complex process of predicting the lithological composition, reservoir properties, and oil and gas saturation, in addition to interpreting high-resolution seismic data, is the processing and automated interpretation of geophysical research materials in wells (GBS). Such processing was performed using the method of functional transformations of geophysical parameters by integrating them into information systems (Sect. 5.1 and [11, 15]). In addition to the continuity of data processing and analysis, this method provides a quantitative linking of all geophysical parameters based on their direct relationships, as well as the integrated use of data and the results of solving individual problems. Using the set of these parameters located in a certain system, the material composition, porosity, content of bound water, and useful capacity are established, oil and gas saturation of the reservoirs is estimated, and a number of very important physical characteristics of the geological section are calculated. An example with the result of such processing and interpretation of well logging data for well 2 is shown in Fig. 9.17. It should be noted that well 2 (parametric one) was drilled to a depth of 4985 m and revealed the most thick (over 395 m) section of the Unayzah reservoir (Lower Permian deposits) in the study area. It is mainly represented by sandstones, which in general, as already noted, are low-porous  6–7% on average. During drilling in the Unayzah section, the well produced a gas inflow in the form of a flare with a height of 1.5–3.0 m, which was crushed by drilling fluid with an increased specific gravity. During tests in open-hole conditions, small inflows were also obtained in the form of a gas plume of 1.5–6.0 m, condensate, and a small amount of water after nitrogen treatment. During one test, attempts to stimulate the Unayzah formation by hydraulic fracturing were unsuccessful due to poor conditions for water injection into the reservoir rocks (viz., poor absorption of the reservoir rocks). The underlying Qusaiba formation (Lower Silurian deposits) was not tested due to technical problems. And in the Khuff strata (Upper Permian deposits), although the intervals of good porosity were developed, the hydraulic fracturing of which was successful, but in the conditions of a cased well, no inflow was obtained during testing. In this case, the logs indicate a weak development of the porosity of the Khuff formation (Fig. 9.17). In general, well drilling confirmed the presence of a structural trap identified by 2D seismic data, as well as oil source rocks, a sealing formation (in the form of clays and dense carbonates at the bottom of the Khuff formation), and a very thick gas-bearing reservoir (although the quality of the latter throughout the structure has not yet been fully studied). The studied structure is a relatively large anticline with a height of about 135 m at the level of the Unayzah formation (Fig. 9.16). According to geologists, the corresponding structural trap here has rather large potential reserves, despite the opening of the target section with low-porous sandstones. To this it is worth adding

396

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.17 Litho-stratigraphic column with distribution of character and the degree of reservoir rocks fluid saturation along the well 2 for the Qusaiba, Unayzah, and Khuff deposits

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

397

that at the edges of the studied area, there are several fields with good and very good oil and gas productivity or reservoirs that have not yet been tested. According to the analysis of microsection and core samples, the pore space of the upper part of the Unayzah deposits in well 2 is completely cemented by relict paleosoil. This happened early enough and prevented quartz bedding. Indeed, a rather thick interval of relict soil is observed in the upper part of the section, from where it was washed away by rains on the underlying deposits and clogged the pore space (in the intervals of 4358–4386 and 4426–4466 m; Fig. 9.17). This, according to geologists, means that as you move away from the structure, from a high interfluve, the interval of paleo-soil decreases, and the interval of sandstone increases and becomes cleaner, thereby increasing the quality of the reservoir (Fig. 9.15b). An example of seismic data processing and interpretation based on the use of HRS-Geo technology software for seismic lines of the studied area is shown in Figs. 9.18 and 9.19. One of the seismic profiles is located on the area in the sublatitudinal (7984) and the other in the submeridian (7447) directions.

9.3.3

Prediction of Geological Indicators in the Sublatitudinal Direction

At the initial stage of processing, the structure of the wave field, covering the sediments of the Lower Silurian-Upper Permian age, was preliminarily analyzed, and reflectors that corresponded to the target lithological and stratigraphic units of the studied section—Qusaiba, Unayzah, and Khuff deposits (Fig. 9.18a)—were identified on this basis. As a result, the geological structure features of the real medium in the studied vertical plane are determined. In particular, in the general structure of the wave field, the sedimentation and postsedimentation features of deposit accumulation and formation are quite confidently fixed (vertical precipitation movements associated with tectonic processes, postsedimentary folds, discontinuities, multiscale layering, etc.). To bind the target reflecting horizons to the corresponding geological boundaries of the studied section, we used sonic logging data (SN), vertical seismic profiling (VSP), and the results of 1D seismic modeling. The reflectors are directly obtained as a result of the corresponding target horizon correlation in the sections of effective acoustic impedance (AI)—the result of the inversion (solution of the inverse dynamic problem) of a seismic recording into the impulse response of the medium (Fig. 9.18b). In Fig. 9.18, in terms of effective acoustic impedance, it is presented in the form of variable intensity (this form of presenting the results of seismic inversion is very convenient for interpretation, in particular, when comparing the intensities of the AI values of different intervals of the studied section). In the presented sections, which are free from the influence of the elastic oscillations source, the seismic wave process, the ratios of different layers of a

Fig. 9.18 Seismic data processing and interpretation results along the line 7984: (a) the fragment of the CDP time section as a result of special processing graph using, (b) effective acoustic impedance (AI) section in the form of deviations, (c) AI section in the form of variable intensity; distribution of predicted parameters: (d) clayiness, (e) porosity, (f) water saturation, (g) oil and gas saturation, (h) predictive litho-stratigraphic columns for defined vertical sections of the data fragment

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

Fig. 9.18 (continued)

399

400

9 Examples of HRS-Geo Technology Used in Other Regions

Fig. 9.18 (continued)

thin-layer section with high and low values of AI are well fixed (Fig. 9.18b, c). With an accuracy of the time discretization step of the seismic record (Δt ¼ 0.002 s), the layering geometry can be seen quite confidently over almost all thin layers and interlayers of the studied section. In the process of interpreting the section of acoustic impedance, the main reflectors are correlated, starting with top of the Unayzah formation sediments. The corresponding reflecting boundary Un-k (A) is negative, i.e., has a “-” sign, here, in the section of the well 2, and the difference (from top to bottom) of formation velocity values is ΔV ¼ 1250 m/s (Fig. 9.17). The last of the reflecting horizons is Qus-k, associated with the bottom of this formation, and simultaneously with the top of the Qusaiba formation sediments, also negative (in the section of well 2, the difference in reservoir velocities here is ΔV ¼ 560 m/s (Fig. 9.17).

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

401

Between these two reflectors, three more reflecting boundaries are correlated (Fig. 9.18b, c): Un-B, the bottom of the Unayzah A siltstone formation or the top of the Unayzah B formation (this is a positive boundary with a difference in the formation velocity of ΔV ¼ 780 m/s (Fig. 9.17)); Un-B1, conditional boundary inside the Unayzah B formation (this boundary is negative); and Un-C, conditional boundary, the top of the Unayzah C formation (this boundary is positive). Moreover, as presented in Fig. 9.18b–c, rather confidently, structural features and the internal structure of the target deposits are fixed, as well as the existing elements of wedging and rock replacement with one characteristic of wave resistance (acoustic impedance) to other ones. In accordance with the developed technological scheme for processing and interpreting seismic data using the high-resolution seismic technique, the sections of reflectivity and acoustic impedance (for the corresponding target deposits) are tuned to the physical parameters and properties of the section previously obtained in a vertical section before a direct prediction of the desired geological parameters of the well 2 (Fig. 9.17). After that, the prediction of the desired geological indicators is carried out over the entire section of productive deposits limited by target reflective boundaries (using the corresponding correlation dependences and the developed system of petrophysical equations) (Sect. 7.2 and [12]). The results of the main geological and geophysical parameter prediction of the considered productive stratum along the line 7984 in the interval of the PK 0–1070 and time of 2.390–2.830 s in the form of the trace-by-trace predicted values of clay, porosity, water saturation (formation water), and hydrocarbons (the sum of oil and gas) are given, respectively, in Fig. 9.18d–g. Predicted lithologic-stratigraphic columns with fluid-type distributions for given vertical sections of the productive part with a step along the line of 50 m and in the range of PK 955–1065 are shown in Fig. 9.18h. In general, as can be seen from Fig. 9.18d, the considered deposits of the Unayzah formation are rather strongly clayed. Among the entire spectrum of stratified layers, the greatest claying is observed in the Unayzah A formations between the reflectors Un-k (A) and Un-B (in Fig. 9.18d, this is a thick blue-green palette). The distribution of clay material content along the profile in the lateral direction in each of the considered formations occurs rather unevenly (clay content here varies between 50 and 90%). In the intervals of the section, with which potentially productive horizons are associated (first of all, the Unayzah B layer, between the reflectors of Un-B and Un-B1), an uneven distribution of clay material is also observed, but with much smaller values (the limits of clay content are in the range of 15–45%). As is known, clay formations, on the one hand, have the maximum ability to save sedimentation traits (they prevent the energetic flow of postsedimentation processes due to their relatively low permeability), and, on the other hand, their screening properties control the formation, preservation, size, location, and phase composition of deposit hydrocarbons. Less clay intervals, inherent of potentially productive objects in the sediments of the Unayzah complex, are naturally characterized by an increased content of sandsilt material (Figs. 9.17 and 9.18d). The location of reservoir development zones in

402

9 Examples of HRS-Geo Technology Used in Other Regions

the corresponding sedimentation environments is associated with such intervals. The reservoir properties of the section in the form of predicted porosity for the deposits under consideration are characterized by increased values of Kp ¼ 15–22% (Fig. 9.18e). Such objects here are deposits of the horizons Unayzah B and Unayzah C. As noted above, the strata of sandstones and siltstones in these deposits are of marine and continental genesis. For a significant part of these deposits, porosity is characterized by an uneven distribution in the lateral direction. A more or less consistent distribution over the entire section is noted for deposits of the Unayzah B formation in the second half of the profile at PK  500–1070 (Fig. 9.18e). On the whole, for the most reservoir interlayers, the porosity over the Unayzah section is characterized by 15–20%. The intervals of perspective and productive deposits with sufficiently good reservoir properties marked in this way are filled with fluids, in particular, reservoir water (Fig. 9.18f), and hydrocarbons: oil + gas (Fig. 9.18g). As can be seen from the presented figures, the intervals with good reservoir properties correspond to the most significant oil-saturated sections of the studied productive strata. It should also be noted that the studied productive and potentially productive formations of the considered sediment complexes are characterized by the heterogeneity of the structure and uneven distribution density of oil and gas resources. This is clearly seen both on sections of the predicted values of lithological components, reservoir properties, the nature and degree of reservoir rock saturation with fluids (Fig. 9.18d–g), and on predicted lithological columns with fluid-type distributions for given vertical sections, presented in Fig. 9.18h with a step along the line of 50 m. On these materials, it can be seen that the development of the reservoir (its geometry) along the line is quite complex, not continuous, zonal, and lenticular. Judging by the results obtained, it can be stated that the oil-saturated intervals are distinguished mainly in the deposits Unayzah A, Unayzah B, and Unayzah C; they are relatively small in length and in vertical sizes. The intensity of their manifestation is different. From those oil- and gas-saturated intervals, which are quite well manifested in the section, it can be seen that they are concentrated mainly in formations that are good reservoirs. The following geological and geophysical parameters are characteristic of the noted productive interlayers: clay content, varying within 10–30%, porosity of Kp  10–20%, and oil and gas saturation coefficient of Ko  40–90%. Of particular interest is the section associated with the identified positive structure, confined to PK 955–1050 (Fig. 9.18g–h). Here in the sediments of the Unayzah B formation, the section is characterized by a lower content of clay material (Fig. 9.18d), increased porosity (Fig. 9.18e), and the highest oil and gas saturation (Fig. 9.18g–h). In addition to the predicted indicators, the structural-tectonic factor confidently manifests itself, which controls the preservation of the local accumulation of hydrocarbons (with possible location and the corresponding oil and gas accumulation zone). With a high probability, this prospective deposit can be considered as massive, flat sheet, and anticline. This part of the section, as the most perspective among the others, is of paramount importance for its subsequent study and testing in relation to the prospects of oil and gas potential.

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

9.3.4

403

Prediction of Geological Indicators in the Submeridian Direction

For the second of the selected seismic lines (7447), located in the research area in the submeridian direction, only part of the results is shown (Fig. 9.19). In addition to the CDP time section (Fig. 9.19a), obtained as a result of applying a special processing graph, the sections of effective acoustic impedance (Fig. 9.19b), the average oil and gas saturation values in the form of trace-by-trace values (Fig. 9.19c), and predicted lithologic-fluid columns for one of the selected sections (Fig. 9.19d) are also given. As in the section of the previous profile, the relatively small parts in sediments of the horizons Unayzah A, Unayzah B, partially Unayzah A siltstone, and Unayzah C are quite confidently distinguished by the extent and intensity of oil saturation. They are characterized by approximately the same geological and geophysical parameters—projected average values of clay and porosity, oil and gas saturation (5–17%), and oil and gas saturation coefficient (Ko  70–90%) (Fig. 9.19c, d). The obtained prediction results for this line with increased values of oil saturation (Fig. 9.19c) correspond to increased values of acoustic impedance (Fig. 9.19b). The same sections correspond to relatively lower clay values and higher predicted sandiness and porosity (which are not given here). Of particular interest here is the interval of the section located within the identified positive structure in the area of PK 575–710 (Fig. 9.19c, d). As can be seen from the presented figures, in the sediments of the Unayzah B formation, the interval is characterized by the highest oil and gas saturation. However, the distribution of this parameter turns out to be very uneven, discontinuous due to the reservoir thickness varying strongly in lateral. The structural-tectonic-predicting criterion, which controls the safety of the local hydrocarbon accumulation, also manifests itself here. Most likely this proposed object is a reservoir of structural and tectonically shielded type. However, compared with the priority object examined on line 7984, the perspective object (on line 7447) is less representative, primarily due to the expressed lithological heterogeneity of the Unayzah B productive horizon and, obviously, the complex structure of the reservoir pore space being traced as a whole in sediments of the Unayzah formation (Fig. 9.19d). In addition to the oil-saturated interval of the section associated with the anticlinal structure (in the area of PK 575–710), a number of other oil-saturated intervals located in the depressed, lowered part are fixed in the section of line 7447. Such areas, in particular, are distinguished in the sediments of the Unayzah A formation (in the area of PK 275–425), Unayzah B (in the area of PK 150–240), and Unayzah C (in the in the vicinity of PK 165–280) (Fig. 9.19c). Moreover, a positive structure with a relatively small amplitude is distinguished in the section of the profile near PK  225–250 in the context of almost all horizons. Obviously, the oil-saturated intervals of the section identified in this way correspond to traps that include both structural and lithologically shielded elements. The level of oil saturation in the vicinity of the depression, as well as within the local anticline structure (PK 575–710) is very high, reaching values of Ko  50–90%.

Fig. 9.19 Seismic data processing and interpretation results along the line 7447: (a) the fragment of the CDP time section as a result of special processing graph using (b) effective acoustic impedance (AI) section in the form of variable intensity; distribution of predicted data: (c) oil and gas saturation, and (d) predictive litho-stratigraphic columns for defined vertical sections of the data fragment

9.3 Composition and Property of Oil-Perspective Strata Prediction. . .

405

Fig. 9.19 (continued)

Thus, the processing and interpretation of materials obtained under rather complex seismic and geological conditions, which are characterized by significant lithological heterogeneity of the structure, the presence of low-porous reservoirs with a complex pore space structure that degrade the fluid-conductive properties of the rocks, made it possible to significantly supplement the available information on the oil prospects of the studied region, using the high-resolution seismic technique. The information obtained as a result of the research is aimed both at increasing the information content and effectiveness of seismic surveys in the area under consideration, and at the end, reducing the volume of unproductive drilling. This became possible on the basis of a complete exclusion from the analysis of the results of the seismic wave interference phenomenon (due to the application of the seismic inversion procedure of the initial seismograms and final time sections), increasing the resolution of the seismic data by an average of an order—up to the level of the sampling step of the seismic record in time—and based on a more in-depth automated integrated interpretation of land seismic and borehole observational materials.

9.3.5

Afterword

As a result of further studies (after the authors performed geological prediction on the materials of two seismic lines), the Lukoil Overseas oil company, which carried out exploration work in the eastern part of Block A on the Tukhman structure (Saudi Arabia (SA)), announced the discovery of a hydrocarbon field here with resources of more than 100 million tons of standard fuel (million tons of equivalent fuel) [www.lukoil-overseas.ru]. Then it was also announced the discovery of the Mushaib gas condensate field with recoverable reserves of 150 million tons of oil. In total, during the exploration period, nine exploratory wells were drilled on the block. In connection with the transition to the evaluation phase of open fields, 90% of the Block A territory was returned to the state fund, as a result of which the total area of the evaluation work of the Tukhman and Mushaib fields amounted to 2900 km2 (with the initial area of Block A about 30,000 km2, located in the southern part of the SA in the Rub’ al-Khali desert near the world’s largest Al-Ghawar oilfield).

406

9.4

9 Examples of HRS-Geo Technology Used in Other Regions

Summary

For other regions, the studies were carried out on the territory of the Timan-Pechora province, Western Siberia, and Saudi Arabia. On the territory of the Timan-Pechora province, the features of the structure and development of buried river systems (paleoriver incisions, boundaries of erosion sides, structures of internal sediment filling of paleochannels, etc.) are studied. It is shown how the structure of the interference wave field can easily confuse the studied “seismic image” of a promising object in the form of a buried river system (paleoriver incisions), with the proposed “biohermal construction.” Here it is important to ensure an adequate restoration of the seismic record dynamics by implementing a special graph for processing seismic data and to use the procedures developed by the authors that provide a solution to the IDSP. In the territory of Western Siberia, oil-promising objects have been identified in the sediments of the Jurassic complex and the pre-Jurassic base. According to the results of the research, ten sites are identified promising points represented on the sections of effective acoustic impedance and sections of the distribution of predicted oil saturation. Almost for each of them (the recommended well), the entire required set of necessary prediction geological indicators for each productive horizon is formed. In the seismogeological conditions of Saudi Arabia, an example of solving the oil geology problem using the HRS-Geo technology is given. The parts of the section in the sediments of the Unayzah A, Unayzah B, partially Unayzah A siltstone, and Unayzah C horizons are characterized by the predicted average values of porosity and oil and gas saturation (5–17%) and the oil and gas saturation coefficient (Kn  70–90%). With a high probability, this proposed deposit can be considered as massive, stratified, and arched. As a result of further research, a field of hydrocarbon raw materials with resources of more than 100 million tons of conventional fuel has been discovered here.

References 1. Teplov Ye, L., Kostygova, P. K., Larionova, Z. V. et al. (2011). Prirodnyye rezervuary neftegazonosnykh kompleksov Timano-Pechorskoy provintsii (Natural reservoirs of oil and gas complexes of the Timan-Pechora province). Ministry of Natural Resources and Environmental Protection of the Komi Republic, Timano-Pechorsk Scientific Research Center. p. 286. 2. Safonov, A. S., Kondrat'yeva, O. O., & Fedotova, O. V. (2011). Poisk neantiklinal'nykh lovushek uglevodorodov metodami seysmorazvedki (Search for non-anticlinal hydrocarbon traps using seismic exploration methods). M., Nauchnyy mir. p. 512. 3. Nesterov, I. I., & Shpil'man, V. I. (1987). Teoriya neftegazonakopleniya (Oil and gas accumulation theory) (p. 232). Nedra. 4. Milashin, V. A., Trofimov, V. L., & Khaziev, F. F. (2008). Vozmozhnosti prognozirovaniya sostava i svoystv nefteperspektivnykh tolshch s ispol'zovaniyem tekhnologii VRS-Geo

References

407

(Possibilities of predicting the composition and properties of oil-perspective strata using HRSGeo technology). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 16–24. 5. Trofimov, V. L., Khaziev, F. F., & Shkol'nik, S. A. (2014). Obnaruzheniye drevnikh ruslovykh sistem metodom vysokorazreshayushchey seysmiki (Ancient channel systems detection by the method of high-resolution seismic). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 51– 60. 6. Movshovich, E. B., Knepel, M. N., Nesmeyanova, L. I., & Pol'ster, L. A. (1981). Printsipy vyyavleniya zon fatsial'nogo kontrolya neftegazonakopleniya (Principles of identifying of facial control zones of oil and gas accumulation) (p. 268). Nauka. 7. Baraboshkin Ye, Y (2011). Prakticheskaya sedimentologiya. Terrigennyye rezervuary (Practical sedimentology. Terrigenous reservoirs). Posobiye po rabote s kernom (Core Handbook). M., Tver'. Publishing house GERS. p. 152 8. Muromtsev, V. S. (1984). Elektrometricheskaya geologiya peschanykh tel–litologicheskikh lovushek nefti i gaza (Electrometric geology of sand bodies—lithological traps of oil and gas) (p. 260). Nedra. 9. Trofimov, V. L., Khaziev, F. F., & Milashin, V. A. (2012). Dinamicheskiye kharakteristiki otrazhennykh voln s uchetom vklada elementarnykh granits i tolshch (Dynamic characteristics of reflected waves taking into account the contribution of elementary boundaries and strata). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 12–24. 10. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., et al. (2009). Spetsial'naya obrabotka i interpretatsiya dannykh seysmicheskikh nablyudeniy v slozhnykh geologicheskikh usloviyakh metodom vyokorazreshayushchey seysmiki (Special processing and interpretation of seismic observation data in difficult geological conditions by the method of high-resolution seismics). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 36–50. 11. Trofimov, V. L., Khaziev, F. F., Milashin, V. A., et al. (2007). Avtomatizirovannaya obrabotka i interpretatsiya dannykh GIS dlya obnaruzheniya nefteperspektivnykh obyektov metodami vysokorazreshayushchey seysmiki (Automated processing and interpretation of well logging data for the detection of oil-prospective objects by high-resolution seismic methods). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 54–66. 12. Khaziev, F. F., Trofimov, V. L., & Milashin, V. A. (2008). Opredeleniye geologogeofizicheskikh parametrov real'noy sredy metodom vysokorazreshayushchey seysmiki (Determination of geological and geophysical parameters of the real medium by the high-resolution seismic method). Tekhnologii seysmorazvedki (Seismic Technologies), 2, 25–30. 13. Trofimov, V. L., Milashin, V. A., Khaziev, F. F., et al. (2008). Izucheniye stroyeniya i otsenka obstanovok osadkonakopleniya tonkosloistoy real'noy sredy metodami VRS-Geo i GIS (Study of the structure and assessment of sedimentation environments of a thin-layered real medium using HRS-Geo and GBS). Tekhnologii seysmorazvedki (Seismic Technologies), 3, 57–67. 14. Zalyayev, N. Z. (1981). Kompleksnaya interpretatsiya geofizicheskikh parametrov funktsional'nymi preobrazovaniyami s pomoshch'yu EVM (Complex interpretation of geophysical parameters by functional transformations using a computer) (p. 150). BelNIGRI. 15. Zalyayev, N. Z. (1990). Metodika avtomatizirovannoy interpretatsii geofizicheskikh issledovaniy skvazhin (Technique of automated interpretation of well logging). Minsk. University Publishing House. p. 142. 16. Khaziev, F. F., Trofimov, V. L., Milashin, V. A., & Mal'tsev, G. A. (2007). Perspektivy optimal'nogo razmeshcheniya geologorazvedochnykh rabot s primeneniyem seysmicheskikh dannykh vysokogo razresheniya (Prospects for the optimal location of exploration works using high-resolution seismic data). Geofizika (Geophysics), 4, 167–175. 17. Trofimov, V., Khaziev, F., & Trofimova, A. (2018). Tekhnologiya VRS-Geo. Izucheniye nefteperspektivnykh obyektov metodom vysokorazreshayushchey seysmiki (HRS-Geo technology. Study of oil-prospective objects by the method of high-resolution seismic). Oil & Gas Journal Russia, 1–2(123), 28–35.

Conclusion

1. A significant increase in the structural detail of oil and gas deposits reservoirs is ensured through the use of program-methodical implementation of the highresolution seismic technique realized in the form of HRS-Geo technology, which is based on a numerical method for solving the inverse dynamic seismic problem, which provides for the separation of seismic information related to source of elastic oscillations shooting and the studied geological medium. The study of real media is focused on the research of the seismic record dynamic parameters, and the achievement of the maximum possible resolution of these parameters. In particular, the construction of 2D sections and 3D cubes of effective acoustic impedance (AI) and reflection coefficients (RC), with a vertical resolution equal to the sampling step of the seismic recording in time, is being implemented. The developed technology is most in demand for study of a thin-layer geological section, including weak in intensity reflected waves that respond to low-contrast acoustic heterogeneities of the geological medium (depending on the reflectivities varying along the section). 2. The concepts of an elementary layer and an elementary boundary are introduced for unambiguous determination of a model of a thin-layer geoacoustic medium in the description, development, and use of technology for obtaining seismic data with high spatial resolution. An analysis of the information content of thin-layer section elements is carried out by quantifying the contribution of elementary boundaries and strata to the interference wave field. In connection with the development and introduction of the concept of “interference contribution matrix” and with its application, the contributions of local responses from lithology, porosity, and oil and gas saturation to the seismic wave field and to the results of its various transformations are estimated.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2

409

410

Conclusion

According to the results of seismic modeling, it is established that lithology is characterized by the strongest influence—its contribution to interference seismic recording is in the range of 60–80%, sometimes reaching 90%. The effect of porosity on the seismic record and its pseudo-acoustic transformations (PAL) is estimated on average to be in the range of 10–30%. The influence of oil saturation on the record dynamics compared with lithology and porosity is significantly lower and averages 10–15%. 3. The rationale for the solution of the inverse dynamic seismic problem in the classical setting is given, determining the conditions of existence, uniqueness, and stability of the solution. By iteratively testing a model with different parameters, the area of solution existence is determined. If an instability of the solution is detected during the inversion, the algorithm is tuned to obtain an approximate solution through regularization. Some general concepts for solving the inverse dynamic seismic problem (IDSP) are analyzed, affecting this area of research to the extent that it concerns the author’s development—HRS-Geo technology. The inverse problem is formulated as follows: a field is set outside the medium under study or on some part of this medium, the class of models within which the solution is searched, the noise characteristics, the restrictions on the solution; it is required to determine (evaluate) the characteristics of the medium. The seismic data inversion problem in numerous works is considered as deterministic or geostatistical (stochastic). The result of deterministic inversion is the only model of elastic properties that satisfies seismic data and a priori constraints, and the result of stochastic inversion is a set of equally probable realizations of the distribution of elastic and discrete properties in a real medium model. Examples are given, and the features of the application of various inversion transformations based on the use of specialized software packages and individual developments on the interpretation of the geological models prediction results are highlighted. 4. The topic of hydrocarbon indication based on the use of dynamic parameters of seismic record is still relevant, but at the same time, it has not been fully studied, and its widespread use is not noted in practice. The results obtained using various approaches to the interpretation of seismic data justify the presence of a certain contribution of the real medium parameters to the kinematic and dynamic characteristics of the seismic record. However, such approaches have their own features and disadvantages that make the prediction results ambiguous. One of the main disadvantages of this is the use of interference wave fields as initial data in solving the inverse problem. 5. The program-methodical implementation of the high-resolution seismic method and the technique of its application are made in the form of HRS-Geo technology. The IDSP solution is found by the optimization method, which consists in the selection of the AI and RC models for a given structure of the wave field (WF) using the known formulas for solving the direct problem for calculating the seismic wave field. In this case, the convolution model algorithm is used, in which it is possible to take into account the noise level, the residual background

Conclusion

411

of multiple waves, and the regularization factor. To find a solution, the direct search method is used—the zero order (Hooke-Jeeves method) which does not require the calculation of the derivatives of the objective function. In the process of solving the inverse dynamic problem, an important role is assigned to the stage of determining the shape of the initial seismic signal. To optimize the solution, a system of objective functions (an objective function vector) has been developed in which various types of discrepancies between real and model data are iteratively calculated. A feature of this system is that it calculates and analyzes the residuals in energy and in the form of signals separately and makes a decision on optimizing the model as a whole. The joint use of the characteristics makes it possible to extract the distribution of the medium properties with the maximum detail in the process of stable inversion of wave field data. The results of solving the inverse dynamic problem are presented on test and real materials using software tools of the HRS-Geo technology. The real possibilities of the seismic wave field inversion procedure at certain stages of technology development are shown. In addition, during the process of the technology developing, a new important type of noise is introduced into the noises classification - wave interference, which is the main obstacle to achieve the most seismic data resolution. Methods of suppressing such noise in the HRS-Geo technology are in detail discussed. 6. Processing and interpretation of data from geophysical borehole surveys (GBS) and vertical seismic profiling (VSP) in vertical sections of deep wells is performed. Processing and automated interpretation of these standard well logging (GBS) methods is carried out by the method of functional transformations of geophysical parameters and by integrating them into information systems. This ensures the continuity of data processing and analysis, a quantitative coordination of all geophysical parameters based on their genetic relationships. Using the set of these parameters located in a specific system, the material composition, porosity, bound water content, and useful reservoir capacity are determined; oil and gas saturation of the reservoirs is estimated; and a number of physical characteristics of the geological section are calculated. On this basis, lithological-stratigraphic columns and detailed geoacoustic models are constructed in vertical intervals of the well section. The processing of vertical seismic profiling data (VSP and PM VSP) is carried out on the basis of using SKOR software to determine the velocity characteristics of various wave types (longitudinal, transverse, exchange), effective elastic deformation parameters, and the stress state of a real medium under natural conditions (in situ) occurrence of rock mass. 7. Further improvement of the technology seems possible to carry out on the basis of the development and integrated use of research directions such as studying the velocity and elastic-deformation characteristics of the section using VSP data, velocity fields and velocity gradients of elastic waves for heterogeneous geological media, and determining multidimensional relationships between seismic and field geophysical characteristics of the section. Such areas of research have been tested by the authors on numerous real borehole,land, and

412

Conclusion

marine seismic materials with deep theoretical, methodological, and experimental studies. 8. A special system of petrophysical equations is developed to determine the lithological composition and nature of reservoir rocks fluid saturation. With an integrated approach to the analysis of the results obtained, the maximum possible amount of reliable geological and geophysical information is extracted from seismic data for the purpose of searching for perspective oil and gas objects, evaluating their characteristics for calculating hydrocarbon reserves and resources, optimal placement of exploration and production wells, and, in general, to justify the development strategy of oil industry. Before directly predicting the desired parameters, the studied section interval is compared with the physical parameters previously obtained for this section interval from the GBS data, and then, at a given lateral direction in the plane of the seismic section (or 3D seismic cube) for each point of the target section interval (layer), the required geological and geophysical indicators are determined. 9. Using the high-resolution seismic method, the results of a comprehensive interpretation are obtained that characterize the detailed features of the geological structure in various seismic and geological conditions in different regions. In hard geological conditions, this is a prediction of the lithological composition and reservoir properties of rocks. Under favorable seismogeological conditions, the nature and degree of reservoir rock fluid saturation is determined. Under the conditions of the Volga-Ural oil and gas province, the prospects of oil and gas are largely associated with deposits of terrigenous Devonian, which is confirmed by the presence of a significant number of oil and gas fields on it. In other regions, examples of integrated interpretation using HRS-Geo technology are given for areas of the Timan-Pechora province, Western Siberia, and Saudi Arabia. Due to the in-depth comprehensive interpretation of the HRS-Geo technology, well logging, and drilling data, a number of new perspective areas in reservoirs have been identified; the structure of the geological medium, oil contours, and oil prospects of a real section have been significantly refined. 10. In conclusion, it should be emphasized that, based on the application of HRS-Geo technology, seismic modeling is implemented with an assessment of the elementary boundaries and strata contribution, which allows a quantitative assessment of the geological indicator contribution to the interference seismic record and the results of its various transformations; integrated graph for processing seismic data, ensuring the preservation of the initial dynamics of seismic records; and solving the inverse dynamic seismic problem by a numerical method, which allows extracting information about the real geological medium from 2D interference waveforms in the form of 2D sections and 3D cubes of effective acoustic impedance (AI) and reflection coefficients (RC). Two-dimensional sections and 3D AI and RC cubes have a vertical resolution equal to the time discretization of seismic recordings and allow for detailed interpretation of seismic data based on the study of the composition and properties of productive deposits and the formation of a geological model of oil-perspective objects (buried paleochannels, rifogenic structures, stratigraphic

Conclusion

413

disconformity, layer wedging, etc.). A study is being made on the sedimentation (deposition) conditions by the methods of classification and prediction of various facies groups in order to identify zones of facies substitution and heterogeneity zones of a thin-layer section; clarification of the oil and gas deposit boundaries along the external contours of oil and gas; in-depth comprehensive geological and geophysical interpretation of the results of processing geophysical materials using high-resolution, GBS, and drilling data; and preliminary estimation of hydrocarbon resources from high-resolution seismic data with constant and variable calculation parameters. All stages of the interpretation are not implemented on time sections and cubes of the wave field, but on detailed sections and cubes of geological medium parameters.

Index

A Acoustic deterministic inversion, 73 Acoustic heterogeneities, 17, 34 Acoustic impedance, 237 Acoustic inhomogeneitie, 17 Acoustic parameters, 6 Additive linearized model, 270

B Borehole seismic observations, 215

C Carboniferous deposits, 294 CDP seismogram, 237, 239 Cenozoic sediments, 245 CMP method, 10 Computational mathematics, 52 Conventional vertical hodographs, 240 CORREL program, 216 Correlation coefficients matrix, 266 Correlation matrix, 263 Cylindrical coordinate system, 54

D Data inversion versions, 133 Deep seismic sounding (DSS) data, 238 Dynamic data processing, 268

E Elastic wave propagation, 238 Environmental parameters, 4

F Famennian Devonian stage, 362 Famennian Upper Devonian stage, 370 Filtration-capacitive properties (FCP), 58, 203

G Geological indicators, 3, 9, 35, 52, 267 Geological indicators interference, 16 Geological parameters lithological component, 35, 40 oil saturation, 42, 43, 46 porosity, effect of, 41 water saturation, 42 Geophysical borehole survey (GBS) data, 35, 411 Geophysical parameters, 4, 9 Geophysical well survey (GBS) data, 253 Geostatistical inversion, 63, 64, 67, 73

H High-resolution seismic data, 4 High-resolution seismic method, 412 High-resolution seismics, 6 High-resolution seismic techniques, 2

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 V. L. Trofimov et al., Oil and Gas Reservoir Prospecting and Exploration, https://doi.org/10.1007/978-3-030-84389-2

415

416 High-resolution seismic technology (HRS-Geo), 5 Hooke-Jeeves method, 103 Horizontal-layered models, 237 HRS-Geo technology, 8, 9, 11, 12, 126 noise suppression (attenuation), 179, 180 HRS-Geo technology programs, 268 Hydrocarbon deposits, 2 Hydrocarbon indication, 3 Hydrocarbons indication characteristics, 75 fluid nature determination, 78, 80, 81, 83, 85–87 hydrocarbons in structural and lithological traps, 75 porosity determination, 77 reflection coefficient ratio determination, 78 terrigenous rock density determination, 77 velocity determination, 76

I Individual dependent variables, 262 Integral geometry method, 238 Interference contribution matrix (ICM), 7, 33 forming field characteristics, 20 interference seismic field, 21 pseudo-acoustic logging, 21 reservoir parameter, 20 VKSYNT program, 21 Inverse dynamic seismic problem (IDSP), 52 computational schemes, 101 deterministic search method, 103 direct optimization methods, 102 Gdata, 139, 142, 143 HRS-Geo Technology, 109 isotropic medium, 99 JDow, 143, 145, 146 KMGph, 148, 149 MU, 149, 152 objective function, 100, 102 optimization method, 103, 105, 106, 109 Prdgm, 152, 154 PtrAlnce, 155 real and computed seismic traces, 102 real seismic data, 120, 122, 123, 125, 126 SbNGph, 160 special processing, 126, 136, 137 stochastic method, 103 Svginf, 163

Index SvMGph, 159, 160 test and real data, 110 TNGph, 165 trace inversion algorithm, 111, 114, 120 wave propagation physics, 98 YtGph, 168, 170, 174 Inverse problems computational resources, 53 cylindrical coordinate system, 54 direct problems, 57 modern mathematical and numerical methods, 53 Inverse seismic problems, 238 Inversion process, 52 Inversion technologies AVO/AVA inversion algorithm, 61 elastic parameters distribution, 58 geostatistical inversion, 63, 64, 67 neural- network method, 67, 69, 71 Inversion technologies acoustic deterministic inversion, 59, 60

L Lagrange polynomial, 241 Linear multiple regression, 264 Lithological column, 35 Lithological component seismic trace lithology content, 40 Lithological composition, 6 Lithological-petrographic parameters, 203 Longitudinal seismic waves, 2

M Mesozoic sediments, 245 Multidimensional dependence determination experimental distribution curve, 259 geological and geophysical parameters, 252 geological informativeness, 252 geological property influence, 253 geological section, 252 parameters, 252 thermobaric environment, 255 variables, 259 Multidimensional dependencies, 255, 262 Multiple regression coefficients, 261 Multivariate regression, 11 Multivariate statistical data analysis, 255

Index N Neural networks, 74

O Oil-and gas-saturated thicknesses, 384 Oil-saturated intervals, 403

P Partial correlation, 10 Petrophysical equation system elastic wave propagation, 269 GBS data, 268 heterogeneities, 268 hydrocarbons, 277 linear petrophysical equations, 271 lithological composition, 270 material composition, 270 oil and gas saturation, 274 oil saturation, 275, 276 prospective sediments, 268 quantitative assessment, 271 seismic data, 268, 269 seismic survey, 279 Petrophysical equations, 412 Petrophysical method, 203 Polarization method (PM), 225 Porosity determination, 201, 202 PRECIS program, 215 Prognosis geological section (PGS), 253 Program-methodical implementation, 410 Pseudo-acoustic transformations (PAL), 33, 410

Q Quantitative assessment, 271

R Ray orthogonality condition, 240 Reflected waves boundaries and layers, 24, 28 boundaries and strata, 28, 30, 32 interference contributions matrix, 20, 21 pseudo-acoustic logging, 21, 22 Regression equations, 260, 263, 264 Regression programs, 259 Regularization, 52 Reservoir properties, 6 Reservoir rock fluid saturation, 5 Riccati equation system, 53

417 S Saudi Arabia acoustic impedance (AI), 397 geological structure, 392 Lukoil Overseas oil company, 405 OMEGA system, 392 perspective and productive deposits, 403 processing and automated interpretation of geophysical research materials in wells, 395 seismic wave process, 397 sonic logging data (SN), 397 submeridian direction, 404 Unayzah formation, 402 2D seismic data, 395 Seismic data, 198, 269 Seismic data inversion problem, 410 Seismic data lateral resolution, 131 Seismic data processing, 133, 134 HRS-Geo technology, 176–179 special graph, 180–182, 184, 186, 188, 190 Seismic data vertical resolution and informativity, 130 Seismic exploration method, 214 Seismic interference, 6 Seismic method, 2 Seismic recording, 7 Seismic records, 412 Seismic sampling, 34 Seismic wave field GBS methods, 16 geological indicators, 16 Seismic wave fields, 2 Self-organizing neural networks, 72 Signal-to-noise ratio, 131 SKOR software, 4, 9, 219, 411 Sonic measurements (SN), 215 Spline interpolation, 241 Statistical analysis, 264 Stratimagic software package, 72 Synchronous deterministic inversion, 73

T Tectonic faults, 247 Timan-Pechora province, 360, 412 AI and RC sections, 362 buried river systems, 370, 371 CDP time sections, 361, 370 HRS-Geo technology, 368 oil and gas potential, 369 paleo-incision channel, 370 seismic survey methods, 360

418 Timan-Pechora province (cont.) structural features, 360 sublatitudinal direction, 368 3D(S) coherence map, 369 Two-dimensional heterogeneous models, 238

U Upper Devonian, 362

V VELOC program, 217 Velocity analysis, 239 Velocity distribution, 240 Vertical distribution, 35 Vertical seismic profiling (VSP), 4, 9, 231, 411 Volga-Ural province crystalline basement, 286 Devonian terrigenous complex, 287 interpretation results, 284 Kynov and Pashiisk horizons, 290 Burreg period, 303 filtration-capacitance parameters, 291 geological structure, 293 lower level of saturation, 302 oil saturation map, 297 oil-saturated intervals, 296 Onbysk oilfield, 304 optimal distribution, 302 predictive clay content distribution, 294 predictive values of clay content, 301 TATECH” experts, 303 Kynov horizon, 288 Kynovian horizon, 287 Mullin horizon, 287 oil and gas province (OGP), 285 Pashiisk horizon, 288 Samara region, 332 Ardatov horizon, 351, 353 automated processing and interpretation, 333 Bashkirian stage, 333 Bobrikov horizon, 334 clayization, 340 crystalline basement, 343 interpretation process, 337 Kynov horizon, 345, 348 oil saturation contours, 353, 354 Pashiisk horizon, 349, 351 porosity distribution, 340

Index seismic data preprocessing, 337 structural constructions, 341 structural features, 344 terrigenous Devonian stratum, 335 Tournaisian stage, 335 Starooskol horizon, 287 terrigenous Devonian, 291 crystalline basement, 296 deposits, 291 geological structure, 291 Tournaisian-Kynov stratum, 288 upper and middle Devonian deposits, 306 Bashkirian stage, 314 Bobrikov horizon, 316 Buzuluk depression, 306 Carbonate deposits, 309 crystalline basement, 308, 314 identified objects, 330 oil saturation contours, 322, 328 Pashiisk horizon, 316 predicted hydrocarbon distribution density, 322 predicted porosity distribution, 313 prediction results, 313 processing and automated interpretation, 306 sonic logging (SN), 309 Volga-Ural Province, 12

W Wave field (WF), 98 Wave field decomposition, 18 Wave propagation velocities, 214 Well logging data geological and geophysical processes, 223, 224, 226, 228 lithological composition determination, 200, 201 oil and gas saturation determination, 202 porosity determination, 201, 202 pre-jurassic basement sediments, 205, 207 productive sediment saturation, 209, 211, 213, 214 reservoir filtration properties, 202–204 velocity and elastic-deformation characteristic determination, 214, 216, 218–220, 222 West Siberian Province automated processing and interpretation, 372

Index clayiness distribution, 383 geological substance, 384 oil-saturated areas, 381 porosity distribution, 383 predicted indicators, 389 predicting geological and geophysical parameter, 373

419 pre-Jurassic basement, 385, 388 pre-Jurassic sediments, 377 qualitative geological interpretation, 378 structural constructions, 380 tectonic fault polygons, 378 Tyumen formation, 372