Dynamics of Hydrocarbon Migration: Quantitative Dynamics Studies and Applications 9811955336, 9789811955334

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Xiaorong Luo · Likuan Zhang · Yuhong Lei · Wan Yang

Dynamics of Hydrocarbon Migration Quantitative Dynamics Studies and Applications

Dynamics of Hydrocarbon Migration

Xiaorong Luo · Likuan Zhang · Yuhong Lei · Wan Yang

Dynamics of Hydrocarbon Migration Quantitative Dynamics Studies and Applications

Xiaorong Luo Institute of Geology and Geophysics Chinese Academy of Sciences Beijing, China

Likuan Zhang Institute of Geology and Geophysics Chinese Academy of Sciences Beijing, China

Yuhong Lei Institute of Geology and Geophysics Chinese Academy of Sciences Beijing, China

Wan Yang Geology and Geophysics Program Missouri University of Science and Technology Rolla, MO, USA

ISBN 978-981-19-5533-4 ISBN 978-981-19-5534-1 (eBook) https://doi.org/10.1007/978-981-19-5534-1 Jointly published with Science Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Science Press. ISBN of the Co-Publisher’s edition: 978-7-03-074438-8 © Science Press and Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are reserved by the Publishers, whether the whole or part of the material is concerned, specifically the rights of 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 publishers, 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 publishers 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 publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Secondary Hydrocarbon Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Basic Concepts of Hydrocarbon Migration . . . . . . . . . . . . . . . 1.1.2 Driving Forces of Hydrocarbon Migration . . . . . . . . . . . . . . . 1.1.3 Conduits of Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Characteristics and Forming Mechanisms of Migration Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.5 Dynamic Model of Hydrocarbon Migration and Accumulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.6 Tracing and Dating Hydrocarbon Migration . . . . . . . . . . . . . . 1.2 Geodynamics and Methods of Quantitative Analysis . . . . . . . . . . . . . 1.2.1 Geodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Development of a Geological Model . . . . . . . . . . . . . . . . . . . . 1.2.3 Methods of Quantitative Research . . . . . . . . . . . . . . . . . . . . . . 1.3 Dynamics in Hydrocarbon Migration and Accumulation . . . . . . . . . 1.3.1 Development and Application of Migration and Accumulation Dynamics in Petroleum Geology . . . . . . . 1.3.2 Concept of Hydrocarbon Migration-Accumulation Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Aspects and Methodology of Hydrocarbon Migration-Accumulation Dynamics Studies . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Mechanisms and Processes of Secondary Migration of Oil and Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Physical Experiments of Secondary Petroleum Migration . . . . . . . . . 2.1.1 Experiment Setup and Observation System . . . . . . . . . . . . . . 2.1.2 Formation of Migration Pathways . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Remobilization in Migration Pathways . . . . . . . . . . . . . . . . . . 2.1.4 Pathway Saturation and Oil Saturation in Pathway . . . . . . . .

1 1 2 3 7 15 18 21 25 25 26 27 33 33 36 42 48 63 63 64 70 85 89

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2.1.5 Oil Migration in a Single Fracture . . . . . . . . . . . . . . . . . . . . . . 2.2 Mechanisms and Processes of Secondary Migration of Hydrocarbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Dynamic Characterization of Hydrocarbon Migration and Pathway Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Mechanisms and Processes of Pathway Formation . . . . . . . . 2.3 Numerical Simulations of Secondary Hydrocarbon Migration . . . . . 2.3.1 MigMOD and Its Applicability . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Simulation Using Conceptual Geological Models . . . . . . . . . 2.3.3 Heterogeneity of Hydrocarbon Migration Pathways and Influencing Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 A Case Study on Hydrocarbon Migration in Middle Jurassic of Paris Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Hydrocarbon Conduit System and Its Quantitative Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Sandstone Carrier Beds and Their Characterization . . . . . . . . . . . . . . 3.1.1 Concept of Carrier Bed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Delineation of Carrier Beds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Development of Carrier Bed Models . . . . . . . . . . . . . . . . . . . . 3.1.4 Geometric Connectivity of Carrier Beds . . . . . . . . . . . . . . . . . 3.1.5 Fluid Connectivity of Carrier Beds . . . . . . . . . . . . . . . . . . . . . 3.1.6 Quantitative Characterization of Conductivity of Carrier Bed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Quantitative Characterization of Fault Carriers . . . . . . . . . . . . . . . . . . 3.2.1 Geological Factors Affecting Fault Opening and Sealing . . . 3.2.2 Principles and Model of Quantitative Research on Fault Opening and Sealing . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Concept of Fault-Connectivity Probability . . . . . . . . . . . . . . . 3.2.4 Effectiveness Evaluation of Parameters for Characterizing Fault Opening and Sealing . . . . . . . . . . . . 3.2.5 Quantitative Characterization of Fault Carrier . . . . . . . . . . . . 3.3 Carriers Associated to Unconformity . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Characteristics of Unconformities as Migration Conduits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Spatial Distribution of Unconformity-Related Carrier Beds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Model of Unconformity-Related Carriers . . . . . . . . . . . . . . . . 3.4 Development and Quantitative Characterization of a Composite Hydrocarbon Conduit Framework . . . . . . . . . . . . . . . 3.4.1 Principles of Construction of a Composite Conduit System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Method to Establish a Composite Conduit Framework . . . . .

102 106 107 109 121 122 127 132 141 145 151 152 153 155 156 158 160 164 166 168 170 176 178 189 195 195 199 211 213 213 215

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3.4.3 Quantitative Characterization of Composite Conduit Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 4 Quantitative Evaluation Method of Hydrocarbon Migration and Accumulation Efficiency and Resource Distribution . . . . . . . . . . . 4.1 Primary Hydrocarbon Migration and Estimation of Hydrocarbon Expulsion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Dynamic Condition Analysis of Hydrocarbon Primary Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Estimation of Hydrocarbon Expulsion from Source Rocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Estimation Method of Hydrocarbon Losses During Secondary Hydrocarbon Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Model of Hydrocarbon Secondary Migration Process . . . . . . 4.2.2 Proportion of Hydrocarbon Migration Pathway to Migration Conduit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Estimation Model of Losses During Secondary Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Estimation of Non-Commercial Hydrocarbon Accumulation Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Calculation Principle and Method . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Examples of Non-Commercial Accumulation Amount Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Quantitative Evaluation of Hydrocarbon Resources and Spatial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Material Balance Method for Hydrocarbon Resources Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Realization of Hydrocarbon Migration and Accumulation Evaluation in Migration Pathway . . . . . . 4.4.3 Method Process of Hydrocarbon Migration and Accumulation Eff Efficiency and Resource Distribution Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Applicability Test of Research Methods . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Study of the Hydrocarbon Migration and Accumulation Dynamics in the Eastern Part of the Southern Slope of the Dongying Sag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Characteristics of Geological Elements of Reservoir Formation . . . 5.1.1 Tectonic Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Characteristics of Stratigraphy . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Petroleum Geological Conditions . . . . . . . . . . . . . . . . . . . . . . . 5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

231 232 232 242 254 255 257 264 265 268 269 270 274 274 277

279 280 286

293 293 294 295 301 305

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5.2.1 5.2.2 5.2.3 5.2.4

Establishment of the Basin Geological Model . . . . . . . . . . . . Modelling of the Evolution of Pressure System . . . . . . . . . . . Evolution History of Organic Matter Maturity . . . . . . . . . . . . The Hydrocarbon Expulsion History of the Source Rocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Hydrocarbon Migration and Accumulation Period and Petroleum Migration-Accumulation Systems . . . . . . . . . . . . . . . 5.3.1 Chronological Analysis of Hydrocarbon Migration and Accumulation Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Determination of Hydrocarbon Migration and Accumulation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Establishment and Quantitative Characterization of the Carrier System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Quantitative Characterization of the Sandstone Carrier Bed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Quantitative Characterization of the Capability of Fault Carrier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Establishment of Composite Carrier System . . . . . . . . . . . . . 5.5 Evaluation of Hydrocarbon Migration and Accumulation Efficiency and Prediction of Favorable Area . . . . . . . . . . . . . . . . . . . . 5.5.1 Estimation of Hydrocarbon Loss During Migration and Accmulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Evaluation of Hydrocarbon Resource . . . . . . . . . . . . . . . . . . . 5.5.3 Distribution of Hydrocarbon Migration Pathways and Migration and Accumulation Amount . . . . . . . . . . . . . . . 5.5.4 Prediction and Evaluation of Favorable Exploration Target Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

305 314 322 324 328 328 335 338 339 349 367 375 375 381 383 385 387

Chapter 1

Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

Hydrocarbons were generated from a source rock and moved into a carrier bed during primary migration, then moved further along the carrier bed during secondary migration, and finally accumulated in a trap where the driving forces for migration reached equilibrium with resistant forces (England et al., 1987; Hobson, 1954, 1997; Magoon & Dow, 1992). Hydrocarbon migration reflects the essence of petroleum as a natural fluid and the dynamic nature of the process and, thus, is the core in the study of petroleum geology, especially, dynamics of hydrocarbon accumulation (Leverson, 1967; Li, 2013). Understanding the process of migration is also imperative to petroleum exploration and production (Chen, 1991; Luo, 2003). Migration is a complex process and controlled by many factors of dynamics (Hunt, 1996; Li, 2003). It had occurred in the subsurface in the geological past and cannot be directly observed and, in many cases, left little evidence in the geologic record (Catalan et al., 1992; Dembicki & Anderson, 1989; Schowealter, 1979). As a result, it is the least understood process in petroleum geology. This chapter provides an overview of the quantitative results of previous studies on hydrocarbon migration, accumulation, and dissipation and associated dynamics, analyzes the characteristics of migration pathways and related forces, presents an understanding of integrated processes of migration, accumulation, and dissipation and, finally, discusses the methodology and approach in hydrocarbon migration research in petroliferous sedimentary basins.

1.1 Secondary Hydrocarbon Migration The research on hydrocarbon migration and accumulation involves multiple disciplines and has been pursued by many scientists over the years (e.g., Chen, 1988; Hobson, 1954, 1997; Hunt, 1990; Jin & Zhang, 2005; Li, 2013; Luo, 2003). Progresses have been made in several areas, including migration dynamics, hydrocarbon carrier systems, dynamic mechanisms of migration and accumulation, dating © Science Press and Springer Nature Singapore Pte Ltd. 2023 X. Luo et al., Dynamics of Hydrocarbon Migration, https://doi.org/10.1007/978-981-19-5534-1_1

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

of hydrocarbon charging into reservoirs, and geochemical tracing. They serve as the foundation for future studies on the dynamics of hydrocarbon migration processes.

1.1.1 Basic Concepts of Hydrocarbon Migration Hydrocarbon movement in carrier beds or reservoirs are generally termed secondary migration (Allen & Allen, 1990; Hobson & Tiratsoo, 1981; Li, 2013; Mann et al., 1997). This definition implies that, after the primary hydrocarbon migration out of the source rocks, the environmental and dynamic conditions for migration remain relatively consistent (Chen, 1988; Hunt, 1990; Li, 2013; Zhang et al., 2000). In general, the phase of hydrocarbon during primary migration is the same as that during secondary migration (Li, 2003), and is dominantly free (Mann et al., 1997; Ungerer et al., 1984, 1990). Oil migration in a water-soluble phase is inefficient because of the low solubility of oil in formation water (Hunt, 1996; McAuliffe, 1979). In addition, oil migrates very slowly in a diffusion phase and cannot form significant accumulations (Leythaeuser et al., 1982). The phase of natural gas migration is likely more diverse than that of oil (Chen, 1986). The density difference between natural gas and water is much greater than that between oil and water; and under the subsurface conditions, the gas–water and oil–water interfacial tensions are similar (Li, 2013). Thus, natural gas also migrates mainly as a free phase. Natural gas diffusion and dissolution in water are important during migration and accumulation (Hao et al., 1991, 1994). Natural gas can migrate in a soluble phase in oil, because its solubility in oil is great and the gas can be separated from oil under favorable temperature and pressure conditions (Larter & Mills, 1991). The generation, migration, accumulation, and later dissipation of hydrocarbons as fluid all have occurred in the water-saturated space, including pores, fractures, and dissolution cavities (Zhang et al., 2000). Hydrocarbon tends to move in strata. Its mode, location, and their changes are determined by the equilibrium among hydrodynamic force, capillary force, and buoyancy (Berg, 1975; England et al., 1987; Hubbert, 1953; Schowalter, 1979; Wang & Chen, 1999). Dynamically, hydrocarbon accumulates generally in a place, namely a trap, where the driving and resistant forces reach equilibrium. Hydrocarbon accumulation and dissipation are special cases in the realm of migration under specific hydrodynamic and fluid flow conditions (Luo, 2008). Secondary migration conduits can be dissolution vugs and cavities, fractures, fault zones, and unconformities, in addition to pore space (Leverson, 1967). The migration can occur laterally along carrier beds or unconformities or vertically along fault zones, depending on the equilibrium of migration forces and characteristics of conduits (Hobson & Tiratsoo, 1981; Zhang & Zhang, 1989). The vertical migration distance can be up to several thousand meters, depending on the thickness of basin fill and fault length (Hao et al., 2004; Xie et al., 1997). Laterally, the distance can be 10 s, even 100 s of kilometers if the amount of hydrocarbon is large and migration conduits are continuous (Allen & Allen, 1990).

1.1 Secondary Hydrocarbon Migration

3

Hydrocarbon migration is extremely non-uniform (Berg, 1975; Dembicki & Anderson, 1989; Harms, 1966; Luo, 2011; Luo et al., 2015; McNeal, 1961; Schowalter, 1979; Smith, 1966; Zhang et al., 2003a). The rock bodies that serve or contain migration conduits are commonly heterogeneous and discontinuous, such as spatial variations in lithology, physical properties of carrier beds and reservoirs (Schowalter, 1979), and partitions and connections related to faults (Allan, 1989; Hobson, 1954). Hydrocarbon migrates along some so-called dominant paths where the differences between driving and resistant forces are larger; and only the traps located along those paths can accumulate hydrocarbons (Hao et al., 2000). Migration only occurs in a part of a conduit, even though the conduit is homogenous (Catalan et al., 1992; Dembicki & Anderson, 1989; Luo, 2011), accounting for 1– 10% in volume of the conduit (Schowalter, 1979). Migration pathways are highly complex in heterogeneous conduits (Luo et al., 2015, 2016b), and may occupy a large portion of the conduit (Karlsen & Skeie, 2006), which, in some cases, can be regarded as accumulations (Luo et al., 2015). In laboratories, hydrocarbon flow in porous media is fast, about 10 cm/h (Catalan et al., 1992; Dembicki & Anderson, 1989; Luo et al., 2004; Schowalter, 1979; Vasseur et al., 2013). The main factor controlling the flow velocity is the hydrocarbon supply rate from the source, in addition to the difference between driving and resistant forces and the transmissivity of the conduits during the formation of commercial accumulations under geological conditions. The entire hydrocarbon migration in a sedimentary basin is a composite process composed of infinite episodes of physical movement over a long period of geological time (Luo et al., 2012; Zhang et al., 2010).

1.1.2 Driving Forces of Hydrocarbon Migration The migration of hydrocarbons in free phase state in the conduits is determined by the equilibrium among hydrodynamic, capillary, and buoyancy force (Berg, 1975; England et al., 1987; Hubbert, 1953; Schowalter, 1979; Tao, 1993). Buoyancy force is always a driving force, while hydrodynamic and capillary forces may act as driving or resistant forces. Hydrodynamic force is produced by differential hydraulic head (Hubbert, 1953) as well as the difference in excessive pressure between two strata when they are connected (England et al., 1991; Hao et al., 2004; Luo et al., 2003). If the water flow direction is the same as the buoyancy force acting on the hydrocarbon, the hydrodynamic force serves as the driving force for hydrocarbon migration and vice versa (Li, 2013; Luo, 2003; Zhang et al., 2000). The wettability of grain surface determines whether the capillary force is driving or resistant. If the grain surface is water wet, the capillary force is resistant; if oil wet, it is driving (Shen 1995; Zhang & Wang, 1989). Rocks in the subsurface are commonly saturated with formation water and mostly water wet. Thus, capillary force is commonly resistant. However, when rocks are subject to polarized fluids, the wettability of some grains will change to become mixed in wettability (Kovscek et al., 1993; Robin et al., 1995), and become conducive to hydrocarbon flow (Qi et al., 2015). Regardless of the role of capillary

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force, the direction of the resultant force between hydrodynamic and buoyancy forces mainly determines the direction of hydrocarbon migration (Hindle, 1997; Zhang et al., 2000). Hubbert (1953) introduced the principle and methodology of hydrodynamics into the study of hydrocarbon migration and provided a simple and practical method to study the forces for secondary migration (England et al., 1987; Li, 2013). He developed the hydraulic potential as the potential of a unit mass of fluid relative to a datum. When the datum is taken as the water table or surface of a sedimentary water body: Φ = gz + P/ρw

(1.1)

where Φ is the hydraulic potential with a unit of m; z the distance between the observation point and datum with a unit of m; P excess pressure at the observation point with a unit of MPa, which is 0 for the hydrostatic environments; ρ w water density with a unit of kg/m3 . The hydraulic potential is the sum of all mechanical energy of a unit mass of fluid relative to the datum (Hubbert, 1953). Because of the density difference between oil and water, the potential of oil can be obtained by considering the buoyancy of oil in water superimposed on the potential of water (Hubbert, 1953): Φo =

ρw ρw − ρo Φw − gz ρo ρo

(1.2)

where Φ w and Φ o are potentials for water and oil, respectively; ρ o density of oil. The potential of natural gas can be obtained similarly and needs to consider the effect of temperature and pressure: Φg =

ρw − ρ g ρw gz Φw − ρg ρg

(1.3)

where Φ g is potential of natural gas; ρ g the density of nature gas averaged over the distance from the observation point to datum, and can be obtained from the equation of state of gas (Weast, 1975):  ρg =

dρg (P, T )

(1.4)

Hubbert (1953) further explored Eq. (1.1) to derive the driving force to facilitate hydrocarbon migration and accumulation: dE = −gradΦ = g −

1 gradP ρ

(1.5)

1.1 Secondary Hydrocarbon Migration

5

This is a vector equation which asserts that at any given point a unit mass of the specified fluid will be acted upon by a force E which is the vector sum of two independent forces, gravity, and the negative gradient of the pressure (Hubbert, 1953). Combining with Eq. (1.2), we can get the effect of force on oil in water as: Eo =

ρw ρw − ρo ρw Ew − g= E w + υo (ρo − ρw )g ρo ρo ρo

(1.6)

where E w and E o are driving forces for water and oil, respectively; υ o oil specific volume. Thus, the second term in the right side of the equation is the buoyancy force of a unit mass of oil in water; the first term in the right is the driving force as an increase of water driving force by a ratio of ρ w /ρ o , as transformed from the force on water to that on a unit mass of oil (Tao, 1993). The small density of natural gas would result in a driving force larger than that on oil. Hubbert’s (1953, 1957) definitions of the hydraulic potentials of water, oil, and natural gas integrate the forcing relationship during secondary migration and demonstrated the differences in the flow dynamics among the three fluids and the role of hydrodynamics. However, the selection of datum is arbitrary and, thus, the derivation of potentials of oil and natural gas and driving forces is somewhat abstract and impractical. Thus, some researchers (e.g., Dahlberg, 1982; Lerche & Thomsen, 1994; Schowalter, 1979) used streamline dynamic analysis in their calculations under the conditions of water flow and a stable oil column, even though they adapted the concept of potential of oil and natural gas. However, the hydrodynamic force derived from Hubbert’s (1953) concept of fluid potential is only a force intensity vector E, which is not consistent with the dimension of fluid pressure we usually use. England et al. (1987) took the effect of capillary force into account in migration. They defined the hydraulic potential as the work needed to move a unit volume V of water from the datum to the observational point in the subsurface. When the datum is taken as the water table or surface of a sedimentary water body: ΦV = P V − mgz + Pc V

(1.7)

where Φ is hydraulic potential; z the distance from the observation point to datum; P the fluid pressure at the observation point; Pc the capillary force in the rock where two fluid phases are present; and m the mass of a unit volume of the fluid. For water flow, Pc = 0, then the hydraulic potential can be expressed as: Φw = P − ρw gz

(1.8)

The potential of oil Φ o is also expressed as a function of that of water Φ w : Φo = Φw + (ρw − ρo )gz + Pc Similarly, the potential of natural gas Φ g as:

(1.9)

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

Φg = Φw + (ρw − ρ g )gz + Pc

(1.10)

where, in Eqs. (1.9) and (1.10), the first term at the right is the potential of water, i.e. hydrodynamic work. The second term can be regarded as the work needed to move oil or gas over a distance of z by the buoyancy force of oil or gas in water. The third term is the resistant capillary force for oil or gas during flow. The hydraulic potential in Eq. (1.7) can similarly be transformed into driving force: F=−

dΦ = −gradP + ρg − gradPc dz

(1.11)

For water flow, the capillary gradient gradPc is 0. The driving force at the observation point is the resultant force between the pressure gradient and the weight of a unit volume of fluid acting on the observation point. The hydraulic potential incorporating capillary force can be used to explore the difference in potential of oil and natural gas among the pores within the carrier beds or between the carrier rock and caprock or sealing surface. However, such definition may facilitate qualitative analysis of hydrocarbon migration and accumulation (Hao et al., 1994), but is difficult to apply in quantitative analysis of migration dynamics. Analysis of migration dynamics must abandon the assumption that the conduits may be regarded as macroscopically homogeneous, because hydrocarbon migration occurs mostly in a quite limited portion of the conduits (Dembicki & Anderson, 1989; Luo, 2011; Schowalter, 1979). During hydrocarbon migration in heterogeneous conduits, the capillary forces at the possible break-through points along a migration pathway are of variable direction and magnitude (Luo et al., 2004; Tokunaga et al., 2000). The actual break-through position is determined by the geometry of pathways, driving forces, and the difference in magnitude of capillary force between adjacent possible break-through points. In summary, the incorporation of capillary force in hydraulic potential of oil and natural gas makes quantitative study of migration and accumulation rather difficult (Luo et al., 2020). Furthermore, even if the capillary force is incorporated, the excess pressure within a reservoir still cannot be integrated with the abnormal pressure in the caprock. The fact that the caprock can generate and keep high abnormal pressure indicates extremely low permeability of the caprock at a geological time scale (Luo & Vasseur, 1997). The hydrodynamic force driving fluid flow in caprocks toward adjacent reservoirs during hydrocarbon migration is far greater than buoyancy and capillary forces. Thus, it is difficult to integrate all forces in such defined hydraulic potential. In fact, if the capillary force is not considered in the hydrocarbon potential as defined by England et al. (1987), the concept of fluid potential matches well with the concept of gravitational potential. In hydrostatic environments: Φo = vo (ρw − ρo )gz

(1.12)

1.1 Secondary Hydrocarbon Migration

7

If buoyancy force, vo (ρ w − ρ o )g, is regarded as gravitational force mg, the concept of hydraulic potential of oil and gas would be consistent with that of gravitational potential. Dahlberg (1982) used the downslope movement of a ball on an undulating surface driven by gravity as an analog for hydrocarbon migration driven by the flow potential. Hence, the capillary force should be removed from the definition of fluid flow potential of England et al. (1987) during quantitative study of hydrocarbon migration. As a result, the flow potentials for oil Φ o and natural gas Φ g are (Luo, 2008): Φo = Φw + (ρw − ρo )gz

(1.13)

Φg = Φw + (ρw − ρ g )gz

(1.14)

Thus, the potential of oil and gas is the work needed to move a unit volume of hydrocarbon from the datum to an observation point by the resultant sum of the buoyancy of hydrocarbon and the excessive pressure for migration. The direction and magnitude of the driving force are not related to the physical properties of the migration conduit, and only determined by the normal direction and gradient of the hydrocarbon potential field. Hydrocarbon would migrate from an area of a high potential to that of a low potential. As a result, it is the balance between the driving and resistant forces for migration that determines the migration direction in a carrier bed, the extent and location of accumulation in a trap, and the efficiency of sealing of cap rocks, fault zones, and unconformities. In the process of migration, the balance between the driving and resistant forces determines the formation of actual migration pathways and hydrocarbon movement within the pathways. When the former is greater than the latter, hydrocarbon will move; otherwise, hydrocarbon will remain immobile. In summary, the concept of fluid (hydrocarbon in this case) potential unifies the dynamic relationship among hydrocarbon migration, accumulation, and dissipation (Luo, 2008).

1.1.3 Conduits of Migration A conduit connects the source of hydrocarbon with the reservoir and facilitates the migration of hydrocarbons generated from the source rocks to a trap where the hydrocarbon is pooled. The transport capacity and connectivity of a conduit during the main stages of migration control the direction of migration and location of accumulation. Hence, characterization of the conduits is fundamental to the study of hydrocarbon migration and accumulation dynamics. In petroliferous basins containing siliciclastic rocks, the conduits are mainly composed of sandstone carrier beds, faults and joints, and beds associated with unconformities. Since 1990s, a large amount of research on hydrocarbon migration conduits have been carried out. However, some important

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

Fig. 1.1 A model of individual fluvial sandstones in a carrier bed

questions remain. For the study on dynamics of migration and accumulation, the research on migration conduit focuses on the description and reconstruction of the spatial structure, transport capability, and their temporal changes of various types of conduits. 1. Sandstone carrier beds Sandstones are an important type of carrier beds and reservoirs in siliciclastics-filled basins. Sandstones or an assemblage of sandstone bodies with high porosity and permeability and surrounded by muddy sediments form a carrier bed (Lei et al., 2014; Luo et al., 2012). Individual sandstone bodies in a carrier bed possess hydrodynamic connectivity to allow hydrocarbon to move through them. Multiple sandstone bodies may nest in a stratigraphic interval under a regional seal (Fig. 1.1), all of which may have been hydrodynamically connected to each other during a geological period of hydrocarbon migration to collectively form an effective migration conduit linking the sources and traps and, thus, form a carrier bed (Luo et al., 2012). The individual sandstone bodies in a carrier bed may be connected by direct lithologic continuity or by linkages through open faults and fracture zones. The study of carrier connectivity is more difficult than that of sandstone bodies in a reservoir bed, because the hydrodynamic connectivity of and among individual sandstone bodies may have changed significantly over a geological time period (Luo et al., 2012). Sandstones are highly heterogeneous, as caused by multi-scale depositional, diagenetic, and tectonic processes at a kilometer to micrometer scale, ranging from depositional systems, lithofacies, and sedimentation units defined by sedimentary structures and textures (Pranter & Sommer, 2011; Weber, 1986). At the microscopic level, the grain composition, size, degree of sorting, shape, mode of packing, degree of diagenetic alteration, and content and type of cements in a sandstone all are not uniform. These heterogeneities are reflected by the heterogeneity of porosity and permeability (Doyle & Sweet, 1995; Dreyer et al., 1990). At the macroscopic scale, the heterogeneity of a carrier bed is mainly caused by the spatial variations of depositional systems, lithofacies, and sedimentary structures (Chandler et al., 1982; Goggin et al., 1992; Liu et al., 1996). Individual systems and facies may be linked to different provenances, sediment compositions, depositional environments, and tectonic settings, which would result in variabilities in microscopic sandstone attributes.

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9

The porosity and permeability of a sandstone change constantly through diagenesis during burial, and generally tend to decrease with depth (Ehrenberg & Nadeau, 2005). However, they also vary generally over a wide range at the same depth. In fact, in addition to compaction and pressure dissolution that generally reduce porosity and permeability, a myriad of other factors affect diagenesis (Bloch et al., 2002; Liu et al., 1992; Maxwell, 1964; Surdam et al., 1984; Shou et al., 2006). Thus, it is impossible to characterize all the mechanisms and processes of diagenesis that have caused the spatial variations of porosity and permeability in sandstones. Diagenetic changes overall are heterogeneous in sandstone, resulting in complex fluid flow characteristics in sandstones (Dutton et al., 2002). Up to date, the relationship between diagenesis and fluid flow has not been systematically studied (Fitch et al., 2015). Some recent studies have shown that diagenetic processes and changes of porosity and permeability differ in different parts of a texturally heterogeneous sandstone body (Fitch et al., 2015; Luo et al., 2015a, 2015b; Morad et al., 2010; Shi et al., 2017). If we can grasp such architectural heterogeneity, the diagenesis of each lithofacies might be actually relatively simple, and further divide its diagenesis process into different time stages in a certain way, then the history of diagenesis process may be clearly established (Luo et al., 2015, Shi et al., 2017). The different diagenetic history of the component lithofacies within a carrier bed (or reservoir) enhances the overall heterogeneity of the bed, and further complicates later fluid flows and diagenetic processes to manifest the characteristics of architectural heterogeneity (Luo et al., 2016a, 2016b). Two aspects should be considered in the study of the connectivity of a carrier bed, which contains multiple spatially related component lithofacies. The first aspect is the geometric connectivity, as determined by the spatial connections among component lithofacies. The second is the hydrodynamic connectivity of the carrier bed, which characterizes the fluid flow through the bed during hydrocarbon migration (Lei et al., 2014; Luo et al., 2012). The research on the geometric connectivity of sandstones has been extensive. Our ability to predict the spatial distribution and connectivity of carrier beds has been significantly improved with the application of knowledge and techniques in sedimentology, high resolution sequence stratigraphy, and geophysical reservoir characterization (Hao et al., 2000; Yang et al., 2002a). The spatial distribution and geometry of carrier beds can be quantitatively and more accurately described and documented using seismic reservoir characterization technology that calibrates the seismic data with well data (Menno, 2006). However, the advantage of this technology lies in lateral prediction, but with a vertical seismic resolution much lower than that of well data analysis (Alejandro, 2006). Therefore, integration of seismic and well data and geophysical, petrophysical, and geological technologies is necessary to study the geometric connectivity of carrier beds. Hydrodynamic connectivity studies use mostly dynamic data of oilfield development to analyze the characteristics of connectivity at the present. The connectivity can be assessed using production data and methods, such as artificial tracing, geochemical analysis, formation pressure analysis (Deng et al., 2003; Lei et al., 2014), or pulse testing (Johnson et al., 1966). However, the fluid connectivity does not reflect the

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

characteristics of hydrocarbon migration in the geologic past, because the porosity and permeability of sandstones and the connectivity among carrier beds might have changed during the evolution of the basin. How to determine the hydrodynamic connectivity of the carrier beds in previous migration and migration-accumulation periods is an important problem to be solved in the study of hydrocarbon migration. When evaluating the conductivity of a carrier bed, it is necessary to consider its permeability with the constraints of fluid connectivity (Lei et al., 2014). The hydrocarbon migrating capacity along connected carrier beds depends on their conductivity, which can be characterized by permeability. Generally speaking, the larger the throat radius is and the smaller the difference between the pore radius and the throat radius is, the better the permeability of a sandstone. The permeability of sandstone mainly depends on grain composition, sorting, roundness, and orientation, type and content of cements, pore structure, and other factors (Dreyer et al., 1990; Manmath & Lake, 1995). 2. Fault Carriers Faults play an important role in hydrocarbon migration and accumulation (Fisher & Knipe, 2001; Hooper, 1991; Smith, 1966). They may act as either barriers or conduits to fluid flow (Karlsen & Skeie, 2006; Smith, 1966) during fluid migration. Obviously, the opening and closing of a fault control hydrocarbon migration and accumulation and, therefore, the distribution of hydrocarbon fields in petroliferous basins (Harding & Tuminas, 1989; Selley, 1998). The dual behavior has been the focus of discussions over several decades (Losh et al., 1999; Sibson, 1981, 1994). It has basically been recognized that regardless of the nature of a fault, the flow of fluid along the fault is periodic, and the fault is mostly open during periods of active faulting, with high permeability, and can be used as a channel for vertical and lateral migration (Anderson et al., 1994; Hooper, 1991). However, during the dormant period, a fault is usually in a closed state, with low permeability, and blocks oil and gas movement (Bouvier et al., 1989; Fowler, 1970). Therefore, it is more appropriate to use the fault “opening and closing”, instead of “sealing” to describe the contribution of different fault segments to the fluid connectivity in the process of faulting. Based on the capillary sealing model of Hurbert (1953), Smith (1966) first discussed theoretically the mechanism of lateral sealing of faults. He emphasized that the juxtaposition of sandstones may destroy the sealing capability of faults. However, he noticed that faulted sandstones may be sealed by mudstones squeezed into the sandstones during faulting. This will change the migration direction or even stop migration to cause in situ accumulation. He then put forward a concept of fault sealing or opening. Watts (1987) studied fault sealing of single-phase and two-phase hydrocarbon columns, and proposed the “pressure depth diagram” analysis method. Allan (1989) proposed a practical method to evaluate the lateral sealing of faults by illustrating two-dimensional juxtaposition of sandstone and mudstone beds on both sides of the fault plane. Nevertheless, under realistic geological conditions, the juxtaposition of strata across a fault is not simply the direct contact of any pair of strata. The migration of

1.1 Secondary Hydrocarbon Migration

11

oil and gas along the fault is closely related to the internal structure, mechanical properties, and intensity of the fault (Gibson, 1994). Previous studies through field observation, microscopic observation, and experimental simulation (Antonelini & Aydin, 1994; Berg & Avery, 1995; Engelder, 1974; Knipe, 1992, 1997; Weber et al., 1978) have shown that a fault may be regarded as a zone with a certain width and complex internal structure; the rock composition, deformation characteristics, geometric characteristics, and physical and chemical changes of the rocks in the fault zone may have an impact on the sealing ability of the fault. Intense faulting causes muddy sediments to be involved in the fault zone, resulting in mudstone smear, reducing or even changing the physical properties of the fault zone (Weber et al., 1978). The throw of the fault zone can commonly be divided into a large number of small segments or micro fracture deformations (Lehner et al., 1997). The strata on both walls of the fault are affected by the faulting and form fractures. The micro fractures on the fault sliding surface and the surrounding rocks in both walls may cause an increase of permeability (Zhang et al., 2007c). Therefore, the fluid flow in the fault zone is very complex and highly uneven during oil and gas migration (Zhang et al., 2010). Faulting increases the anisotropy of rock permeability in and near a fault zone, which varies with time. In the direction perpendicular to the fault plane, the permeability of rocks decreases due to the mud smearing and crushing; parallel to the fault plane, the permeability increases unevenly. The open microfractures in the fault zone lead to dilatancy of rocks. The increase of permeability is mainly concentrated in the direction parallel to the fault plane. The permeability of rock mass increases by 1–3 orders of magnitude (Brace, 1966; Zoback & Byerlee, 1975). This kind of dilatancy and its corresponding permeability increase may only occur near the faults with shallower depth (Downey, 1984; Luo & Vasseur, 2002; Luo, 2004). At the same time, the distribution characteristics of in-situ stress in the fault zone or on the fault plane will affect the distribution of hydrocarbon migration channels in the fault zone. Macro joints and fractures are commonly developed in places of stress concentration of the fault plane, and increase the permeability and, thus, may serve as the migration conduit (Bruhn et al., 1994). Berg and Avery (1995) analyzed the distribution of stress and possible permeability changes in different parts of a growth fault, and found that the middle part of the fault plane is mostly represented by tensile stress, which keeps the fault plane open and may become a conduit for migration; the upper and lower ends of the fault by compressive stress; and the derived shear stress may also form mudstone smear, which reduces the permeability. Once the faulting stops, the rock dilatancy corresponding to stress disappears; and the large confining stress promotes rapid closure of fault plane and fractures. As a result, the permeability decreases in the entire or part of the fault zone (Luo, 2004; Luo & Vasseur, 2016). Therefore, whether oil and gas can migrate along a fault zone is affected by the combined effects of various factors. In practical research, specific analysis should be carried out in light of specific conditions (Zhang et al., 2011). In most cases, the duration of an active faulting episode is only a fraction of the total life span of a fault and is less than the duration of the entire migration process (Hooper, 1991; Robert & Nunn, 1995). As a result, only a small amount of hydrocarbons

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

migrated through a fault segment during one episode of faulting (Haney et al., 2005). Probably thousands of faulting episodes and associated migration events are needed to accumulate a commercial quantity of hydrocarbons in a trap (Anderson et al., 1994). The properties of an open fault segment are probably ever-changing; and fluid flows during individual migration events may have behaved differently. Fault “opening” and associated hydrocarbon migration are geological processes, representing a series of physical processes that occurred repeatedly over a geological time span. Any permeability measurements at specific localities along a fault zone would only represent the current state of fault sealability, which may differ greatly from that during active faulting and hydrocarbon migration. Luo (1999) carried out a study on the dynamics of fault opening in the central structural belt and migration mode of natural gas in Yinggehai Basin, South China Sea. Faults opened episodically; and the width, extension, offset strata, shape of fault plane, and degree of internal connectivity vary greatly between individual stages of faulting. When a fault is opened, the mixed phase fluid containing natural gas flows along winding and narrow channels in the fault zone, and causes extremely high local thermal anomalies at shallow depth. Open faults during different episodes connect different natural gas sources, CH4 or CO2 , causing that different proportions of natural gases accumulate in the reservoirs on two sides of the faults. Quantitative characterization of fault opening and closing is still in the exploration stage. Due to the limitation of the number of wells drilled through faults and the difficulty of coring in the fault zones, it is difficult to observe the lithology of the fault zones, which may only be speculated by the indirect methods. Several observed physical and chemical properties of fault zones have been proposed as proxy data to evaluate the timing and duration of fault opening and closing (Allen, 1989; Antonelini & Aydin, 1994; Berg & Alana, 1995; Engelder, 1979; Gibson, 1994; Knipe, 1992, 1997; Sorkhabi & Tsuji, 2005; Smith, 1966; Weber et al., 1978). Many studies have been devoted to identifying diagnostic parameters that may be universally applied to effectively assess the sealability of faults (e.g., Bouvier et al., 1989; Harding & Tuminas, 1989; Knott, 1993; Lindsay et al., 1993; Schowalter, 1979; Sorkhabi et al., 2002; Sorkhabi & Tsuji, 2005; Watts, 1987; Yielding et al., 1997). Dozens of parameters have been used (Bouvier et al., 1989; Lindsay et al., 1993; Knott, 1993; Yielding et al., 1997; Sorkhabi et al., 2002). In fact, any factor that affects sealability in one way or another can be parameterized. However, the effectiveness of a particular parameter varies from case to case (Færseth et al., 2007). The effect of a geological factor on fault sealability during hydrocarbon migration must be well understood before the factor can be accurately parameterized and effective. A variety of approaches have been used to characterize the sealing ability of faults to study their impact on fluid flow. Perkins (1961) emphasized that the juxtaposition of sandstones may destroy the sealing capability of faults. However, he noticed that faulted sandstones may be sealed by mudstones squeezed into the sandstones during faulting. Allen (1989) stated that stratal discontinuity across a fault must be studied three dimensionally. Smith (1966) introduced some theoretical considerations on sealing and non-sealing faults based on the capillary model of Hubbert (1953). To

1.1 Secondary Hydrocarbon Migration

13

characterize the sealing heterogeneity along fault planes, several parameters, such as clay smear potential, shale smear factor, and shale gouge ratio (SGR), were proposed to estimate the degree of mudstone smearing within fault zones and to evaluate the sealing ability of faults (Bouvier et al., 1989; Lindsay et al., 1993; Yielding et al., 1997). Using outcrop observations, Hasegawa et al. (2005) and Kachi et al. (2005) designed several sealability parameters to describe the three-dimensional sealing characteristics of faults and emphasized the heterogeneity of fault sealability. These detailed studies have pushed the concept of fault sealability from qualitative toward quantitative (Sorkhabi & Tsuji, 2005). Zhang et al. (2007d) recognized that fault sealability should be a comprehensive description of the role of faults in the entire process of hydrocarbon migration in a relatively long geological period. From a geostatistical point of view, Zhang et al. (2010) introduced an empirical fault-connectivity probability method to assess the hydraulic connectivity of faults during hydrocarbon migration at a geological time scale. Whether hydrocarbon migration has already occurred through a fault segment at any point of time in the basin history is identified by the presence or absence of hydrocarbon-bearing layers on both sides of the segment. Data from the Chengbei Step-Fault zone in Qikou Depression, Bohai Bay Basin, northeast China, were used to develop this method. The role of an open fault segment in migration was then assessed by the relationship between the fault-connectivity probability and a parameter called the fault opening index (Zhang et al., 2010). The probability models of opening faults established for different types of faults in several basins are similar (Lei et al., 2014; Zhou et al., 2010). 3. Unconformity carriers Unconformities are common in sedimentary basins. They have been caused by erosion and non-deposition related to the tectonic movement of the crust and fall of sea or lake levels. An unconformity can be demonstrated by the contact relationship between old and new strata (Bates & Jackson, 1984). As early as 1930s, it was recognized that unconformity played an important role in hydrocarbon migration and accumulation (Levorsen, 1954; Li, 2013; Pan, 1983). Hydrocarbon reservoirs related to unconformities have also been found worldwide (Hunt, 1996; Wang, 1983). The formation of Karamay oilfield in the northwestern margin of the Junggar Basin in NW China is a typical example of large-scale migration of hydrocarbon along unconformities (Wang, 1983). In fact, an unconformity is only a stratigraphic surface. The strata sub- and suprajacent to the surface are the media for hydrocarbon migration. The variations of porosity and permeability of the strata near an unconformity are complex, resulting in many uncertainties about whether the strata can constitute a migration conduit (Luo et al., 2014; Song et al., 2010). When a rock bed is exposed at the earth surface due to the tectonic uplift, it is subject to weathering, denudation, and water leaching. The degree of weathering and erosion varies greatly due to the topography, so that the strata of various ages and lithology may be overlapped on the unconformity (Dolson et al., 1994; He, 2007; Hunt, 1996; Wu et al., 2003). The strata above and below the unconformity commonly

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

undergo different diagenesis during burial, which changes the porosity and permeability of these strata. All these processes and factors generate the complexity of unconformities as carriers. Strata immediately sub- and supra-jacent to an unconformity comprise an unconformity zone, which commonly show specific stratigraphic patterns (He, 2007; Wu et al., 2002). Wu et al. (2003) divide an unconformity zone in Junggar Basin into transgressive-lag conglomerate and sandstone above the unconformity and a clayrich weathering layer and a moderately-altered vadose layer below the unconformity through core and wireline log analysis. They interpreted that the transgressive conglomerate and sandstone and the vadose layer are favorable carriers for hydrocarbon migration. He (2007) studied the basal Cretaceous unconformity in Junggar Basin, NW China, and concluded whether a weathered crust is present is critical to forming carriers within an unconformity zone. Zhang and Ai (1996) identified three packages across the unconformity at the top of Ordovician carbonate rocks in Tabei uplift in Tarim Basin, NW China, including a layer of lag deposits overlying the unconformity, a vadose layer underlying the unconformity, and the phreatic layer further downward. They proposed that the lag deposits may serve as carriers for hydrocarbon migration. Sediments overlying unconformities commonly include basal conglomerates and transgressive sandstones, both of which may be good carriers (Gao, 2007; Hao, 2007; Liu et al., 2003; Sui & Zhao, 2006). During the formation of the clay-rich sediments underneath the unconformity, several types of traps can be formed (He, 2007; Song et al., 2010). However, if the sediments below an unconformity are siliciclastic, the texture and physical properties of the weathered sediments are very complex, as controlled by lithology, diagenesis, and weathering processes. As a result, these sediments commonly are not good carriers for long-distance hydrocarbon migration (Song et al., 2010). At present, the research on unconformity carriers is limited to the distribution and texture of the unconformity zones and qualitative analysis of the relationship between unconformity and hydrocarbon migration and accumulation. Future research should focus on the location, mode of hydrocarbon migration, relationship with other types of carrier beds, and quantitative characterization of the conductivity of unconformity zones. 4. Composite carriers The various types of carriers of hydrocarbon migration commonly do not occur alone in a basin. The so-called composite hydrocarbon migration carrier system is commonly a three-dimensional framework composed of two or more types of carriers (Fu et al., 2001; Galeazzi, 1998; Zhang et al., 2003c ). Li et al. (2004) identified several types of composite carriers based on the spatial patterns of faults (fractures), reservoirs, and unconformities in the Tertiary formations in Jiyang Depression, Bohai Bay Basin, eastern China, namely, meshwork-carpet, T, and stair types. Zhang et al. (2003c) proposed a multi-stage migration and accumulation model for the Shahejie reservoir system in Dongying Depression, Bohai Bay Basin, suggesting that hydrocarbon had migrated along a complex three-dimensional network system consisting

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of sandstone carrier beds and faults. Lei et al. (2014) established a three-dimensional framework of composite carrier complex consisting of sandstone carrier beds and faults. They used several unified parameters to quantitatively characterize the performance of the complex carrier systems, and simulated and analyzed the processes of hydrocarbon migration and accumulation during the main accumulation periods. The conductivity of individual types of carriers in a composite carrier complex differs greatly and also changes during basin evolution. Whether such a complex can form effective conduits between hydrocarbon sources and traps depends on the conductivity of individual component carriers and the timing relationship with accumulation. If we consider only the spatial distribution of component carriers but ignore the timing relationship, we will not be able to fully understand the role of the carrier complex in hydrocarbon migration and to accurately reconstruct the hydrocarbon accumulation processes (Lei et al., 2014; Luo et al., 2012). It is necessary to take into account the stratigraphic relationship of the entire carrier complex, the spatial distribution of the conductivity of individual carriers, and the timing of carrier connections to form an effective conduit between sources and traps.

1.1.4 Characteristics and Forming Mechanisms of Migration Pathways The spaces used by hydrocarbon moving through a carrier during migration are called pathways (Dembicki & Anderson, 1989; Luo et al., 2004). The mode of migration and the characteristics of migration pathways in the carrier system are important factors controlling the formation and distribution of hydrocarbon accumulations (Luo et al., 2007a, 2007b). Experimental observations show that the pathways only occupy a part of a carrier (Catalan et al., 1992; Vasseur et al., 2013). In macroscopically homogeneous porous media, hydrocarbon migration only takes place along some limited pathways within the carrier, and the volume of the dominant migration pathways accounts for only 1–10% of the total potential conduits (Luo et al., 2008; Schowalter, 1979; Thomas & Clouse, 1995). This understanding is critical to estimating the loss of hydrocarbon in migration conduits and the possibility of long-distance lateral migration (Allen & Allen, 1990). It is speculated that hydrocarbon migration in the sedimentary basins is similar to that in the experiments (Dembicki & Anderson, 1989; Schowalter, 1979; Thomas & Clouse, 1995). Vasseur et al. (2013) suggested, if the area of hydrocarbon generation and expulsion from the source rocks is small, the mode of hydrocarbon migration should be the same as that observed in the laboratory. Numerous divergent migration pathways may be formed from the area of expulsion to the surrounding area (Schowalter, 1979; Allen & Allen, 1990). This mode of migration with a very limited range of pathways apparently depends mainly on the direction of the driving forces

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

(Luo et al., 2004). Verweij (2003) discussed the characteristics of migration pathways under the conditions of homogeneous carrier beds and small hydrodynamic forces for several cases where the carrier beds have regular tectonic structures. Allen and Allen (1990) analyzed the relationship between the configuration of oil supply area and the structural configuration of the upper boundary of the carrier bed, and pointed out that hydrocarbon migration was not uniform at a macro-scale, and the configuration of the oil supply area affects the mode of hydrocarbon migration. Hydrocarbon tends to migrate along the direction of maximum structural gradient of the carrier bed; the migration pathways converge at the structural apex to form the dominant pathways. Therefore, even though some traps are far away from the oil supply area, they still can accumulate hydrocarbon if they are near the dominant migration pathways. Hindle (1997) emphasized the importance of regional caprock overlying the carrier bed. When the caprock has a great enough sealability, the direction of hydrocarbon migration is mainly determined by the structural configuration of the upper boundary of the carrier bed. Above the hydrocarbon generating area, there are many hydrocarbon migration pathways, which form a dense network. But far from the hydrocarbon generating area, the main mode of lateral migration is along limited and concentrated pathways (Hindle, 1997). In other words, hydrocarbon migration is divergent in the entire region, but, in the local region, the formation of each dominant migration pathway is the result of gradual convergence of several small migration pathways. Therefore, the macro-scale migration process is quite similar to the drainage convergence on the earth surface, that is, the surface sheet flows in an area converge into small streams, which then gradually converge into large rivers and finally flow into lakes and oceans. Luo (2011) simulated the characteristics of dominant migration pathways driven by buoyancy using the percolation model method (Fig. 1.2). The simulated hydrocarbon migration pathways are superimposed on an elongate basin as by Allen and Allen (1990), in which the carrier beds are set to be uniform. The flux of hydrocarbon migration in the pathways changes from small to large as reflected by the color changes in Fig. 1.2. The higher the migration flux is, the more hydrocarbon flowing through the pathway, which would become the main migration pathway. The simulation results show that hydrocarbon migrates along a limited number of dominant pathways, the pathways tend to converge in the direction of large fluid gradient, and the flux of migration varies greatly among the pathways. The shape and heterogeneity of migration pathways depend on the degree of dominance of driving forces over resistant forces. Many factors control hydrocarbon migration along so-called dominant pathways. Various geological factors related to the driving and resistant forces will affect the formation of dominate pathways as well as the shape and migration efficiency of pathways. The factors affecting buoyancy include the density and viscosity of oil, gas and water (Schowalter, 1979; Thomas & Clouse, 1995), structural configuration at the top of carrier bed (Gussow, 1954, 1968; Hindle, 1997), distribution and variation of formation temperature and pressure (Schowalter, 1979), and distribution of fluid potential field (Dahlberg, 1982; England et al., 1987; Hubbert, 1953; Lerche & Thomsen, 1994). Factors related to the resistant capillary force include, for example,

1.1 Secondary Hydrocarbon Migration

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Fig. 1.2 Simulation results of hydrocarbon migration pathways in a hypothetical basin. The gray lines are the results of Allen and Allen (1990) estimated from fluid potential; the irregular images give the migration pathways simulated by percolation model; and the black to yellow color scheme in d indicates the relative flux of hydrocarbon migration within the pathways. From a–d, the ratio of buoyancy to capillary force is 10–2 , 10–3 , 10–4 and 10–5 , respectively (after Luo, 2011)

petrophysical properties of carrier beds (Bekele et al., 1999), sealability of caprock (England et al., 1987), sealability and connectivity of fractures (Allen & Allen, 1990; Hindle, 1997). However, it is still controversial on the contribution of each factor to migration patterns and pathways. Gussow (1954, 1968), Hindle (1997) emphasized that the shape and location of migration pathways are largely controlled by the configuration of stratigraphic surfaces, while Rhea et al. (1994), Bekele et al. (1999) suggested that horizontal permeability of rocks in the carrier bed is the important factor controlling the heterogeneity of migration pathways. Basin-scale hydrocarbon migration along dominant pathways is still lack of direct evidence. Most related studies are based on experimental results at a micro-scale (Dembicki & Anderson, 1989; Luo et al., 2004; Schowalter, 1979; Thomas & Clouse, 1995), or computer simulations using simple hydrodynamic relationships

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

(Dembicki & Anderson, 1989; Hindle, 1997; Luo, 2011; Thomas & Clouse, 1995). The study using chemical tracers in crude oils supports the theory of hydrocarbon migration along limited pathways (Larter & Aplin, 1995). It appears that crude oil in some oilfields migrates mainly through pathways with a width of only tens of meters or less in the carrier bed to fill reservoirs in traps. On the other hand, the extremely uneven migration pathways inevitably pose tough requirements and challenges for any methods of tracing pathways and direction of hydrocarbon migration with geochemical indicators. Hydrocarbon discoveries are commonly located along a specific structural trend (Yang et al., 2002b). In addition, in the absence of traps around the migration pathways, hydrocarbon may spill directly to the surface to form seeps. Hindle (1997) suggested that these seeps can be used as an important indicator of hydrocarbon migration pathways.

1.1.5 Dynamic Model of Hydrocarbon Migration and Accumulation After being expelled from a source to a carrier bed, hydrocarbon migrates along the dominant pathways governed by the relationship between driving and resistant forces, and accumulates where suitable traps are present. Based on the understanding of hydrocarbon migration patterns and mechanisms, a dynamic model of hydrocarbon accumulation through migration has been attempted (Dembicki & Anderson, 1989; England et al., 1987; Schowalter, 1979). Before hydrocarbon enters the carrier bed from a source rock, the rocks in the carrier bed are water wetted. Oil or gas must overcome the resistant capillary force of the pore throats in the carrier bed in order to migrate upward. Generally, hydrocarbon first accumulates gradually at the interface between the source rock and carrier bed. When the accumulation reaches a certain height, buoyancy generated by the hydrocarbon column will overcome the capillary force; and hydrocarbon will migrate vertically or laterally in the carrier bed (Emmons, 1924). If the source rock is located below the carrier bed, hydrocarbon will migrate vertically upward within the carrier bed; and the migration distance is the apparent vertical thickness of the carrier bed. When the hydrocarbon reaches the upper surface of the carrier bed, it will be stopped by the overlying seal and then have to migrate along the upper surface of the carrier bed in the direction of decreasing hydrocarbon hydrodynamic potential (England et al., 1991; Hindle, 1997). Hydrocarbon migration pathways occupy only a small part of the carrier bed near the upper surface of the carrier bed (Thomas & Clouse, 1995). Regardless of hydrodynamics, the necessary condition for secondary migration of oil is that buoyancy is greater than capillary force (Berg, 1975; Hobson, 1954): 

1 1 − (ρw − ρo )g Z > 2δ rt rp

 (1.15)

1.1 Secondary Hydrocarbon Migration

19

where Z is the height of the oil column, ρ w and ρ o the densities of water and oil, respectively, δ the interfacial tension, r t the pore radius, r p the pore throat radius, and g the gravity acceleration. The left-side term is the net buoyancy of the oil column and the right term the capillary force. When buoyancy and capillary forces are balanced, the oil column height is called the critical oil column height Z o required for secondary migration:  1 1 1 − Z o = 2δ rt rp (ρw − ρo )g 

(1.16)

Catalan et al. (1992) estimated that the critical oil column height is 0.04–0.16 m in their glass bead model experiment. Berg (1975) considered that the critical column height of oil of a density of 0.9 g/cm3 is 0.304–16 m in medium and fine sandstones. Hydrodynamic forces have an important impact on hydrocarbon migration (Hobson & Tiratsoo, 1981). Based on the concept of fluid potential (Hubbert, 1953) and neglecting the capillary force, Berg (1975), Lerche and Thomsen (1994) discussed the equilibrium between hydrodynamic forces in an inclined carrier bed and an oil segment of a certain length parallel to the interface of the carrier bed. They established the critical relationship of oil migration affected by hydrodynamic force. Because oil migrates non-uniformly, the formation of migration pathways is related to the probability distribution of throat radius in the carrier bed (Luo, 2011). Migrating oil selects commonly the maximum pore throat at the oil–water interface around the pathways to break through. The actual critical height of migration should be smaller than those previously estimated (Vasseur et al., 2013). From the point of view of dynamics, oil accumulates where a hydrodynamic equilibrium occurs in the carrier bed (Lerche & Thomsen, 1994) to stall the migration temporarily. The location is commonly where the fluid potential is at the minimum (Dahlberg, 1982). If the volume of the accumulation is large enough, the part of the carrier bed may form a reservoir. However, once the dynamic conditions of oil flow in this part changes, hydrocarbon will continue to migrate (Tissot & Welte, 1984), and new accumulations may occur in other parts, or the hydrocarbon will migrate directly to the surface, resulting in the loss of hydrocarbon (Hindle, 1997). The migrating hydrocarbon always seeks a route with the least resistance in the carrier bed to create new pathways, and only occupies a small part of the carrier bed. If the migrating hydrocarbon stops at a trap and there is still continuous hydrocarbon supply, the hydrocarbon will fill first the large pores, throats or fractures in the reservoir, then gradually the small ones. Finally, the hydrocarbon will occupy most of the pore spaces, the amount of which depends on the hydrocarbon buoyancy, hydrodynamic force, and caprock sealability, and will leave only residual water in the remaining pore spaces (England et al., 1987; Schowalter, 1979). The residual water consists mainly of free water in very small throats and connected pore spaces and adsorbed water on the surface of grains (Bear, 1972). Schowalter (1979), England et al. (1987, 1995) also proposed conceptual models of hydrocarbon accumulation in inclined lithologic and anticlinal traps. In their models, hydrocarbon first migrates to the apex of an anticline along heterogeneous pathways, followed by gradual diffusion

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

around the migration pathways, which, eventually, merge with each other to fill the entire trap. Heterogeneity of clastic carrier bed induced by sedimentary structures commonly results in uneven diagenesis during burial (Luo et al., 2015a, 2015b, 2016a, 2016b). Some thin beds and laminae related to the structures become tight at the early stage of diagenesis. They form a barrier network within the carrier bed, which affects fluid flows during the late stages and leads to heterogeneous diagenesis and, ultimately, hydrocarbon accumulation (Shi et al., 2017; Luo et al., 2015a, 2015b). The migration pathways in heterogeneous carrier beds are quite different from those understood before (Luo et al., 2015a, 2015b). Figure 1.3 shows the simulated migration pathways in two models of heterogeneous carrier beds, where migration occurs in a classic source-reservoir-caprock combination in an anticlinal trap. The numerical simulation method is based on the theory of invasion percolation (Luo, 2011; Luo et al., 2007a). The vertical migration pathways in the carrier bed are limited to the mesh separated by low-permeability barriers. As long as the location of the overflow point on the mesh upper wall is not at the top, there is hydrocarbon accumulation in the mesh at any time. When the hydrocarbon-water interface in the mesh reaches the overflow point, hydrocarbon migrates to the next mesh. The processes repeat mesh after mesh. The migration pathways and hydrocarbon accumulations are complex and variable, as controlled by the structure of the barriers. In architecturally heterogeneous carrier beds, the space occupied by pathways is therefore wider than that in macroscopically homogeneous carrier beds because hydrocarbon flows laterally among the meshes. In some cases, even the entire carrier bed is occupied by the pathways. In addition, hydrocarbon tends to accumulate at the trap located at the high point, as long as the hydrocarbon sources are sufficiently large and the migration time is sufficiently long. This is similar to that in macroscopically homogeneous reservoirs. However, the presence of textural barrier layers in the reservoir will hinder and restrict hydrocarbon migration (Luo et al., 2015a, 2015b). As a result, the oil–water interface will not be perfectly horizontal; and the reservoirs will contain some compartmentalized water pockets, resulting in mixed oil and water content (Fig. 1.3).

Fig. 1.3 Simulation results of hydrocarbon migration and accumulation in heterogeneous carrier beds. The irregular lines are migration pathways. The scale in each diagram indicates relative migration flux, which is the relative quantity of hydrocarbons passing through the pathways. Modified after Luo et al. (2015a, 2015b)

1.1 Secondary Hydrocarbon Migration

21

1.1.6 Tracing and Dating Hydrocarbon Migration Hydrocarbon migration under realistic basin conditions is a complex process in carrier beds that have undergone multiple adjustments and changes of dynamic conditions. Methods and techniques are needed for quantitative testing and analysis to determinate the basic dynamic characteristics, such as source, timing, and pathways. Geochronological technology for dating hydrocarbon accumulation and for source correlation and tracing have advanced greatly to push forward the study of secondary hydrocarbon migration. (1) Geochronology of hydrocarbon migration and accumulation Traditional geochronological analysis in petroleum geology qualitatively determines mainly the relative time of hydrocarbon migration and accumulation. The methods include, for example, timing of trap formation, saturation pressure-dew point pressure, organic petrology, and hydrocarbon-water interface tracing (Chen et al., 2007; Yue et al., 2003; Zhao & Li, 2002). At present, these methods are the most basic for determining the age of hydrocarbon migration and accumulation and providing broad constraints for other more “accurate” dating methods. Since 1980s, geochronology of petroleum migration and accumulation has made great progress with the development and application of fluid inclusion and radiometric dating technologies. Many radiometric dating methods have been applied successfully (Haszeldine et al., 1984; Mclimans, 1987; Lee et al., 1985, 1989; Hamilton et al., 1989; Parnell & Swainbank, 1990; Zhang et al., 1997; Selby & Creasera, 2003). The accuracy of age dating of reservoir formation has been significantly improved, indicating that the geochronology of reservoir formation has changed from qualitative to semi-quantitative. A simple and widely-used method is the combination of homogenization temperature of fluid inclusions and burial history (Haszeldine et al., 1984; Mclimans, 1987). Fluid inclusions contain abundant physical and chemical information of fluids during hydrocarbon accumulation and rock diagenesis. The occurrence, fluorescence color, homogenization temperature of saline inclusions coeval with hydrocarbon inclusions in diagenetic minerals in rocks and fractures can be used to date the age of hydrocarbon charging. The stages of charging can be identified by “projecting” the data onto a burial history map marked with isotherms (Chen, 2007; Haszeldine et al., 1984). However, the reliability of the results is still controversial due to uncertainties in both the burial-thermal evolution history and inclusion testing (Liu et al., 2012). Radiometric dating is the most rapidly developed method for hydrocarbon reservoir formation. At first, the age of authigenic minerals formed before hydrocarbon charging is calculated by the amount and proportion of K/Ar, Ar/Ar or U/Tu radioactive elements in authigenic illite, potassium feldspar, and other minerals; and the youngest age was used to represent the age of reservoir formation (Boles et al., 2004; Hamilton et al., 1989; Lee et al., 1985, 1989). Radiometric dating of trace metal elements has also been used to estimate hydrocarbon accumulation age (Parnell & Swainbank, 1990; Selby & Creasera, 2003; Cai et al., 2014). The concentrations

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of parent and daughter pairs of radioactive elements, such as U–Pb, Pb–Pb, Rb–Sr, Sm–Nd, and Re–Os, in crude oil, asphalt, and kerogen, can be used to date these materials to estimate the age of hydrocarbon migration and charging in the reservoirs as well as the source of hydrocarbons in some cases. Some problems still exist in these methods, including, for example, the degree of a close system in the geological history, sample preparation, and identification of mineral transformations at various stages of diagenesis (Liu et al., 2012). The future direction should be the integrated application of various methods. For example, Liu et al. (2013) developed a method using the laser microprobe on a micro-area with a trace amount to achieve high precision and high resolution. Cai et al. (2014) applied the Re-Os method to reservoir asphalt, not only accurately determined the migration and charging time, but also effectively traced the migration pathways and estimated the source. (2) Oil and gas—source correlation and migration tracing Geochemical methods have always played an important role in the study of secondary hydrocarbon migration (Hunt, 1996; Larter & Mills, 1991; Waples & Curiale, 1999; Zhang & Jin, 2000). It is common that various chemical compounds in formation fluids are selected as indicators to analyze the distribution and evolution of organic matter in source rocks and, then, such indicators are applied in migration study (Tissot & Pelet, 1971). Oil and gas—source correlation is to use various geochemical indices to determine whether a genetic relationship exists between oil and gas and source rock (Waples & Curiale, 1999). On the basis of the correlation, the migration tracing is to determine the direction and pathways of oil and gas migration by analyzing the variations of various geochemical indices, following the color layer fractionation effect that may occur during the process of oil and gas migration (Larter & Mills, 1991; Wang et al., 2000). Seifert and Moldowan (1978) attempted first to use hydrocarbon compositions to evaluate migration distance. Since then, molecular geochemical methods have gradually become widely used in oil and gas correlation and migration tracing, with the advancement in analytical technology. Various types of indicators have been proposed, such as biomarkers of saturated and aromatic hydrocarbons (Horstad et al., 1995; Radke et al., 1982; Wang et al., 2005), non-hydrocarbon compounds (Larter et al., 1996; Li et al., 1998, 1999), carbon isotope (Schoell, 1983), rare gas composition and isotopes (Xu et al., 1979), and trace elements (Boles et al., 2004; Cao et al., 2010); and significant progress has been made in interpretation of hydrocarbon migration direction, pathway and distance (Jin & Zhang, 2005; Li, 2013). Source rock is the starting point of hydrocarbon migration and accumulation. The hydrocarbon molecules generated in intermediate and low maturity source rocks commonly inherit some parent molecular characteristics (Hunt, 1996). However, the physical and chemical properties of hydrocarbons change inevitably through various physical and chemical reactions with rocks and pore fluids in the carrier beds during migration, especially with those within and around the migration pathways (Larter et al., 1996; Boles et al., 2004). These changes are important clues for tracing hydrocarbon migration history by using corresponding geochemical indicators, such

1.1 Secondary Hydrocarbon Migration

23

as specific molecules, element concentration, and isotopic composition (Liu et al., 2007; Losh et al., 2002). Several classic studies (e.g., Clayton & Swetland, 1980; Schoell, 1983; Leythaeuser et al., 1984) demonstrated some still widely-used methods in tracing hydrocarbon migration based on the fractionation of hydrocarbon molecules during migration. These methods include tracing the changes of hydrocarbon components, biomarkers, carbon isotopes and physical properties to identify the hydrocarbon migration direction and accumulation location and are effective at a shallow and intermediate burial depth in stable basins, where hydrocarbon is supplied from similar sources. Commonly-used parameters include density and viscosity of crude oil, nC21- /nC22+ , n-alkanes/cycloalkanes of saturated hydrocarbons, tricyclic terpanes/(tricyclic terpanes + 17alpha(H) − hopane), Ts/(Ts + Tm), rearranged steranes/regular steranes, monoaryl steranes/triaryl steranes, benzothiophene, and alkyl dibenzothiophene (Wang et al., 2005), carbon isotope of alkanes, compositions of light hydrocarbons of C6 and C7 series, (benzene + toluene)/naphthenes, and methane carbon isotope (Hu et al., 2005), natural gas components C1 /C2+ , iC4 /nC4 , total hydrocarbon/non-hydrocarbon and methane carbon isotope (Zhang et al., 1995, 1999). The two-dimensional chromatographic testing technology apparently can obtain high-precision measurement of hydrocarbon components and standard parameters (Wang et al., 2013) to significantly improve the effectiveness in tracing hydrocarbon migration. In addition, QGF and QGF-E quantitative fluorescence analysis detects the micro-fluorescence characteristics of hydrocarbons that are adsorbed, free, or encapsulated in reservoirs to provide information on hydrocarbon migration pathway, paleo-oil–water interface, and timing of hydrocarbon charging into reservoirs (Li et al., 2006). Non-hydrocarbon compounds in crude oil (NSO) have been used to interpret hydrocarbon migration direction and distance since 1990s, and achieved rapid development and good results. Some pyrrole neutral nitrogen compounds (carbazole, benzocarbazole, dibenzocarbazole and their alkyl derivatives), alkaline nitrogen compounds (e.g., benzoquinoline, phenols and their methyl derivatives) have been used (Larter et al., 1996; Liu et al., 1997; Li et al., 1998, 1999; Wang et al., 2000). Advanced Fourier transform ion cyclotron resonance mass spectrometer (FT-ICRMS) can achieve a high resolution and large detection range (Hughey et al., 2004) for NSO heteroatomic compounds. Its application can be used to find effective parameters for source correlation and migration pathway tracing. The results of Liu et al. (2015) show that the concentration of NSO heteroatom compounds in crude oil changes systematically with migration distance. New parameters should be identified in the future. However, in complex superimposed basins that have experienced several periods of tectonic movements, the petroleum systems are commonly characterized by multiple sources and stages of hydrocarbon migration, accumulation, adjustment (Jin & Wang, 2004). Hydrocarbons may have been expelled at variable positions and stages of basin evolution, resulting in mixing and secondary changes of migrating hydrocarbons, such as biodegradation, water washing-oxidation, pyrolysis, and thermochemical sulfate reduction. Hence, the applicability of conventional organic

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1 Progresses and Problems in the Study of Hydrocarbon Migration Dynamics

geochemical indicators is greatly reduced (Karlsen et al., 2004). In deep basins, which have generally undergone multi-stage tectonic movement and deep burial, the hydrocarbons may have generated from not only original organic matter, but also crude oil and asphalt and, thus, have multiple origins (Zhao et al., 2017). In addition, high-temperature thermal cracking may cause significant changes in the molecular and isotopic compositions of hydrocarbons. As a result, organic geochemical parameters may not be effective in tracing hydrocarbon migration (Liu et al., 2013); and diligence is required in such studies. Recent studies have found that carbon isotopic composition of the main components of high-maturity hydrocarbons may still keep the parent signature, if the thermodynamic fractionation effect can be ascertained. Li and Guo (2010) analyzed the carbon isotopes of n-alkanes in pores, throats and inclusions in a reservoir to interpret the mixing characteristics of crude oil and estimate the contribution of various hydrocarbon sources. The organic carbon isotope signature may also be used to interpret the sources of natural gases as obtained through acidolysis and desorbed from source rocks and reservoirs for gas source correlation (Liu et al., 2010). Liu et al. (2007) proposed a ternary index system for tracing natural gas migration and accumulation. The three independent indicators are stable isotopes, rare gas isotopes and light hydrocarbons. They may be cross-checked to ensure the consistency of the results. However, the uncertainty in isotopic fractionation makes the interpretation of the origin and mixing proportion of oil–gas mixture from different sources quite difficult. Further research on the methods and effectiveness of the index system is needed. Hydrocarbon fluids interact with rocks and other geofluids during migration and result in mass exchange. Trace elements and isotopes of minerals and rare gas isotopes would record the history of the fluid activities, which can be used to trace hydrocarbon migration in multi-stage maturation and mixed hydrocarbon systems (Boles et al., 2004; Liu et al., 2007). The changes of concentration of trace elements, especially Mn, Fe, Mg and Sr, in carbonate cements coeval with hydrocarbon migration can be used as indicators (Cao et al., 2007, 2009; Gregg & Shelton, 1989; Rossi et al., 2001). Carbon isotope values of authigenic carbonate cements precipitated from hydrocarbon-bearing fluids show significant differences that can be used to indicate the direction of hydrocarbon migration (Boles et al., 2004; Zhu, 2007). If mantlesourced water intrudes into a petroleum system, rare gas isotopes can be used to trace natural gas migration (Liu, 1993; Xu, 1996a, 1996b). The rare gases are characterized by enrichment of 3 He and 40 Ar. Their intrusion would increase the ratios of 3 He/4 He and 40 Ar/36 Ar in natural gas. This increase can be used to identify the crust-mantle material exchange process and the amount of contribution from mantle materials, and to infer the migration and accumulation process based on the reconstruction of geological processes in the study area. In spite of the existing problems, these methods have a great potential in the study of hydrocarbon migration in complex basins.

1.2 Geodynamics and Methods of Quantitative Analysis

25

1.2 Geodynamics and Methods of Quantitative Analysis Traditional geology is basically descriptive. Quantitative analysis is the goal of geological research and the direction of geology in the realm of sciences (Allen & Allen, 1990). The development of geology from qualitative to quantitative has been progressive. In the past, most of the so-called dynamic studies in geology used the method of logical reasoning to infer the mechanism of the occurrence and evolution of geological phenomena and described the processes qualitatively. The phenomena are difficult to document in space and cannot be duplicated in time, and are the final product of various complex geological factors and processes during a geological period. Therefore, qualitative methods inevitably lead to multiple solutions and uncertainties in solving geological problems. As a fluid mineral, a myriad of processes and factors, such as tectonic activity, sedimentary environment, temperature–pressure field, stress field, and fluidchemical environment, during basin evolution may significantly influence the variable processes during hydrocarbon generation, migration, and accumulation. These factors interact and dynamically couple with each other in time and space, resulting in extremely complex hydrocarbon generation, migration, accumulation, and reservoir formation. Quantitative research in dynamics is the only way to deepen our understanding of these processes and factors.

1.2.1 Geodynamics Originally, dynamics is a branch of theoretical mechanics. Based on Newton’s Law of motion, dynamics focuses mainly the relationship between the force acting on an object and its motion. The object is macroscopic; and the speed of its motion is much less than that of light. However, since the concept of dynamics was introduced into the study of geology, its connotation and extension have undergone significant changes. Scheidegger (1982) defines geodynamics as the continuous movement of matter and energy in the earth’s interior, which transfers deep matter and energy to the shallow part of the earth through various tectonic movements, controls the characteristics of earth’s crust, and interacts with the earth surface forces to form various geological features. In other words, geodynamics studies the geometric patterns of observed crustal tectonic changes and their movement and evolution, and attempts to explore the sources of dynamic forces, mode and process of tectonic changes from a mechanical point of view (Ma & Gao, 1996). Geodynamics is a dynamic analysis of the macroscopic behaviors of various tectonic movements on the earth. Therefore, it applies methods of dynamics to study the behavior of the earth system (Fu & Huang, 2001). In fact, the concept and connotation of geodynamics are closely related to the characteristics of geoscience itself, such as non-reproducibility in space and time, incompleteness of observed data, and relative simplicity of research methods.

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Dynamics analysis is essential in geological research to study the genesis mechanism and evolution process of geological phenomena (Bates & Jackson, 1984). The study of HC migration and accumulation mechanisms will naturally use the idea and method of geodynamics. Because the environmental conditions of HC migration and accumulation are complex and ever-changing and the migration process is elusive, the study must be a complex systematic engineering. It is far from enough to rely solely on the cumulation of geological data and the experience of geologists. And qualitative research is often difficult to consider the interaction of many geological factors and their coupling interaction, and cannot solve the multi-solution difficulties (Luo, 2003). Therefore, only through quantitative dynamic analysis can we get more strict constraints from the aspects of time, space and influencing factors, understand the essential characteristics of geological phenomena, and solve the problems of HC migration and accumulation mechanism and process (Luo, 2008; Yang et al., 2002a, 2002b). Quantitative analysis is inevitable in HC migration accumulation research.

1.2.2 Development of a Geological Model Geological process is very complex. A geological phenomenon may have formed by many tectonic activities and influenced by many geological factors processes. It is impossible to document every detail of the phenomenon and reproduce every step of every geological process it has experienced. Previous studies have found that many complex geological phenomena and processes in basins can be simplified if our observation focuses on some time intervals and/or a specific spatial range, when and where one geological process plays a critical role (Phillips, 1991). Therefore, it is not necessary to describe all the components and processes of a geological phenomenon in a quantitative analysis. As long as the contents relevant to the studied problem can be observed within a specific time and space range, or/and confined to certain conditions, a simple and clear relationship can be established that will be easy to be set up mathematically, so that the equation describing the system can be greatly simplified. Subsequently, we can quantitatively acquire the understanding of some essential characteristics of the phenomenon. This is the research method of scale analysis. For geological problems, scale analysis is an important step to realize the modeling method. Strictly speaking, all geological bodies are heterogeneous, which is determined by various controlling factors of variable scales. Depending on the accuracy of research means and research purposes, the study targets have to be regarded as homogeneous so that the existing knowledge can be used to solve the problems. However, a geologic body can be regarded as homogeneous only at a specific scale and could be highly heterogeneous at some other scales. For example, the minerals that make up the grains can be regarded as homogeneous at the scale of rock particles, but at the scale of a rock, the rock may be heterogeneous in mineral composition; conversely, a rock body that is highly heterogeneous at a specific scale may be considered as

1.2 Geodynamics and Methods of Quantitative Analysis

27

homogeneous on the other scale (de Marsily, 1981). For example, various properties of a fractured rock are heterogeneous at the fracture scale, but at the scale of a bed, some of these properties can be considered as homogeneous (Marle, 1965). The scale analysis requires an in-depth understanding of the role of each controlling factor and its effective temporal and spatial ranges. As a result, appropriate spatial and temporal scales can be selected according to the purpose of the study, so as to ensure that the phenominons to be studied is best manifested in the selected scales and that its essential characteristics will not be affected when the less important factors are ignored. In addition, various confining conditions need to be defined; the related problems outlined; and the main research contents and their background context clarified. Subsequently, an appropriate geologic model can be established. Within the framework of the model, various quantitative analyses can be carried out to quantify the physical properties of one aspect of the geological phenomenon. Eventually, some essential characteristics of the phenominen will be better understood through simulation analysis. Quantitative simulation analysis in the context of a geological model is a reliable and realistic method for quantitative research in geology. Its importance has long been recognized as a critical component of laboratory analysis. The so-called simulation is an operation to reproduce the occurrence and process of a phenomenon based on detailed and thorough observations. The basic requirement for an insightful simulation analysis is a solid geologic model (Luo, 1998). A suitable model must satisfy two conditions: first, it must include and accurately describe the main attributes and their relationships of the phenomenon to be simulated; second, the simulation analysis must be completed in a reasonable time. Hence, the model has to be simplified to some degree from the actual phenomenon to accentuate the main points to be studied. In light of the vast space and long time span in the formation of geological phenomena, the dynamic relationship among various processes must be taken into account in model construction in order to make the model comparable with the reality (Thomas & Clouse, 1995). Models may have many levels and types. They can be either geological (Athy, 1930; Allen & Allen, 1990; Wilson, 1975), physical (Chen, 1998; Li, 1979), mathematical (Bredehoeft & Hanshaw, 1968; Carslaw & Jaeger, 1959; Smith, 1971), or numerical (Bethke et al., 1988; Lerche, 1990; Luo, 1998).

1.2.3 Methods of Quantitative Research The history of quantitative geological analysis can be traced back to the beginning of the development of geology (De Marsily, 1981). Quantitative methods used in geological research mainly include geological statistical, physical simulation, mathematical modeling and numerical modeling analyses. These methods may be applied to various aspects of the study of hydrocarbon migration and accumulation mechanisms (Jin & Zhang, 2005).

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1. Methods of Geological Statistical and Probability Analyses Geological statistical and probability analyses can extract some trends from geological data. They are effective methods in quantitative studies of geological problems (Hou, 1998). All geological problems are influenced and controlled by multiple factors and, thus, are complex and multivariate in nature. Multivariate statistics has long been basic to mathematical geology and one of the major methods in quantitative study of petroleum geology (Kang, 2005). At present, commonly used multivariate statistical methods include regression analysis, trend surface analysis, cluster analysis, discriminant analysis, principal component analysis, factor analysis, correlation analysis, correspondence analysis, non-linear mapping analysis, canonical correlation analysis and Markov model analysis (Li & Zhao, 1998). Depending on the research objectives and data type and availability, suitable multivariate statistical analyses can be used to determine the influence, relationship, and relative importance of various factors to explore the origin of a geological phenomenon and conduct quantitative prediction. Multivariate statistical analysis has been widely used in petroleum geology research. Some scholars have carried out quantitative research on hydrocarbon accumulation by using multivariate statistical analysis of a large amount of data in exploration and production (Sui, 2005; Pang, 2007), such as quantitative prediction of the level of hydrocarbon charging and oil saturation in sandstone reservoirs (Zeng, 2003; Zhang et al., 2004b) and oil-column height in stratigraphic reservoirs (Zhao, 2010). The geological data (e.g., reservoir parameters) obtained through experiments and observations commonly show both regional spatial patterns and local variations. Traditional statistical methods have limited applicability because they do not consider spatial continuity and correlation of geological parameters. Thus, geostatistical methods should be used (Li & Wang, 2006). The theoretical basis of geostatistics is the theory of regionalized variables. Variation function (variogram function) is the basic tool. Geostatistics is a geological mathematical method for quantitative study of deterministic and stochastic geological phenomena in space (Hou et al., 1998). It fully considers the spatial trends and variations of geological parameters and correlation and dependence of parameters among samples. A geological statistical model conforming to geological processes can be used to effectively characterize the spatial variations and distribution of many geological parameters. With the advancement in the theory and method of geostatistics and related software, geostatistics has become a common method in petroleum geology. The main applications include prediction of spatial distribution of reservoir parameters, such as including thickness, porosity, permeability, and oil saturation, research on reservoir heterogeneity and anisotropy, and reservoir modeling (Kang, 2005; Qiu & Jia, 2000; Srivastava, 1994; Wu, 1999; Wu & Li, 2005). The concepts and methods in these applications can be used to evaluate HC migration pathways by quantitatively analyze the connectivity of sandstone carrier beds (Lei et al., 2014; Zhang et al., 2010). However, in practical applications, problems remain for geostatistical methods. Geostatistical analysis must discriminate the sources and reliability of geological

1.2 Geodynamics and Methods of Quantitative Analysis

29

data, eliminate or reduce various influences caused by nondeterministic factors on geological parameters and statistical results. Most importantly, geostatistical analysis must be done with correct geological concepts and models, so that the spatial stationary conditions and intrinsic hypothesis are satisfied. 2. Physical Modeling Method In geological research, physical modeling is designed to simulate geological processes and analyze quantitatively the mechanisms and processes in the formation of geological phenomena in the laboratory. Physical models are built based on our understanding of geological models. To construct a good physical model, the dominant elements that may characterize the study target and their relationship need to be identified in order to establish the dynamic similarity between the physical model and target based on the basic principles of rock mechanics (McClay & Bonora, 2001; Thomas & Clouse, 1995). The similarity may be manifested in geometry, movement, thermodynamics, forces, or boundary conditions, and so on. Hydrocarbon migration and accumulation are dynamic subsurface processes occurred in the geological past and cannot be observed directly (England et al., 1987; Hindle, 1997; Schowalter, 1979). Therefore, simulation experiment is an important means to study the process and mechanism of HC migration and accumulation (Catalan et al., 1992; Zeng, 2000; Luo et al., 2004; Jin & Zhang, 2005). Simulation experiments, designed for subsurface dynamic environment and conditions, can directly observe the hydrocarbon migration process and various phenomena and document various dynamic parameters to identify dynamic factors and mechanisms of hydrocarbon migration and accumulation (Lenormand et al., 1988; Luo et al., 2004; Vasseur et al., 2013). The results of physical modeling provide theoretical basis and appropriate mathematical models for quantitative analysis of hydrocarbon migration and accumulation at a basin scale. Since the beginning of the twentieth century, many scholars have paid attention to physical simulation of secondary migration and accumulation of oil and gas. A large number of simulations have been carried out using various devices and models (e.g., Catalan et al., 1992; Dembicki & Anderson, 1989; Emmons, 1924; Hubbert, 1953; Luo et al., 2004; Lenormand et al., 1988; Schowalter, 1979; Selle et al., 1993; Thomas & Clouse, 1995; Yan et al., 2012a, 2012b; Zeng et al., 2000; Zhang et al., 2003a, 2004a, 2007a, 2007b) and improved our knowledge on phase-state, driving forces, pathway characteristics, and influencing factors of secondary hydrocarbon migration. The models have been constructed using etching glass, glass bead, artificial sandstone, and real cores. The etching model has basic characteristics of reservoir pore structure. It is usually made on flat glass by photochemical etching or casting process, and mainly used to study hydrocarbon migration mechanism at the pore scale (Lenormand et al., 1988). The glass bead model is used to simulate oil migration mechanism (Catalan et al., 1992; Luo et al., 2004; Thomas & Clouse, 1995; Tokunaga et al., 2000; Yan et al., 2012a; Zeng et al., 1997) and is relatively simple and easy to observe various phenomena. The artificial sandstone and real core models are generally considered to be closer to the actual reservoirs and their simulation results

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more credible. Their disadvantages are poor visualization and relatively complex manufacturing process (Selle et al., 1993). Laboratory experiments for the study of morphological characteristics of hydrocarbon migration pathways began with Schowalter (1979). He found that the injection pressure required for non-wetting phase fluids to pass through rock samples filled with wetting phase fluids is only slightly higher than the expelling pressure, and the corresponding saturation of non-wetting phase when the oil travels through the samples is generally less than 20%, some even less than 5%. This indicates that secondary oil migration is highly heterogeneous. Oil migrates only in some of the pores with the lowest capillary resistance. Once some very narrow migration pathways formed, the subsequent migration may follow them. Similar experiments were carried out by other researchers (e.g., Catalan et al., 1992; Dembicki & Anderson, 1989; England et al., 1987; Thomas & Clouse, 1995); and their results substantiated Schowalter’s findings. Selle et al. (1993) established an experimental facility for direct observation of oil migration pathways in reservoir rock samples by using a gamma ray absorption method. They concluded that the volume of migration pathways is much smaller than that of the conduit. With the advancement of experimental simulation techniques and methods, the devices have changed from simple assembly to large integrated and automated systems to simulate and analyze oil and gas migration and accumulation under more complex conditions. The improvement on instrument resolution and data processing technology have greatly increased the accuracy of experimental results. The introduction of advanced testing methods, such as Gamma ray decay (Illangasekare et al., 1995), X-ray decay (Liu et al., 1993), conductivity (Lalchev et al., 1997), and NMRI (Nuclear Magnetic Resonance Imaging, Miao et al., 2004), makes the experimental samples not limited to transparent porous media, but also in opaque media, such as real cores. The measurements on rock physical properties and fluid saturation distribution can be quantitatively done in real time (Yan et al., 2012a, 2012b). The similarity of experimental conditions to those in the surface in geometry, kinematics, thermodynamics, and dynamics determines whether physical simulation results reflect the actual hydrocarbon migration and accumulation process. Adequate geological models and key factors must be designed to match, as closely as possible, the subsurface geological conditions. Boundary conditions of the models should be selected to fit for specific research problems. Otherwise, the experimental results will be far off from the geological observations and biased or even misleading. 3. Numerical modeling method Because of the complexity of geological problems, there are few cases for which mathematical models can be established to obtain analytic solutions (Luo & Vasseur, 1997; Smith, 1971). Thus, mathematical models are not widely used. Since 1980s, due to the advancement of computer technology and numerical calculations, numerical modeling utilizing large discrete numerical calculation, such as finite element and finite difference methods, developed rapidly. It provides convenient and realistic methods and tools for quantitative analysis of geological phenomena (Ungerer et al., 1984). Basin modeling technology has advanced significantly to quantitatively

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simulate the formation and evolution of petroliferous basins and associated oil and gas generation, migration and accumulation (Hantschel & Kauerauf, 2009). Sedimentary basin modeling technology combines numerical modeling and basin dynamics, and is effective for quantitative study of geological phenomena and events in basins (Allen & Allen, 1990; Hermanrud, 1993). By the early 1990s, basin modeling methods had been commercialized all over the world (Burrus et al., 1991; Hermanrud, 1993; Pang, 2003). The results of basin modeling have greatly improved our understanding of sedimentary basins and the success rate of exploration and development of mineral resources (Luo et al., 2014; Gusterhuber et al., 2013). Advanced physical simulation and integrated numerical basin modeling can quantitatively describe the evolution and processes of various geological phenomena in sedimentary basins. The models effectively link subsurface stress, geothermal, and fluid pressure fields, and apply basic equations of various physical fields to describe the occurrence and evolution of individual processes to a great depth (Dickinson et al., 1997; Hantschel & Kauerauf, 2009; Lerche, 1990; Luo, 1998; Ungerer et al., 1990). Previous researchers have proposed several methods in numerical simulation of secondary migration of oil and gas, such as hydraulic potential (Hubbert, 1953), multi-phase Darcy flow (Ungerer et al., 1990), stream-line (Lehner et al., 1988), flow path (England et al., 1987; Hindle, 1997; Sylta, 1993), hybrid (Hantschel et al., 2000), and invasion percolation (Wilkinson & Willemsen, 1983) methods. Welte et al. (2000) classified these methods into four simulation techniques: fluid potential, multi-phase Darcy flow, hybrid, and invasion percolation. These methods have their respective advantages and disadvantages (Shi, 2009; Welte et al., 2000), can partly solve the problems encountered in oil and gas migration, but still cannot fully adapt to the complexity of oil and gas migration under realistic basin conditions. Welte et al. (2000) suggested that a hybrid method that combines two other methods may be the best approach. The multiphase Darcy flow method is the conventional and most reliable hydrodynamic simulation method and uses a group of partial differential equations to set up a mathematical model. The characteristics of temperature and pressure fields of oil, gas and water and their changes can be solved by approximation (Ungerer et al., 1984, 1990). The two-dimensional two-phase secondary migration models based on Darcy’s Law are often used to simulate the pathways characteristics and calculate average velocity of hydrocarbon migration in permeable strata and faults (Bekele et al., 1999; Burrus et al., 1991; England et al., 1987; Hippler, 1997; Yuan et al., 2002). However, two problems arise from the application of this method at a basin scale. First, a large amount of data is needed to establish the model, and difficult to be obtained from existing exploration data in light of the heterogeneity of oil and gas migration. Second, execution of the current algorithms is time-consuming. Thus, only models with a coarse grid can be run, resulting in low-resolution output (Bekele et al., 1999). Finally, the complexity of the approximate solution often leads to problems with convergence and stability (Shi, 2009).

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Based on observations of two-phase displacement, Wilkinson and Willemsen (1983) proposed an inversion-percolation model by abstracting the pore throat structure of porous media into a regular grid, where pores and throats were represented by nodes and lines, respectively. In such a model, the selection of sizes of pores and throats for the displaced-phase fluid flow is based on a specific stochastic function, which is a statistical model and does not include physical time. Physically, secondary petroleum migration can be considered as a slow invasion process (Wagner et al., 1997). Thus, the invasion-percolation model is the simplest but most effective method to study petroleum migration (Hirsch & Thompson, 1995; Shi, 2009). From the physical point of view, there is no obvious difference between the invasion percolation and the flowpath methods. The secondary migration of oil and gas can be regarded as a microscopically-discontinuous non-uniform movement along the dominant conduits and can be completed instantaneously, so the scale effect of time can be ignored (Shi, 2009). However, the methodology and numerical realization of the two methods are quite different. The flowpath method considers that reservoir rocks can be invaded under sufficiently large oil and gas pressure during a long enough geological time, regardless of permeability (Dembicki & Anderson, 1989; Hindle, 1997), and generally considers only the role of fluid dynamics, although England et al. (1989) attempted to incorporate the effect of capillary force). However, this method cannot express the microscopic relationship between migration force and resistance, and has difficulty to show the heterogeneous characteristics of hydrocarbon migration pathways (Luo, 2003; Shi, 2009). It must be recognized that migration in sedimentary basins is a geologic, not a single physical, process (Luo, 2011). Thus, the simulation of migration should be descriptive but not dynamic (i.e., to solve numerous physical equations), because the latter would be too complex to be realized computationally. Migration simulations based on the invasion-percolation theory may satisfactorily explain migration processes in heterogeneous media (Carruthers, 2003; Luo, 2011; Luo et al., 2015, 2007a, 2007b). The most significant advantage of the invasion percolation-based method is that the bi-phase (water and oil or gas) expulsion pathways in porous media can be well characterized (Meakin et al., 2000; Wilkinson & Willemsen, 1983; Zhou et al., 2006). Furthermore, the method can be implemented with simple algorithms to simulate migration in models that contain tens or hundreds of millions of grid cells and be completed in a matter of minutes (Welte et al., 2000). Thus, the invasion-percolation model provides an ideal solution for modeling secondary petroleum migration (Carruthers, 2003; Luo et al., 2020).

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1.3 Dynamics in Hydrocarbon Migration and Accumulation The purpose of studying hydrocarbon migration is to understand the dynamic conditions and processes of hydrocarbon accumulation and preservation. Because of limitations on observations, laboratory experiments or numerical simulation analysis generally regard the process of oil and gas migration as a single physical process. Thus, practical applications of the methods and results of migration research often oversimplify the description of migration process with limited consideration of the various geological effects and influencing factors. Only qualitative results and interpretations can be derived. To accurately and quantitatively describe and analyze the hydrodynamic conditions and processes of oil and gas migration, accumulation and dispersion, a migration-accumulation unit needs to be defined, which consists of oil and gas sources, migration conduits, and migration and accumulation traps and other basic units in a temporal context of the entire oil and gas migration process. Dynamics of hydrocarbon migration and accumulation and related methods are the recent development of the petroleum geology (Yang et al., 2002a), that have advanced petroleum geology from qualitative and static description or comprehensive evaluation of single-factor migration and accumulation conditions to quantitative and dynamic study of mechanisms and processes, by integrating a variety of factors. This reflects the inexorable trend in petroleum geology research and is necessary to meet the need for oil and gas exploration in complex geological systems (Luo, 2008). This section introduces the origin and necessity of migration-accumulation dynamics research and expounds on the concept and details of the research using the concepts of geodynamics.

1.3.1 Development and Application of Migration and Accumulation Dynamics in Petroleum Geology Petroleum geology is a discipline that studies the generation, migration and accumulation of oil and gas in sedimentary basins. Its main research targets include the principles and processes of hydrocarbon generation, migration, accumulation, sealing, trapping, and preservation, among which hydrocarbon generation and migrationaccumulation are the two core issues. The study of migration-accumulation dynamics has a long history and has progressed with intensified exploration and advancement in science and technology. The concepts and methodology have been guiding exploration since the beginning (Selley, 1998). As early as 1861, Canadian geologist Hunter put forward the “anticline theory”, whose theoretical basis is that oil and gas migrate and accumulate in the updip direction under buoyancy in water-saturated strata (Selley, 1998). The idea that oil tends to accumulate in anticlines has guided exploration decision-making for a long time. By

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the beginning of the twentieth century, Venezuela and the United States had discovered large non-anticlinal reservoirs successively. The application of seismic imaging technology facilitated the development of the concept of trap. Geologists realized that subsurface movement of oil and gas is controlled by fluid dynamics through migration over some distance, and the traps are at the end point of oil and gas migration (Levorsen, 1954). In the 1950s, hydrodynamic theory was first introduced into the study of secondary hydrocarbon migration. Buoyancy, hydrodynamic, and capillary forces were identified as the dynamic factors controlling hydrocarbon migration and accumulation (Hubbert, 1953); and hydrocarbon migration—accumulation was regarded as a complete dynamic process (Selley, 1998). From the late 1960s to the 1970s, great progress has been made in the study on the kinetics of petroleum generation. The theory of an organic origin of hydrocarbon generated by degradation of kerogen was established (Durand et al., 1970; Tissot & Welte, 1984). As a result, a comprehensive understanding of the process of hydrocarbon generation, expulsion, migration and accumulation started to form. At this time, although quantitative study on hydrocarbon generation based on thermodynamics was carried out, little is known about the flow characteristics of fluids in the basin, especially the mechanism and process of oil and gas migration and accumulation. The analysis of migration and accumulation was only qualitative on migration phase state, driving and resistant forces, conduit, direction, distance, and time (Li, 2013). The research focused on basic elements and processes, such as source rocks reservoir, caprock, trap, migration, and preservation conditions, effects of basin evolution on these factors, and temporal relationship among the processes (Zhang & Fang, 2002). Since the 1980s, the progress in petroleum geochemistry has made it possible to identify the genetic relationship between oil and gas in separate reservoirs in a basin (Larter & Aplin, 1995; Surdam et al., 1989). This provides an important means for the study of hydrocarbon migration and accumulation. In the same period, the mechanical expression of fluid potential as a driving force for oil and gas migration and accumulation have been better defined (England et al., 1987; Hindle, 1997; Hunt, 1990). The development of quantitative basin analysis and basin simulation technology has made paleo-hydrodynamic simulation based on fluid potential a main (Hindle, 1997; Welte et al., 2000). The mechanisms and processes of primary hydrocarbon migration have also been achieved deeper understanding (Chen, 1995; Chen & Luo, 1987; Chen & Tang, 1981; Magara, 1978; Ungerer et al., 1990) beyond just theoretical discussion. The results and methodology have been applied in the study of realistic accumulation process (Luo, 2001). Studies on basin fluid dynamics in flow mechanism andw pattern, fluid-rock interaction, and migration and accumulation (or mineralization) have been active (Dickinson et al., 1997; Ortoleva, 1995; Surdam et al., 1989). Finally, tracing migration pathways, reconstructing paleofluid activities, and identifying the role of faults in migration and accumulation, and age determination of accumulation using geophysical and geochemical techniques have made significant progress (Karlsen & Skeie, 2006; Luo, 2008). Since 1990s, petroleum geology research has entered the realm of system theory through systematic research on dynamic migration and accumulation processes and

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conception of the petroleum system theory and related methods (Magoon & Dow, 1992). It is recognized that hydrocarbon generation and accumulation occur in the natural systems in sedimentary basins, which include active hydrocarbon-generating sags and other geological elements and processes necessary for hydrocarbon generation, migration, and accumulation (Magoon & Dow, 1992, 1994; Zhao, 2002). The theory of petroleum system offers a foundation for creative thinking and methodology in the study of the entire process from hydrocarbon generation to accumulation (Demaison & Huizinga, 1991; Perrodon, 1992; Yang et al., 2002a), and signifies an important stage in the development of petroleum geology. The methodology in the context of petroleum systems (Magoon & Dow, 1994) have prompted and stimulated a dynamic and comprehensive approach in the study of hydrocarbon accumulation process (Zhang & Fang, 2002), which resulted in remarkable success in exploration (Hu & Zhao, 2001; Zhao & He, 2002a, 2002b). However, its limitation has been shown in its application to exploration under complex conditions of superimposed basins in China (Tian et al., 2007; Yue et al., 2003). In superimposed basins, multiple petroleum systems overlap with each other (Zhao & He, 2002a, 2002b). Multi-stage hydrocarbon generation, migration, accumulation, adjustment and transformation in co-existing petroleum systems generate complex and divers hydrocarbon distributions (Jin & Wang, 2004). Many accumulations with varying dynamic characteristics occur in one petroleum system and, on the other hand, some accumulations with similar dynamic characteristics may belong to different petroleum systems (Jin et al., 2004). This complexity makes it difficult to clarify the process of hydrocarbon migration and accumulation from source to traps and, as a result, hamper the research on and understanding of the mechanisms of hydrocarbon migration and accumulation at variable stages and locations during basin evolution (Luo, 2008; Yang, et al., 2002a, 2002b). Quantitative dynamics research can effectively be used to solve the problems of oil and gas migration and accumulation under complex geological conditions. For this reason, the concept of reservoir forming system is proposed to unravel the complexities. The research will analyze dynamic conditions of migration and accumulation, identify dynamic factors controlling generation, migration and accumulation, reveal mechanisms of migration and accumulation, trace migration pathways, and quantitatively predict directions of migration and scopes of accumulations. It will help establish an efficient exploration strategy in complex basins, and facilitate quantitative evaluation for oil and gas exploration (Yang et al., 2002a). Chinese scholars have therefore proposed the concept of dynamics of hydrocarbon migration and accumulation on the basis of characteristics of many superimposed basins in China (Gong & Yang, 1999; Hao, 2000; Kang & Guo, 1998; Luo et al., 2008; Luo et al., 2020; Tian, 1996; Yao & Sun, 1995; Yang et al., 2002a; Zhang & Fang, 2002). The concept emphasizes quantitative study of dynamic environment, process and result of hydrocarbon accumulation in order to reveal the mechanisms and distribution patterns of hydrocarbon accumulations. These researches have modified and extended the concepts and methods of petroleum system theory to account for the complex processes of hydrocarbon generation, migration, and accumulation in complex basins. Physical and numerical simulation technology has been

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widely used in these researches. Advancement has been made in buoyancy-driven hydrocarbon migration and accumulation mechanism, hydrocarbon-related chemical kinetics process, and quantitative characterization of hydrocarbon carrier system (Hao et al., 2000; Luo, 2011; Luo et al., 2020, 2007a, 2007b). Based on the models of burial, overpressure evolution, and thermal and hydrocarbon generation histories of a basin, a quantitative simulation method for hydrocarbon generation, migration and accumulation has been preliminarily developed and successfully applied in exploration in some basins (Lei et al., 2014; Luo et al., 2007b). In the course of petroleum geology, it has always been the direction and goal of petroleum geologists to study the process of hydrocarbon migration and accumulation using methods of dynamics. Every leap in petroleum geology theory has been the result of a breakthrough in dynamic research on an aspect of migration. With the current advancement in science and technology, including petroleum geology itself, the era of comprehensive and quantitative study on dynamics of the hydrocarbon migration and accumulation to further understand the mechanisms and processes has come, as the inevitable future direction in petroleum geology research (Luo, 2008).

1.3.2 Concept of Hydrocarbon Migration-Accumulation Dynamics Hydrocarbon migration and accumulation dynamics was initially proposed by Tian et al. (1996). The concept includes two basic parts: one is the dynamic conditions of accumulation; and the other is the dynamic processes and results of migration and accumulation under specific dynamic conditions in the geological history. Tian et al. (1996) think that the hydrocarbon migration and accumulation dynamics is founded by geodynamics; the dynamic system and process of migration and accumulation is the core and link the generation, migration, accumulation, and dispersion of oil and gas as an entity. In this context, the generation, migration, accumulation and distribution of oil and gas in the basin can be better understood to guide oil and gas exploration. Tian et al. (2007) further pointed out that the reservoir forming dynamic system is a complex natural system of fluid migration in the basin, which contains several connected subsystems. Similar to the reservoir forming dynamic system, Kang and Guo (1998) proposed the “hydrodynamic system for hydrocarbon migration and accumulation”. It contains a solid framework and internal fluids (oil, gas and water). The system has relatively stable boundaries; and all fluids in the system constitute a flow unit under a unified pressure system. The hydrodynamic system may be characterized as gravity driving, compaction driving, fluid compartmentalization, or stagnating ones. Zhang et al. (1997) thought that a “dynamic system of hydrocarbon accumulation” is a geological unit with a unified energy source for hydrocarbon migration and accumulation, and the study of the dynamic conditions of hydrocarbon migration and accumulation is the key to understand the system. Above

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all, these concepts differ from the petroleum system theory in that they emphasize the dynamic conditions and processes during hydrocarbon migration and accumulation. However, the definitions of the reservoir forming dynamics system are still vague. Many scholars proposed that dynamics of hydrocarbon accumulation should be considered as an independent discipline. Hao et al. (2000) pointed out that migration and accumulation dynamics is to study comprehensively the formation, migration, and accumulation of oil and gas in basins, through the analysis of chemical and fluid dynamics and kinematic processes of energy field evolution and its controls, within the framework of basin evolution and evolution of migration conduits. The methods include geological, geophysical, geochemical as well as computer simulation technology. Zhang and Fang (2002) considered that the study of dynamics combines petroleum geology with geodynamics. In the dynamics study, a basin is the background, oil and gas the object, and petroleum system the unit to study hydrocarbon generation, migration and accumulation. The aspects of dynamics and its controlling factors include the dynamics in hydrocarbon generation, migration, accumulation, and accumulation preservation and destruction. Tian et al. (2007) further brought the aspects of tectonic, sedimentary, thermal, chemical, and fluid dynamics into the field of study, extending to the level of basin geodynamics. On the other hand, other scholars think that the hydrocarbon dynamics study is an extension of petroleum geology. Jin and Xin (1995) proposed the preliminary concept and regarded that the study focuses on the process of formation, evolution or destruction of reservoirs by various agents and forces in sedimentary basins that control the distribution pattern of oil, gas and water. The necessary aspects to be studied include the structural evolution of reservoirs and corresponding stress fields, diagenesis of pore space, and the characteristics and history of flows in permeable beds during migration and accumulation of oil and gas. The controlling factors of reservoir formation should be discussed. The study of hydrocarbon migration and accumulation dynamics provides a set of new research and prediction methods for oil and gas exploration and development. Yang et al. (2002a) think that this study is comprehensive, involving multiple disciplinaries. It interprets that processes of reservoir formation, including hydrocarbon generation, expulsion, migration and accumulation in the paleotectonic setting through the comprehensive quantitative study of temperature, pressure and stress in a specific stratigraphic unit that contains the source rocks and conduit systems for fluid flow. The unit of study may be a single petroleum system or a combination of several related petroleum systems,. The purpose of the dynamics study is to evaluate the plays and exploration targets based on the dynamic mechanism of hydrocarbon accumulation and to establish a set of practical methodology. In summary, the field of dynamics should be, in essence, and the methodology to study the dynamics of hydrocarbon migration and accumulation, with a focus on the geological conditions, influencing factors, dynamic mechanisms, and evolution processes. From the current level of petroleum geology knowledge acquired by oil and gas exploration and development, what we have done or can do should be “the dynamics study of hydrocarbon migration and accumulation,” rather than

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the establishment of “the discipline of hydrocarbon migration and accumulation dynamics”. Therefore, quantitative research on the process and mechanism of hydrocarbon migration and accumulation is critical to developing the methodology in dynamics study. The study should not focus on a specific unit or component in a petroliferous basin. It is the development of the theory and methodology of petroleum system to understand the complex processes of hydrocarbon generation, migration and dispersion in China’s superimposed basins. It provides research ideas and methods in petroleum geology with the progress of science and technology. The variable geological conditions and environments of a basin result in a variety of dynamic mechanisms and processes. The complex geology of superimposed basins requires that any research pays more attention to the mechanism and process of hydrocarbon migration and accumulation, and their quantitative analysis. Luo (2008) defined the dynamics study as a dynamic methodology for quantitative studies on the geological conditions, influencing factors, and dynamic mechanism and evolution process of hydrocarbon migration and accumulation. The study should be a comprehensive analysis of hydrocarbon migration and accumulation process in the basin, and include: (1) identifying stages of hydrocarbon accumulation in the basin, and (2) differentiating and defining the basic units with an unified hydrodynamic environment in the duration of each one stage, where the processes from hydrocarbon source to reservoir may be established, (3) studying quantitatively the mechanism, controlling factors and dynamic processes of hydrocarbon supply, migration and accumulation in each unit, and (4) analyzing comprehensively migration accumulation processes in the hydrocarbon-bearing basin. Dynamics research emphasizes that quantitative study is an effective means to solve the dynamic problems in the process of migration and accumulation, and is necessary to link different accumulation factors and processes. In order to quantitatively study complex petroliferous basins, the study must focus on a single process in an unit of hydrocarbon migration and accumulation that is temporally confined by the stages of hydrocarbon accumulation and spatially by the hydrodynamic field (Fig. 1.4). A unified hydrodynamic environment is not only the prerequisite for quantitative research, but also the basis for delineating hydrodynamic units and determining migration-accumulation processes in them. In a conceptual unit, the hydrocarbon source is not limited to hydrocarbon generation and expulsion of source rocks, but also includes many other possible hydrocarbon providers, such as hydrocarbon re-generation and supply from spillover and destruction of pre-existing reservoirs. Hence, the charging stages in reservoirs must be identified to identify the migration and accumulation processes, and the destruction of pre-existing reservoirs, which would serve as a new source, and the formation of new reservoirs can be studied. The identification of hydrocarbon migration and accumulation dynamic units facilitates quantitative hydrodynamic analysis. The core task is to quantitatively analyze the characteristics and processes of hydrocarbon migration and accumulation of the unit in petroliferous basins and sub-basins. With the knowledge on

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Fig. 1.4 Migration-accumulation dynamic units and method of delineation. a In a source-reservoircap hydrodynamic uint active in the time period, the reservoirs can be delineated on the basis of intervening troughs in the fluid potential field. b Two stages of reservoir formation in one anticline trap. In each stage, the unit outlined by green dashed lines is the source-conduit-trap unit within a single migration-accumulation dynamic unit

the active time spans of individual MAUs and the relationship of supply, migration, accumulation and loss among the units, the cyclic processes of hydrocarbon generation, migration, accumulation, dispersion and re-migration at a larger scale can be interpreted, and the direction and location of hydrocarbon accumulation will be eventually determined (Fig. 1.5). The results may aid in resource evaluation and target prediction for oil and gas exploration. Figure 1.5 shows the complex distribution of oil and gas in a petroleum system, which has experienced multiple stages of structural activities, so that the oil and gas migrated and accumulated during different episodes are present in three traps. By analyzing the time of oil and gas charge and the processes of trap formation, four episodes of oil and gas migration and accumulation can be determined. The first episode of oil expulsion, followed by second episode of oil migration and accumulation caused by fault activities, and followed by the third oil expulsion from the source rock, and the last episode of gas expulsion from the source rock. As a result, seven MAU’ overall can be delineated. Figure 1.5a shows the first episode of oil accumulation, which can be divided into two MAU’: first, source rock expelled oil, which migrated upward in carrier bed 1 (CB1) and accumulated in trap 1 (TR1); second, source rock expelled oil to carrier bed CB2, which migrated upward in CB2 and accumulated in trap TR2. Figure 1.5b shows the second episode. Due to

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Fig. 1.5 Comprehensive analysis of hydrocarbon migration-accumulation processes in a play. The migration direction is marked by the green arrows for oil and the red arrows for gas. Individual MAU’ are outlined with dashed red lines. TR—trap, FT—fault, CB—carrier bed, SR—source rock

a basin uplift, hydrocarbon generation and expulsion in the source rock stopped; an open fault formed. Hence, the accumulated oil in trap TR2 escaped along the fault and became the source of hydrocarbon supply. The escaped oil migrated along the fault and CB1 upward and accumulated in TR1. As a result, TR2-fault-CB1-TR1 constitute a MAU. Figure 1.5c shows the third episode, when the basin subsided and the source rock re-entered into the hydrocarbon generation depth to expel oil. Two MAU’ formed: first, the source rock expelled oil, which migrated upward in CB1 and accumulated in trap TR1; second, the source rock expelled oil into CB2, and the oil migrated upward in CB2. At this time, the lower part of the fault was closed, acting as a seal in CB2, and forced the oil to accumulate in a fault trap TR3. Figure 1.5d shows the fourth stage. The basin continued to subside and the source rock entered the gas generation depth and expelled gas into CB1 and CB2. At this time, two MAU’ formed: first, the source rock expelled gas, which migrated upward in CB1 and accumulated in trap TR1; second, the source rock expelled gas, which migrated upward in CB2. At this time, the lower part of the fault was still closed to force the gas to accumulate in TR3. The scenarios in Fig. 1.5 demonstrate that for a petroleum system undergone multistage of basin evolution, the hydrocarbon migration and accumulation processes in geological history is very complex. It is difficult to directly carry out quantitative oil–gas migration and accumulation analysis for such a complex system. The MAU concept can be used to subdivide such a complex system into several simple MAU’. Each MAU can be analyzed independently, so as to conduct quantitative oil and gas migration and accumulation simulation. During the four stages of hydrocarbon migration and accumulation, different traps were charged with different components of hydrocarbons generated from different kitchens in different periods. If these kitchens and traps are considered as a whole system, the hydrocarbon migration and accumulation processes will be very complex

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and difficult to sort out. As a result, the analysis of the dynamics and mechanisms of migration and accumulation mechanism will easily go astray. However, when these traps and their related hydrocarbon sources are divided into several time and space-confined MAUs, the processes of hydrocarbon supplying, migration and accumulation in each unit become relatively simple to be studied by quantitative migration and accumulation analysis. The differences in composition, source and mixing modes of oil and gas in each stage of migration and accumulation make it possible to sort out the relationships among reservoirs and sources. Thus, the processes of oil and gas migration and accumulation may be reconstructed through the analyses of oil and gas filling stages and oil and gas compositions in each stage. For example, in the model in Fig. 1.5, three–four stages of oil and gas charging occurred in TR1, while only one stages of oil charging ocurred in TR2, and at least two stages of oil charging in TR 3. The oil and gas accumulation process can be reconstructed as long as the time and source of each episode of oil and gas charging are clear. Subsequently, the quantity of oil and gas and and the distribution of oil and gas accumulations can be determined. The dynamics study involves all aspects of petroleum geology. At present, the study can utilize and the existing knowledge of petroleum geology, including those of petroleum system. Some other studies, which are not directly related to the dynamics, can be excluded, such as tectonics, sedimentary dynamics, thermodynamics, and chemical dynamics. As a fluid natural resource, the fluidity of oil and gas determines that the processes of oil and gas migration and accumulation is the key for reservoir formation. These processes are controlled by hydrodynamic conditions and conduit systems, which change with time. Therefore, the dynamics study should grasp the flow characteristics of oil and gas, analyze the migration and accumulation of oil and gas from the perspective of fluid dynamics, and understand the dynamic background and conditions that can preserve the reservoirs after tectonic changes of the basin. The study must quantitatively depict the mechanism and process of oil and gas migration and accumulation. The main reservoir-charging periods of oil and gas need to delineated; the conduit framework in each oil and gas MAU need to reconstructed. This is because a specific conduit framework only relates to a specific reservoir unit over a specific geological time span. Only the conditions under which a major hydrocarbon migration took place in the MAU are critical. Therefore, the conduit framework or system is time-sensitive; that is, it changes with the evolution of basin. Therefore, the methodology of quantitative analysis is adopted in the study of hydrocarbon accumulation dynamics, where the migration and accumulation process from source to trap is the main line and the dynamic mechanism of hydrocarbon migration and accumulation is the core. The hydrodynamic characteristics of basin during the stages of hydrocarbon accumulation stages, are used to subdivide the complex petroleum systems into hydrocarbon MAUs. As a result, the complexity is simplified. In the context of interacting geological processes and factors, the dynamics studies focus on quantitative analysis of the timing of hydrocarbon accumulation, and dynamic characteristics and evolution of hydrocarbon migration and accumulation, and evolution of conduit systems.

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Within the space–time-specific migration and accumulation dynamic unit, the dynamic relationship between the driving force of hydrocarbon migration and conduits in key reservoir-charging period is integrated to quantitatively study the mechanism and process of hydrocarbon migration and accumulation. The oil and gas migration pathways are traced and the distribution patterns of oil and gas reservoirs are analyzed. Subsequently, the amount of loss and accumulation of oil and gas during different migration and accumulation stages may be estimated. The amount of reserve in each reservoir unit as well as the location of oil and gas accumulation location can be evaluated. The areas with major oil and gas accumulation will be determined. Eventually, the results will aid in the selection of exploration targets.

1.3.3 Aspects and Methodology of Hydrocarbon Migration-Accumulation Dynamics Studies The methodology of dynamics study is the extension and development of the methodology used in traditional studies of petroleum geology and petroleum system. It focuses on quantitative analyses of dynamic processes and mechanisms related to oil and gas migration, accumulation and dispersion. The other aspects, which are acquired through studies of petroleum geology and petroleum system may be directly utilized. The authors, based on their collective experiences in dynamics studies on hydrocarbon accumulation in basins of China in last four decades, have formed a set of operable methods and work flow that can be applied by other researches. These methods are illustrated through their research in four aspects, as described below. 1. Quantitative Analysis on Basin History and Evolution in the Study of Hydrocarbon Accumulation Dynamics Hydrocarbon accumulation in reservoirs is a process occurred in the history of a basin. In each stage of basin evolution, the pre-existing basin had been changed. Especially, the petroliferous basins in China generally exhibit the characteristics of multi-stage basin development, resulting in superimposed basins. The geological conditions and dynamic environments of hydrocarbon accumulation have been constantly changing during basin evolution. To study the dynamics of the hydrocarbon accumulation, it is necessary to fully understand the basin history and the characteristics of physical and chemical dynamic fields and the processes controlling hydrocarbon generation, expulsion, migration and accumulation. Since 1980s, basin analysis methods (Allen & Allen, 1990) and basin simulation techniques (Luo, 1998, 1999; Pang, 2003; Ungerer et al., 1990) have be developed to provide effective tools for quantitative analysis and description of basin evolution and characterization of hydrodynamic and chemical fields at various evolution stages (Lerche, 1990; Luo, 1998, 1999; Pang, 2003). The general tectonic and stratigraphic data and results can be directly utilized in basin analysis. So that the attention can be focused on quantitative analysis of

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the dynamic conditions, includinganalysis of basin burial history, documentation of geological events, so as to establish a reasonable geological model of basin evolution and to obtain key geological parameters. Basin simulation technology can accurately reconstruct basin morphology and geologic structures in different geological periods. The evolution and relationship of temperature, fluid pressure, and stress fields and chemical processes (e.g., water–rock interaction, mineral transformation, organic matter decomposition) in the basin can be quantitatively studied (Luo et al., 2003). Thermal evolution, hydrocarbon generation and expulsion history, fluid potential in targeted strata in different periods can be reconstructed (Luo et al., 2003, 2007a, 2007b; Qiu, 2004). (1) Establishment of geological basin model and simulation of basin evolution The geodynamic background, tectonic characteristics and evolution, sequence stratigraphic framework and basin filling history of a basin can be synthesized from results of previous studies. Based on the compiled geological conditions related to hydrocarbon accumulation, a geological model of the basin can be developed using various geological, geophysical and geochemical data obtained through oil and gas exploration. At the same time, the key geological parameters of a basin model, such as compaction coefficient, sediment erosional thickness, and physical properties of rocks and fluids need to be obtained. With respect to basin type and geological characteristics, a reasonable numerical method and its conditions should be determined. Then, and the evolution of the basin as well as the basin filling and burial history can be simulated using basin modeling software. (2) Characterization of Geothermal Field and Thermal Evolution The geothermal field can be characterized using the steady-state temperature measurement data in wells and thermal property data of rocks. The data can be used to construct maps and cross-sections to illustrate geothermal gradient and geothermal flow, and to analyze the thermal state and structural characteristics of basins. For paleogeothermal fields, the characteristics of lithosphere and basin evolution, tectonic activity, and magmatism should be comprehensively considered. Using a variety of data, such as fission tracks, U-Th/He thermal dating, vitrinite reflectance, asphalt reflectance, and inclusion homogenization temperature calibration, the thermal evolution history of the basin can be quantitatively simulated and reconstructed (Hu, 1995). Based on the reconstruction of paleogeothermal field, the maturation history of organic matter in source rocks can be determined to interpret the temporal and spatial distribution of effective hydrocarbon generation. (3) Characteristics and Evolution of Tectonic Stress Field Many in-situ stress field measurement methods are mainly used in the study of present stress field, such as piezomagnetic, stress relief, stress recovery, hydraulic fracturing, and borehole caving methods. Paleostress field was interpreted through geological analysis, by measurements and statistical analysis of various tectonic deformations on outcrops or cores. Meanwhile, the results of paleostress and strain

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analysis obtained from acoustic emission experiments are used as constraints; and numerical simulation method is used to reproduce the distribution of in-situ stress vectors in basin evolution, in order to study the evolution of tectonic stress and the effect of fault sealing and fluid movement. (4) Characteristics and Evolution of Fluid Pressure Field The measured data of DST (drilling stem test), RFT (repeat formation testing) and MDT (modular formation dynamics testing) can be used to analyze the structure and distribution of permeable formation pressure. Then, the formation mechanism of abnormal pressures in reservoirs may be studied qualitatively and/or quantitatively in combination with the actual geological conditions. Pressure anomalies in mudstones are usually estimated by using mudstone compaction curves at maximum burial depth. Paleopressures in reservoirs can be obtained from PVT thermodynamic simulation of fluid inclusions. Basin modeling is also used to quantitatively reconstruct the evolution and distribution characteristics of fluid pressures with the constraints of measured pressures. 2. Delineation of Hydrocarbon Migration and Accumulation Units Oil and gas MAUs are also called as oil and gas migration and accumulation systems, MAUs. Effective delineation of the is critical to studying quantitatively the hydrodynamic characteristics of hydrocarbon migration and accumulation. In the areas with relatively intense oil and gas exploration, it is necessary to focus on the units where reservoirs are present, in order to reduce the workload of research. Therefore, we must first determine the relationship between the discovered reservoirs and possible source rocks, and the main oil and gas accumulation periods and extent in different areas of a basin. Next, the history of fluid pressure and paleogeothermal fields, which are obtained by basin simulation, can be used to interpret the distribution of sources in each petroleum system. The migration and accumulation units can then be delineated according to the occurrence of carrier beds. The fluid potential characteristics in the main carrier beds during the critical periods and the source-reservoir relationship within each hydrocarbon migration and accumulation unit need to be analyzed to interpret the composition and spatial extent of the units. (1) Oil-Source Correlation and Hydrocarbon Migration Tracing Modern organic geochemical testing techniques are used to study the organic geochemical characteristics of discovered oil and gas and source rocks. A variety of biomarkers and their combinations are used to classify the genetic types of oil and gas, to correlate oil–oil (gas–gas) and oil (gas)–source, and interpret the genetic relationship between discovered oil and gas in different reservoirs and their relationship with source rocks. The trend of geochemical parameters reflects oil and gas migration history and can be used to semi-quantitatively analyze the oil and gas migration direction and pathways (Pang et al., 2004; Wang et al., 2000). The results will provide a foundation for analysis of migration-accumulation unit and the charging of reservoirs.

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(2) Stages and Timing of Hydrocarbon Accumulation The timing of hydrocarbon accumulation is the key link in quantitative study of accumulation dynamics. The timing will be used to study the mechanism of hydrocarbon accumulation and to reconstruct the process of hydrocarbon migration and accumulation. The accuracy of petroleum accumulation chronology has been improved significantly with the development of new technologies, such as fluid inclusion analysis and radiometric isotope dating. The method of reservoir fluid inclusion analysis (Mclimans, 1987) and K–Ar or 40 Ar/39 Ar isotopic dating of authigenic illite (Hamilton et al., 1989; Lee et al., 1985) can be used to interpret the time of reservoir charging. The results can be compared with those of traditional methods, which can determine the time of trap formation, hydrocarbon generation and expulsion history of source rocks, and saturated pressure of oil in traps. (3) Evolution of Basin Fluid System and Potential Field The distribution patterns of fluid properties at different scales and in different stratigraphic intervals are important clues to determine hydrocarbon migration and accumulation. They can be mapped by using the chemical composition of oil, gas, and formation water and their stable isotope compositions as well as other geochemical data. The characteristics of fluid pressure field can be used to delineate the fluid dynamic systems. The characteristics, range of interaction, and evolution of fluid systems can be interpreted. Subsequently, and the results of quantitative simulation of paleofluid pressure during basin evolution can be used to interpret the evolution of paleofluid potential field, which can be illustrated as fluid potential maps for individual hydrocarbon accumulation periods. (4) Delineation of Hydrocarbon Migration and Accumulation Units The oil and gas MAUs during the main accumulation stages can be delineated on the basis of the boundaries of fluid potential field and carrier beds, and source-reservoir relationship within each unit. Other information also to be used as the circumstantial evidence, including the extent of effective hydrocarbon sources, organic geochemical characteristics, the distribution of discovered hydrocarbon reservoirs, etc. 3. Delineation and Quantitative Characterization of Hydrocarbon Conduit Systems During a main accumulation stage, different types of conduits, such as sandstone carrier beds, faults, and unconformities, often show lateral changes, forming a complex three-dimensional oil and gas conduit system. However, the conduits are the only possible passages through which oil and gas can migrate. Actual oil and gas migration pathways are very complex and heterogeneous. They always occur along a limited number of dominant conduits. Only traps near the dominant conduits can be filled (Hao et al., 2000; Luo et al., 2007a, 2013). More importantly, the geological characteristics of a conduit systems and their spatial–temporal stacking patterns would always change during the evolution of the basin. Therefore, the conduit

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systems must be time-specific and dynamic. Quantitative assessment of the conductivity of those systems in the main accumulation periods is important. The effective conduit systems should be identified and their conductivities be quantitatively characterized using unified hydrodynamic parameters. (1) Conductivity of Sandstone Carrier Bed The genetic types and spatial distribution of sand bodies can be interpreted through analysis of sequence stratigraphy and depositional systems of sandstones by using advanced seismic reservoir prediction technology. The distribution pattern and geometric connectivity of sandstone bodies within the carrier beds need to be described in detail. Next, in the context of geometric connectivity, the diagenetic characteristics of sandstone, diagenetic sequence, and temporal and spatial relationship with respect to hydrocarbon charges can be identified. The diagenetic products in the main accumulation periods are used as the criterion to identify the fluid connectivity of carrier beds and to interpret the physical characteristics during each main migration period (Chen et al., 2006). Finally, the heterogeneity of the carrier beds and its relationship with oil and gas occurrence can be analyzed; and the conductivity of sandstone carrier beds will be quantitatively characterized using appropriate parameters. (2) Conductivity of Fault Conduits To assess the conductivity of a fault, the researchers should make a full use of geological and seismic data to analyze the attitude of hydrocarbon-controlling faults, their criss-cross cutting relationship, and the relationship with the strata on both sides of the fault. The timing, episode, and intensity of fault activities need to be interpreted to determine the relationship between fault activities and hydrocarbon migration and accumulation. The intersection between faults and carrier beds is important. From the point of view of fluid dynamics, the main geological factors affecting the opening and closure of faults should be analyzed, so that appropriate characterization parameters can be selected. Subsequently, an appropriate quantitative evaluation method can be selected or developed. Eventually, the opening and closure of specific segments of a fault can be quantitatively characterized (Zhang et al., 2003b, 2007c, 2010). (3) Conductivity of Unconformity-Related Conduit System The extent of unconformities and stratal termination patterns (e.g., truncation and overlap) between the conduit carrier beds sub- and supra-jacent to the unconformity are important to assess the conductivity of the conduits as well as the juxtaposition relationship between the carrier beds above and below the unconformity. The evolution and distribution of the connected conduits can be determined, so that their roles in the process of oil and gas migration and accumulation can be assessed (Pan, 1983; Wu et al., 2007). An appropriate modeling method should be selected to describe the structural characteristics of these conduits; and appropriate parameters to characterize the conductivity of carrier beds.

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(4) Composite Conduit Systems and Unified Quantitative Characterization of Their Conductivity A composite conduit system is composed of several conduits of different types. The component systems and their geometric relationship need to be delineate to analyze the conductivity of the composite system in a MAU. The composite system may be composed of a variety of combinations of conduits, including sandstone carrier bed, conductors associated with unconformity, and fault. The most likely combination can be identified by geological analysis and geochemical tracing of reservoirs. The dynamic representation of oil and gas migration and accumulation in different types of conduits needs to be developed; and the parameters which can best describe the conductivity of different types of conduits and the relationship among them need to be determined. Subsequently, they will be used to quantitatively characterized the conductivity of the composite system. 4. Quantitative Analysis of Hydrocarbon Accumulation Process and Assessment of Oil and Gas Distribution In order to analyze quantitatively the process of hydrocarbon migration during the main period of reservoir charging, an appropriate hydrocarbon migration and accumulation model, which integrates hydrocarbon supply and migration force and resistance, will be used to determinate the directions of hydrocarbon migration and locations of accumulation, even to describe the migration pathways. The results of simulation will be verified by using the discovered hydrocarbon and geochemical information. Furthermore, the amount of oil and gas loss along the migration pathways and that after accumulation can be quantitatively estimated. As a result, the resource potential and distribution of each MAU will be reasonably evaluated on the basis of mass balance. Finally, the pattern of hydrocarbon accumulation and enrichment will be analyzed to determine the favorable exploration zones and targets. (1) Simulation of Dynamics of Hydrocarbon Migration and Accumulation The results of migration force field and composite conduit framework can be used to estimate the hydrocarbon generation and expulsion conditions of different sourcereservoir-cap assemblages at different times. Under the unified hydrodynamic background for migration and accumulation during the main migration-accumulation stages, the hydrocarbon sources and carrier systems can be overlaid by using appropriate hydrocarbon migration and accumulation models (Luo, 2011; Luo et al., 2007a). That will permit quantitative analysis of reservoir charging process and distribution of migration pathways by superimposing area of effective sources, field of fluid potential (driving forces), and migration conduits (resistances). The results of simulation will be verified by the distribution of discovered oil and gas and geochemical data; iteratively, the geological model will be revised and improved. As a result, the reliability of predicted oil and gas accumulation and distribution will be improved.

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(2) Quantitative Evaluation of the Potential and Distribution of Oil and Gas Resources The oil and gas migration process in each MAU will be simulated and analyzed through various mathematical statistics and numerical simulation methods. The loss of oil and gas during secondary migration (Lei et al., 2016; Luo et al., 2007c, 2008) can be estimated., The possible accumulation modes along migration pathways will be identified on the basis of correlation between hydrocarbon expulsion history of source rocks and caprock formation. Applying the principle of mass balance (Pang et al., 2005), the amount of commercial oil and gas accumulation in each MAU will be evaluated. The amount of loss of noncommercial oil and gas accumulations along the oil and gas migration pathways can also be estimated by using the pool size sequence method. Finally, the efficiency of oil and gas migration and accumulation and resource distribution will be quantitatively evaluated. (3) Comprehensive Analysis and Prospect Assessment of Hydrocarbon Accumulation The understanding of oil and gas migration and accumulation in all MAUs will be used for a dynamic analysis of migration and accumulation conditions in the study area. The overall process of oil and gas accumulation can be synthesized. Finally, guided by the principles of petroleum system theory, the prospective areas of oil and gas accumulation can be comprehensively analyzed and assessed.

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Chapter 2

Mechanisms and Processes of Secondary Migration of Oil and Gas

Many previous studies on the secondary migration of oil and gas, including the phase state, conduits and dynamic characteristics, have been carried out; and a systematic understanding has been achieved in theory (Allen & Allen, 1990; England et al., 1987; Hubbert, 1953; Schowalter, 1979). However, these understandings and progresses were limited to qualitative analysis. For heterogeneous conduit systems and complex migration processes in actual basins, only quantitative dynamic methods can provide in-depth solutions and answers (Luo, 2011). Both physical and numerical simulations are important means to study the complex migration processes and understand the essential mechanisms of secondary migration of oil and gas (Carruthers, 2003; Luo, 1998; Luo et al., 2004; Schowalter, 1979; Zeng et al., 2000). In this chapter, we first describe a series of physical simulation experiments of secondary migration that demonstrate characteristics of migration pathway geometry and the controlling factors and dynamic mechanisms of petroleum migration. Second, on the basis of these understandings, a mathematical model of hydrocarbon migration and accumulation is established by applying the principles of the invasion percolation theory and treating the buoyancy and capillary force as the principal driving and resisting forces, respectively.

2.1 Physical Experiments of Secondary Petroleum Migration Physical experiments allow us to directly observe and simulate some geological phenomena and physical process and measure the values of parameters necessary to establish geological models. Physical simulation has been widely used in the study of the mechanisms of geological process (Li, 1979). Physical models should be established by reducing the geological models proportionally, so that the physical process in the prototype may be reproduced in the experiments according to the relationship of dynamic similarity (Rapoport, 1955). In this section, the physical experiments © Science Press and Springer Nature Singapore Pte Ltd. 2023 X. Luo et al., Dynamics of Hydrocarbon Migration, https://doi.org/10.1007/978-981-19-5534-1_2

63

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of petroleum migration are described systematically to illustrate the formation of migration pathways, continuous migration of oil along the pathways, and the migration of newly injected oil along the formed pathways after the completion of the first episode of migration. The experimental output is used to analyze the growth of pathways and their characteristics and discuss the controlling factors of secondary petroleum migration.

2.1.1 Experiment Setup and Observation System In order to quantitatively describe the migration process and understand the migration mechanism, the possible factors that may affect the migration should be minimized when designing the experiments, so as to ensure that the details and process of the migration pathways can be observed, and various parameters related to the migration pathways can be accurately measured. 1. Experimental materials The purpose of the physical experiment is to understand the dynamic mechanism and process of displacement of pore water by oil or gas in porous media. The properties of experimental materials must be controllable. Therefore, glass beads and river sands are selected as the filling materials of the porous medium models; and the same type of glass plates are used to make the case panels. In order to ensure that the experimental materials are completely water wetting, the glass beads with varying diameters and the glass plates are heated at 500 °C for 30 min, and after cooling, washed with acid and alkaline solutions, so that the surface of the materials becomes strongly water wetting (Huang & Yu, 2001). Using river sands as filling materials makes the experiments closer to the actual geological conditions. The river sands in our experiments were taken from the Shahe River in the northern suburb of Beijing. The surface characters of the sands are similar to the sand grains in many sandstones. The surfaces of the sands were generally not treated. Some experiments need to take the change of wettability of porous media particles into account. Dimethyldichlorosilane (DMDCS) solution is selected as the surface treatment agent (Huang & Yu, 2001). Glass beads, plates, capillary tubes and other materials are put into dimethyldichlorosilane solution with concentration of 0.001%, 0.005%, 0.006%, 0.007%, 0.01%, 0.05% and 2.5%, soaked for 24 h and dried. The contact angle between water and experimental oil was measured by capillary method (Huang & Yu, 2001; Table 2.1). The water used in experiments is distilled water. The viscosity of the water is 1.0019 cp (20 °C); and its density is 1.007 g/cm3 (20 °C). Three kinds of oil are selected as the charging liquids in the experiments: kerosene, dodecane and octane (Table 2.2). To facilitate observations, oil red or oil blue are used to dye the oil. It is tested that the physical properties of oils do not change after dyeing.

2.1 Physical Experiments of Secondary Petroleum Migration

65

Table 2.1 Contact angles of water–kerosene on the glass surface that was treated with dimethyldichlorosilane solution of variable concentrations Concentration of DMDCS (%)

0.001

0.005

0.006

0.007

0.05

0.1

2.5

Contact angle (º)

57

66

83

91

117

142

167

Table 2.2 Physical properties of liquids used in the experiments

Viscocity (cp, 16 °C)

Density (g/cm3 , 19 °C)

Surface tension (dyne/cm)

Kerosene

1.698

0.792

28.9

Dodecane

1.508

0.749

25.35

Octane

0.545

0.703

21.54

In order to eliminate the relaxation signal of water, it is necessary to add a certain amount of Mn2+ ions in pore water when using NMR to observe the migration pathways. Manganese chloride (MnCl2 ) is added into distilled water to prepare the required concentration of Mn2+ solution. In the experiments, the density difference between two fluid phases is controlled by changing the density of one or both fluids. The required water density may be obtained by changing the concentration of inorganic solutes. Potassium chloride is a good additive because it has a wide range of solubility and can be used to change the density of the water solution. Moreover, potassium chloride is very stable and will not change the properties of kerosene and water. In practice, a variable amount of potassium chloride is dissolved in distilled water to prepare solutions with a density varying from 1.001 to 1.200. The interfacial tension between oil and water is an important dynamic parameter in the secondary migration. Sodium dodecylbenzene sulfonate is a kind of surfactant commonly used in tertiary oil recovery. Adding a very small amount of sodium dodecylbenzene sulfonate in water may greatly change the interfacial tension between oil and water, but has negligible effect on water density. Different concentrations of sodium dodecylbenzene sulfonate are prepared by using aqueous solutions with different salinities; and the interfacial tensions of oil and water in these solutions are measured. 2. Physical models In order to simulate the oil and gas migration process with variable spatial scales and dimensions and facilitate the observation during the experiments, the physical models can be designed as tubular, plate or box. In the experiments, glass beads, river sands and others moderately rounded equant particles were filled into tubular and flat glass containers to simulate porous media. Before filling, glass beads and river sand were screened using copper meshes with different size, to obtain appropriate particle size distribution.

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(1) Tubular model The tubular model is a glass tube filled with glass beads or river sands. The glass tube has a diameter of 20–100 mm and a length of 100–500 mm. Both ends of the tube are in a conical bottle mouth shape and tightly plugged with rubber plugs. Two filling methods, dry and wet, are used. The filling should ensure that: (1) the particles are closely packed and (2) the pore space is completely saturated with water. To construct a dry filling tubular model, place the glass tube vertically, plug the bottom end of the tube with a rubber plug, slowly pour the glass bead from the upper opening, shake the glass tube gently at the same time until filled, plug the rubber plug, weigh the entire tube on a balance as a whole, vacuum the tube, pour distilled water into the tube to saturate the beads and, finally, weigh the tube again. To construct a wet filled tubular model, put glass beads or river sands in a water tank filled with distilled water, stir it fully to remove the micro bubbles adsorbed on the surface of particles, place the glass tube vertically, plug the bottom of the tube with a rubber plug and keep it vertical in the water tank, slowly pour the glass beads with water through the upper port of the glass tube, gently shake the tube until filled, plug the upper port with a rubber plug, and weigh the entire model on a balance. The porosity and permeability of the porous media in the tubular model can be measured directly. The porosity value (φ) of the model is calculated by the following formula: φ=(

W − ρg )/(ρw − ρg ) V

(2.1)

where W is the total weight of water and particles in the model; V the volume of the tube; ρ g the density of glass beads or river sands; and ρ w the density of water. The absolute permeability of the model is measured under the condition of horizontal stable seepage (Zhang & Wang, 1989). (2) Plate model To clearly observe the pathway characteristics of oil at every moment during the migration process, a Hele Shaw model filled with glass beads was used to carry out two-dimensional secondary migration experiments. The model is composed of two parallel glass plates with a width of 300 mm and a height of 400 mm. A 10-mm-wide and 3-mm-thick rubber cushion is clamped along the edge of the two glass plates. A C-clamp is used to compress the glass plate and the rubber cushion to achieve the sealing effect. The glass beads are wet-filled into the model. The model is filled with water first; then the mixture of glass beads and water was quickly poured into the model through a sieve clamped at the top of the model, to let the glass beads settle continuously and evenly, while ensuring that the model is completely saturated with water. During the settlement of glass beads, use a rubber hammer with the same strength to knock the side of the model evenly at all positions to make the glass beads closely packed. This will effectively avoid the layering phenomenon in the filling process.

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The porosity of the porous media in the plate model is calculated according to Formula (2.1); and the permeability is calculated using the porosity–permeability relationship established by the measured results of the tubular model. (3) Three-dimensional model The three-dimensional model is a cubic box that is filled with glass beads or river sands. Its external dimensions are 1000, 400, and 140 mm, respectively. All walls of the box are 10 mm thick plexiglass plates bonded by adhesive; and the top lid can be lifted for filling. The lid is secured to the main body by a rubber gasket and nylon bolt. An injection port is set at the lower part of the bottom plate of the model; and an overflow outlet is set at the upper part of the lid (Fig. 2.1). In order to eliminate the influence of wettability on the oil migration pathways, a conventional glass plate is padded under the top plexiglass plate. The box is first filled with water. Then, a mixture of glass beads and water is ported into the box through a sieve on the port of the lid. Shake the model during the filling process to pack the glass beads tightly. After the filling is complete, check whether there is a water leakage. The method for porosity and permeability measurement of the box model is the same as that for the plate model. 3. Experimental workflow and observation methods The major difference between our and previous experiments is that we can observe the entire migration process in the experiments. The observation, recording and result analysis of the migration pathways are carried out for the entire duration of the experiments. The transparent materials used in the models permit us to observe directly the formation process and major characteristics of migration pathways and speculate on the controlling parameters. In addition, a system for optical photography is designed to record the processes (Fig. 2.2).

Fig. 2.1 Schematic diagram showing the thee-dimensional model for oil and gas migration study. α is the inclination angle of the model

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red oil

oil pump water

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Fig. 2.2 The equipment and set up of oil and gas migration experiments

The physical parts mainly include an advection pump, tubular and/or plate models and an intermediate vessel (Fig. 2.2). The photographing device consists of two light sources, a Nikon D300 CCD digital camera and a computer. Image acquisition is carried out in the darkroom. Generally, the front image of the experiment is observed and photographed. By adjusting the position of the two light sources, the reflection off the front glass plate and the image interference of the surrounding objects are minimized. The flat flow pump or nitrogen bottle is used to adjust the filling pressure of the injected oil or gas. The intermediate containers are used to control alternate oil and water injections into the model in a same experiment. In addition, the pumping action of the flat flow pump will inevitably cause fluctuations in the injection pressure of fluids. The intermediate container will buffer the fluid pressure to minimize the injection pressure fluctuations, because the pressurized oil and/or gas will pass first through container (Fig. 2.2). To conduct the migration simulation experiment, first, place the tubular or the plate model filled with water on the support, and connect all parts following to the diagram in Fig. 2.2. The flat flow pump will drive the pump fluid with a constant flow rate through the intermediate container. The dyed oil is injected from the bottom of the model. The fluid in the model is expelled from the top outlet; and the outflow fluid is collected into a collection container. The arrow in Fig. 2.2 represents the flow direction of the fluid. In the oil migration simulation experiments, the formation of migration pathways and subsequent oil migration along the pathways are relatively slow. So continuous photographing with a high-resolution camera seems to be the simplest and most reliable way to document the migration pathways. When kerosene is injected into the model from the bottom inlet, the digital camera is used to take photos at regular intervals to record the images during the entire migration experiment. The time interval between photos is adjusted according to the speed of oil and gas migration. Only the migration pathways next to the glass panels can be observed by naked eyes and photographing. Other penetrative means have to be used to observe the

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distribution of oil and water within the migration pathways and the three-dimensional clusters of the migration pathways. Previous attempts have been made to measure and observe the migration process of oil and gas in sandstone samples saturated with water, by using γ-ray absorption (Selle et al., 1993) or ultrasonic method (Thomas & Clouse, 1995). However, the resolution of these observation methods is low; and only semi-quantitative average values of hydrocarbon saturation in a narrow range can be obtained, which are not accurate enough to quantitatively study the migration mechanisms. Nuclear magnetic resonance imaging (NMR) is a rapid non-destructive and noninvasive technology that can be used to study multiphase seepage in porous media (Wang et al., 1997). We utilized the NMR imaging into our experiments due to its advantages in fast imaging, high resolution, clear differentiation between oil and water, quantitatively measurement of oil and water saturation. Quantitative observation of oil migration process can be done in opaque models of porous medium, so that the characteristics of migration pathways and the distribution and evolution of internal fluids can be conducted (Yan et al., 2012a). The spin echo nuclear magnetic resonance imaging technology (Wang et al., 1997) is used in our experiments. The signal intensity or brightness (S) of each pixel can be expressed as follows: S = S0 exp(−Te /T2 )

(2.2)

where S 0 is a constant and proportional to the number of hydrogen atoms contained in the pixel; T e the echo interval and can be directly controlled by the instrument; and T 2 the lateral relaxation time, corresponding to the rate of signal attenuation. During the NMR scanning of the migration pathways, hydrogen nuclei (H+ ) in oil and water spin and resonate under the force of magnetic field. Influenced by the molecular structure, the relaxation rates of the NMR signals of the two fluids are different. The relaxation rate of H+ in water is faster than that in kerosene. When paramagnetic ions, such as water-soluble Mn2+ , are added into pore water, the relaxation rate of H+ in water is accelerated, while the rate of H+ in kerosene remains unchanged (Chang et al., 1993; Wang et al., 1996). As a result, the signals of oil and water can be easily distinguished. When the concentration of Mn2+ reaches 700 mg/L, the contribution of H+ in water to NMR signal can be ignored (Yan et al., 2012a). By using the difference of attenuation rate of oil and water signals, when the pathways are measured twice with different echo intervals, oil and water in the model can be distinguished; and the oil saturation of migration pathways in NMR image can be quantitatively estimated (Miao et al., 2004). During the experiment, two calibration tubes were placed next to the model, which were filled with Mn2+ aqueous solution and oil, respectively. The concentration of Mn2+ aqueous solution is greater than 700 mg/L, to make the received signal of the water calibration tube as zero. In this way, the brightness of the oil calibration tube can be used as the reference to the brightness signal. If the average brightness of a pixel unit in the scanned image is Ao , the average pixel brightness of the oil calibration tube is ao , and the average oil saturation S o of the pore space can be calculated by

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Fig. 2.3 NMR image of migration pathways and color display of oil saturation after processing

the following equation: So = φ · Ao /ao

(2.3)

where φ is the porosity of the porous medium in the model. The MRI equipment used in the experiments is Wandong 1.5T superconducting MRI. When scanning the migration pathways, the slice thickness is selected as 3.0 mm; the slice spacing 0.3 mm; and the slice resolution 1.5 mm × 1.5 mm. The oil saturation of migration pathway images was quantitatively analyzed by a MATLAB image processing program (Yan et al., 2012b). In the entire NMR image, the signal intensity of pixel points on the migration pathways is the superposition of the signal intensity of oil and noise signal. Assuming that the signal intensity of noise is N, the actual oil saturation So' should be: So' =

Ao − N ao − N

(2.4)

The corrected image is processed by an optical median filter, a background correction, and a digital median filter in order to standardize and transfer the image intensity into optical density. Furthermore, the optical density is corrected and converted into an image of the oil saturation distribution displayed by chromaticity (Fig. 2.3).

2.1.2 Formation of Migration Pathways In the expulsion experiments of immiscible two-phase fluids, previous researchers have focused on the formation process of migration pathways of the expelled phase (Catalan et al., 1992; Frette et al., 1992; Hirsch & Thompson, 1995; Meakin et al., 2000). However, the actual oil and gas migration in the basin is commonly a long geological process. The oil supply from a source is not stable; and the geological

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conditions of migration change with the evolution of the basin. Therefore, in the study of oil and gas migration, the migration behaviours in the conduit after the formation of migration pathways are also very important. In order to further analyze the migration mechanism and process, a series of migration simulation experiments of different types of experimental models have been carried out by using the aforementioned experimental devices. The entire process of oil migration in the glass bead models has been observed, including the formation of migration pathways, the migration of oil along, within or around the existing pathways, and the variation of migration pathways during and after continuous oil movement. The morphological characteristics of the pathways and the factors affecting the processes were analyzed. 1. Processes in the formation of migration pathways Many works have studied the formation process of migration pathways (Catalan et al., 1992; Dembicki & Anderson, 1989; Lenormand et al., 1988; Selle et al., 1993; Thomas & Clouse, 1995; Zeng & Jin, 2000). We have conducted systematic migration simulation experiments, based on the previous experimental knowledge, in order to understand the morphological characteristics, influencing factors and dynamic relations of pathway formation. The results are presented below. (1) One-dimensional tubular model In the tubular model filled with glass beads, a certain amount of oil is injected into the upper part of the model. Then the model is sealed and inverted, so that the oil starts to move upward under buoyancy. Migration pathways with different geometries are formed. It is also possible to inject oil at the bottom of the tubular model while draining the displaced water through an outlet at the top. Figure 2.4 shows the images of migration pathways at different time points during a representative experiment. The timing starts when the front of the pathways reaches 16 cm. It takes 106 min for the pathways to the 45 cm mark. Thus, the average migration rate is about 0.28 cm/min. In the experiment, oil migration began as soon as the test cylinder is inverted with the oil at the bottom. The initial height of the oil column in the cylinder ss high enough so that the corresponding buoyancy force could easily overcome the capillary resistant force in some pore throats. New branches or “fingers” develop progressively to form somewhat randomly and simultaneously at the water–oil interface. The migration pathways in Fig. 2.4 are narrow (2–3 mm in width) and nearly vertical. The geometry and structure of the pathway remain stable during the experiment. In the experiment, when the oil migration pathways form, the rising speed of the fingers at the front of the migration pathways usually fluctuates. The end of one finger, usually the highest one, rises rapidly after a period of no movement. This is the phenomenon of “Haines jump” (Morrow, 1976). When the highest finger of the migration pathways stops to rise, the lower part of the pathways would sometimes become slightly thicker, and/or some new fingers appear to grown from the sides of the pathways. The oil and gas migration is the result of driving force overcoming the resistance at the pore throat. In porous media, each pore is connected with several other pores

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Fig. 2.4 Images showing the formation of non-uniform migration pathways of oil in a tubular model

via the throats. When oil and gas migrate from one pore to another, they usually enter the pore through the throat where the migration force, which is the difference between buoyancy and capillary forces of oil and gas column, is the greatest. After the oil enters the pore, the condition for further migration has changed. For a cluster of continuous oil in multiple pores, the next migration direction will be selected from one of the throats connected with these pores. A new finger will be formed in the pore that connects with the throat where the migration force is the largest. Such process occurs progressively to extend the pathway. In the cases that the buoyancy always at work, oil tends to migrate upward. But branches from the sides of an existing pathway or multiple pathways may form. (2) Plate model Similar experiments can be done by using a two-dimensional plate model and a three-dimensional box model. By injecting dyed kerosene into the bottom of the twodimensional plate model, the upward migration pathways of oil driven by buoyancy and injection pressure can be observed. The space between the front and back glass plates in the plate model is only 3 mm; and the migration pathways basically occupy the entire space between the two plates. Hence, all the pathways can be observed. Previous studies using displacement models mostly used hollow or filled Hele Shaw cells as experimental devices; and two manners of injection are commonly applied. First, the displacement fluid is injected from the center of Hele Shaw cell; and the expelled fluid flows outward. Second, the fluid is injected from one end of the plate model and flows to the other end (Frette et al., 1992; Lenormand & Zarcone, 1985). In our experiments, oil is injected from the bottom of the device; and water in the

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model is expelled from the upper outlet. The morphological characteristics of the pathways are determined by the interaction of buoyancy, injection force and capillary force corresponding to the throats in the model. Figure 2.5 shows the development of oil migration pathways at different time points in a typical migration experiment. The space between the front and back glass plates of the device is 3 mm. The size of glass beads ranges from 0.6 to 0.8 mm. The injection flow is 0.1 ml/min. And the density deference between oil and water is 0.21454 g/ml. In Fig. 2.5, at the beginning, oil is injected into the model and accumulates near the injection point and extends in all directions. However, the upward migration is dominant to form obvious fingers (Fig. 2.5a). Along with time, the pathways extend principally upward along one finger, and basically remain unchanged in other directions (Fig. 2.5b). Furthermore, the pathways advance upward along the same finger, forming narrow and winding migration finger (Fig. 2.5c–f).

Fig. 2.5 Images of oil migration pathways in a plate model, showing the formation of pathways. The time increment between each image is 15 min; and the total time span is 120 min

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Fig. 2.6 A photo of the box model showing oil migration pathways

(3) The three-dimensional box model To observe the three-dimensional migration pathway development within the box model, MRI images are needed. Figure 2.6 illustrates the formation of oil migration pathways in a threedimensional box model. In the model, the glass beads of a size ranging from 0.4 to 0.6 mm are wet-filled; and the pore water is a Mn2+ solution with a salinity of 7000 ppm. The box model was inclined with 36° angle (Fig. 2.6). Oil was injected into the model at a speed of 0.1 ml/min from the injection port at the bottom of the lower end of the model, and the displaced water was expelled through the outlet at the top of the model. When the front of the oil migration pathways reaches the upper glass plate, it is constrained, and oil has to migrate laterally in the updip direction. The migration pathways grow irregularly and in a zigzag pattern and vary from thick to thin (Fig. 2.6). The Haines jump phenomenon of the pathway front was observed. The movement of the pathway front stops from time to time. During the stagnation period, the pathways became slightly thicker or generated small fingers on the sides of the pathways. After some time, oil broke through the front and continued to move upward. Commonly, multiple fingers occurred in the migration front, each of which expanded continuously, and split off and merged with each other. Sometimes when two fingers merged, some water were enclosed in the pathway, forming isolated “water mass.” The mass is difficult to be occupied and replaced by later migrated oil. The MRI slice images were used to reconstruct the entire three-dimensional migration pathways. Subsequently, the morphological characteristics and formation process of the pathways in any directions can be observed. Figure 2.7a is a top view of the migration pathways, showing the geometric characteristics of the upward (leftward) pathways. Many small fingers developed around the main migration pathways; and some isolated water masses occurred in the clustered pathways. Figure 2.7b is the side view of the migration pathways, showing the crosssectional shape and thickness changes of pathways. Overall, the oil first migrated vertically as driven by buoyancy. When the pathway front reaches the capping plate,

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Laterale migration pathways

Vertical migration pathways

Fig. 2.7 MRI images of oil migration pathways in a box model in top view (a) and side view (b)

the oil was forced to migrate upslope. The lateral migration pathways aggregate into a planar shape, which is about 2 cm thick and has a basically flat lower boundary that is parallel to the capping plate. (4) Morphological changes of migration pathways during migration The experiment shows that, after the migration pathways reach the outlet, the pathways within the model become smaller in width or volume, and easily to be snapped off, even though the experimental conditions remain unchanged. And oil started to migrate in segments (Fig. 2.8). During the pathway formation, oil migrates in the pathways continuously. However, after the pathways are fully established throughout the model, the pathways become frequently snapped off, resulting in a heterogeneous distribution of oil in the pathways. The snap off may occur at any point. The continuous oil segment above the snap-off point may still move upward; and the segment below the snap-off point becomes stationary. While the oil in the upper segment passes through the upper pathway as a whole, some small, isolated oil clusters remain along the original pathways. As oil is being continuously injected into the model, the newly-injected oil in the lower part will join the stationary oil segment to increase the height of the segment. As a result, the oil in the stationary segment will break through the snap-off point to move upward along the original pathway as a whole. The moving segment will also connect the isolated oil clusters to form a long and continuous oil segment again. However, when the length of the re-generated oil segment reaches a certain value, the segment will be snapped off again. In some experiments, the location of the snap-off points appears fixed (Fig. 2.8), whereas in other experiments, the location of the snap-off point changes constantly, forming a complex pattern of movement of multiple segments. Such process is repeated in the existing pathways in the later

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c

Snap-off 卡断点 point

a

b

d Fig. 2.8 Images of migration pathways showing snap-offs and segmented migration. The 5 diagrams present the pathway images of the same experiment at different times. Segments are labeled as “a”, “b”, “c”, and “d.” A snap-off point is also labeled

stage of experiment under the condition of continuous oil supply. The oil in the entire network of pathways shows a recursive pattern of upward migration of oil segments (Fig. 2.8). The snap-offs and segmented migration in the pre-existing migration pathways change the oil saturation constantly in any part of the migration path. The changes were imaged repetitively on the same cross-section in a three-dimensional model by using NMR equipment. Figure 2.9 presents the NMR images of a section at a distance of 30 cm from the injection point and perpendicular to the migration direction at four time points. These images show that once the migration pathways are formed during the later oil migration the outline of the pathways remain basically unchanged; but the oil saturation in the pathways changes in some areas. When the saturation is low, the oil in the pathways will snap off to form isolated clusters (Fig. 2.9). The process of snap-off and segmented migration inhibits the formation of a continuous oil column in the migration pathways. As a result, the segmented oil column is not long enough to travel through the pathways. This affects the rate of oil migration in porous media (Wagner et al., 1997). (5) Relict pathway after migration After oil migrated through the model and the injection of oil stops, the oil remaining in the pathway still migrates under buoyancy over a period. The pathways will narrow as the clusters decrease their width, break off, and become discontinuous. Thus, the oil saturation within the pathways will be greatly reduced. When all the oil clusters in the model move to the top of the model, relict migration pathways remain.

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a

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connected

Snap-off

c

d

Fig. 2.9 Cross-sectional MRI images of oil migration pathway perpendicular to the migration direction in a box model, showing the snap off and clustering in the pathways and resultant change of oil saturation at four time points A–D

The observed variations in oil saturation during and after oil migration in an experiment is illustrated in Fig. 2.10. The three panels show variations in the cluster structure of the pathways and oil saturation during and after the formation of complete pathways, and after oil migration has ceased. The oil saturation in the pathways appear to be high when migrating oil was actively moving during pathway formation. As mentioned above, the development of the pathways generates fingers and causes the growth of existing clusters. A mature pathway was formed and oil migration continued during which the entire pathway appeared to be saturated with oil. Once migration had finished, the cluster shrank and appeared to break off, although its remnants outline the pre-existing pathway. A comparison between the fullydeveloped pathway and the final stage shows that the oil saturation has largely decreased.

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Fig. 2.10 The variations of migration pathways observed in 2-D models during (a), right after (b), and after (c) the formation of pathways and oil migration

4 3 2 1 0

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a

b

ρ1

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Fig. 2.11 Displacement patterns of two immiscible fluids. a Piston pattern, where a low-density fluid displaces a high-density fluid, or a low-viscosity fluid displaces a high-viscosity fluid. b Fingering pattern, where a high-density fluid displaces a low-density fluid, or a high-viscosity fluid displaces a low-viscosity fluid

2. Patterns of Migration Pathways The characteristics of pathways resulted from the expulsion of immiscible fluids in porous media have been studied by previous researchers. Saffman and Taylor (1958) studied the two-phase displacement using a Hele Shaw Call model. They found that both the viscosity and gravity of the fluids can affect the stability of the two-phase fluid interface during displacement. The displacement where the interface is stable is called piston displacement, whereas the displacement where the interface is unstable is call viscous fingering displacement, in which the displacing fluid penetrates the displaced fluid with finger-like clusters. Viscous fingering occurs when a relatively low-viscosity fluid displaces a high-viscosity fluid or a relatively high-density fluid displaces a low-density fluid (Fig. 2.11). However, Saffman Taylor’s theoretical understanding does not fully describe the displacement of two-phase fluids in porous media, because the variation of capillary force and the difference in the curvature of the two-phase fluid interface in porous media are not considered (Chen & Wilkinson, 1985; Homsy, 1987). In porous media, the capillary force is randomly distributed due to heterogeneity. This makes the interface forces of two-phase fluids significantly different. Large throats are often conducive to the invasion of displacing fluid (Chandler, 1982; Lenormand & Zarcone, 1985). In the absence of gravity, when the displacement velocity is very slow (quasistatic), the displacement characteristics in porous media depend on the distribution of capillary force on the two-phase interface (Chandler, 1982; Lenormand & Zarcone, 1985). And the unstable pathways formed is called capillary fingering. For a fast flow, the role of viscous force is more important; and viscous fingering occurs at the displacement interface (Måløy et al., 1985). Most of the secondary migration experiments employ oil injection from the bottom of the experimental device into a water-saturated medium. Since the density of oil is less than that of water, gravity will make the fluid flow in the device unstable.

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However, the viscosity of oil is greater than that of water. Thus, the difference of viscosity makes the migration tend to be stable. Therefore, with respect to the stability of the fluid interface during deplacement, the pathways of immiscible two-phase fluids in porous media can be divided into three patterns: piston, viscous fingering, and capillary fingering patterns (Lenormand & Zarcone, 1985; Tokunaga et al., 2000). The dynamic mechanisms of viscous fingering and capillary fingering are different, but the morphological characteristics of pathways in the two patterns are difficult to be distinguished. In the actual migration process of oil and gas displacing pore water, capillary force and viscous force exist and act simultaneously. It is difficult to identify the corresponding migration dynamic mechanisms only from the morphological characteristics of the migration pathways (Tokunaga et al., 2000). In order to understand the characteristics of petroleum migration pathways and the dynamic conditions of pathway formation, we unified the experimental standards and carried out systematic simulation experiments (Hou et al., 2004; Luo et al., 2004; Zhang et al., 2003). The experimental results show that the morphological characteristics of the migration pathways vary greatly under different conditions. The shape of migration pathways can be influenced by variations in initial oil column height, oil injection rate, and glass bead size and wettability. Under some conditions, very unstable pathways can form (Fig. 2.12a). In these cases, the front edge of the original oil column is basically immobile, but multiple fingerings grow. One or two fingers grow continuously, forming the main migration pathways, whereas the other fingers stop growing and remain immobile. With progressive migration of oil, the already formed pathways remain basically unchanged. Under some migration conditions, multiple pathways with similar characteristics can form; but multiple fingerings are competing for growth (Fig. 2.12b). Under some other conditions, unstable migration pathways can also form (Fig. 2.12c). In these cases, several fingers form at the upper edge of the original oil column and grow at the same time; and the oil migrates upward along these fingers and, in some cases, at the same pace. When these fingerings reach a certain length, the entire oil column below the base of these fingers begins to move upward and continuously amalgamates the fingers from below. In the meanwhile, the fingers keep growing at their tips so that the fingers have similar lengths. Finally, in the case of rapid injection, a piston-like migration pathway will form (Fig. 2.12d). The front of the pathways is nearly straight; and the oil column advances as a whole. These experiments also show that, no matter what kind of shape of migration pathways are generated under different conditions, once the migration pathways formed, their morphological characteristics remain unchanged as long as the experimental conditions remain the same. The migration pathways of oil can be divided into three patterns, piston, finger and stringer (Fig. 2.13). In the piston pattern, the upper surface of the oil column remains planar or subhorizontal, as does the bottom water–oil interface. These two surfaces correspond, respectively, to the migration and imbibition fronts which appear to move upward at about the same speed. In the capillary finger pattern, several finger-like clusters

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Fig. 2.12 Images showing formation of migration pathways under various experimental conditions. a The height of initial oil column is 14 cm. b The oil injection rate is 0.4 cm/min. c The height of initial oil column is 20 cm. d The oil injection rate is 1.0 cm/min

of migrating oil grew at the front. Once the fingers are developed, the entire column moves upward and incorporates the existing fingers from below. In the meantime, the fingers keep growing at their tips. In the buoyancy string pattern, only one branch of the potential finger cluster grows upward, while the oil–water interface remains fixed at its initial position (Fig. 2.13). Although this single finger may locally split into two or more sub-units, eventually only one finger develops and the others stop. Regardless of the migration pattern, the water–oil interface at the bottom of the upward-moving oil column (the imbibition front) remained smooth throughout the experiments. Once a migration pathway was formed, later continuous movement of the oil along the pathway did not change the geometry of the pathway (Fig. 2.13).

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a.

Fig. 2.13 Three patterns of oil migration pathways observed in the experiments: piston (a), finger (b), and stringer (c)

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The morphological characteristics of the pathways of the piston and finger patterns are basically the same. The finger pattern includes the pathways from viscosity and capillary fingering, but the stringer pattern was discussed before by previous researchers and is generally classified as a finger pattern (Tokunaga et al., 2000). The stringer pattern looks like a slightly winding vine, where the front of the initial oil column remains stationary during migration. This pattern is quite different from the other patterns (Figs. 2.12 and 2.13). Similarly, the different patterns of migration pathways in tubular models can be evaluated in the plate models. Figure 2.14 shows the formation of migration pathways under three different conditions. The size range of the glass beads is 0.6–08 mm; and the injection rates at the bottom of the model are 0.1, 0.7, and 3.0 ml/min, respectively. The results show that in the two-dimensional space, the three patterns also form (Fig. 2.14). In the plate model in Fig. 2.14a, the oil is injected from the bottom of the model; and the injection rate is 0.1 ml/min. At the beginning, a small oil cluster formed above the injection point, then several fingers grew simultaneously. Shortly after, only one or two of these fingers continued to grow, while the others stopped; the pathway became narrow and tortuous, with some small branches developed at both sides. This kind of migration pathway can be classified as a stringer pattern. When the injection rate is high, the oil–water interface is stable (Fig. 2.14c). In the early stage of migration (Fig. 2.14c1), a nearly circular stable oil cluster formed around the injection point. With continuous injection, it expanded around the injection point in all directions, similar to the stable displacement formed by point injection in the Hele Shaw Cell experiments without gravity (Vicset, 1992). With the growth of the pathways, the role of gravity gradually becomes important; and the upward expansion rate of the pathways increases significantly; and the pathways

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Fig. 2.14 Images showing the stringer (a), finger (b), and piston (c) patterns of migration pathways in plate models. The numbers in each pattern present the different experiment times

gradually form into the shape of a thick column shape (Fig. 2.14c) as the piston pattern for the point injection plate model. At a moderate injection rate, the pathways appear as a transition pattern between the two end-member patterns. At an injection rate of 0.7 ml/min (Fig. 2.14b), at the beginning of oil injection, a round oil mass is formed around the injection point; and

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the stable interface expands continuously in all directions. With continuous injection of oil, the oil mass expands faster in the upward direction when its diameter reaches a certain width and form a piston-like main pathway with some narrow and winding fingers upward. As the experiment proceeds, fingers keep growing upward while their bases are being continuously “swallowed” by the upward growing main pathway. Further along, the number and length of fingers at the front of the pathways increased, while the growing speed and width of the main pathway decreased (Fig. 2.14b). The pathways in Fig. 2.14b are classified as a finger pattern. When the size of glass beads is small or the injection rate is large, the injected oil expands steadily in all directions around the injection point at the beginning, indicating that the injection pressure is the main driving force at this time. With the increase of the height of the main pathway, the pathway expands more quickly upward; and the fingers at the leading edge of the main pathway become longer, indicating that the role of buoyancy is becoming more important (Fig. 2.14b). 3. Factors influencing morphological characteristics of migration pathways The influence of many factors, such as wettability, particle size, medium material, oil–water physical property, and injection pressure, on the formation and morphological characteristics of migration pathway is assessed through systematic physical experiments (Hou et al., 2005; Yan et al., 2009; Zhang et al., 2003). The results show that the formation of migration pathways is affected by various factors, and commonly a change of one factor can change migration from a stable state to a very unstable state. The effects of wettability of particle surface, particle size, and injection pressure on oil and gas migration are described below. (1) Wettability In our experiments, the wettability of particle surfaces is changed using dimethyldichlorosilane solution (DCDMS; Huang & Yu, 2001). The cylinder, glass beads, and capillary tubes, which are made of glass of a similar composition, were soaked with DCDMS of different concentrations of 0, 0.001%, 0.007%, 0.100%, and 2.500%. The oil–water contact angles were then measured by observation of the meniscus in the capillary tubes; and the corresponding contact angles are of 9°, 57°, 91°, 142°, and 167°, respectively (Fig. 2.15). In the experiment, oil was injected from the top of the cylinder to form an initial oil column with a height of 20 cm. Then, the cylinder was flipped over to start the experiment. Oil migrates upward under buoyancy until the front edge(s) of the pathways reach the top of the cylinder. Figure 2.15 shows the migration pathways formed under different wettability conditions. It is apparent that the wettability of the glass beads affects significantly oil migration and pathway morphology. From oil wetting to water wetting, the pathways change from extremely heterogeneous stringer pattern to the piston pattern (Fig. 2.15).

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Fig. 2.15 Images showing oil migration pathways in a cylinder model filled with glass beads soaked in DCDMS with a concentration of 0.000% (a), 0.001% (b), 0.007% (c), 0.100% (d), and 2.5% (e)

10

8

Height (cm)

84

6 4

2

0

a.

c.

b.

d.

e.

(2) Particle size The particle size determines the sizes of pores and throats, which affect the capillary force and permeability of the migration conduits. The throat radius is inversely proportional, but nonlinearly, to the capillary force (Dullien, 1992). The cylinder models are filled with glass beads of different size ranges of 0.42–0.80, 0.25–0.42, 0.18–0.25, and 0.15–0.18 mm. Same procedures as described in the “Wettability” section was used to prepare the model. The results show that the pathways in the models with small glass beads have a larger cluster width and occupy a larger volume, in comparison to those with large beads (Fig. 2.16). (3) Injection pressure Under the actual geological conditions, the initial oil column height at the beginning of oil migration may be very high (Vasseur et al., 2013), while the length of the device used in the experiment is limited. Therefore, oil is injected under the initial 10

8

Height (cm)

Fig. 2.16 Images showing pathway patterns in a cylinder model with different particle sizes ranging from 0.42 to 0.80 mm (a), 0.25–0.42 mm (b), 0.18–0.25 mm (c), and 0.15–0.18 mm (d)

6

4

2

0

a

b

c

d

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85

Fig. 2.17 Images showing migration pathways in cylinder models formed under different injecting pressures

0.2

0.18

0.07

0.052

0.031Mpa

oil column at the bottom of the device to make up for the shortage of the initial oil column height. A series of experiments with constant injection pressures were designed. Oil was injected into the model from the bottom port and the fluid in the model was expelled out from the discharge port. The pressure differences between the two ports were 0.2, 0.18, 0.07, 0.052, and 0.031 MPa, respectively. Figure 2.17 shows the different oil migration pathways with different injecting pressures. With the increase of injection pressure, the width of oil migration pathway becomes larger; and the final pathways have a piston pattern (Fig. 2.17). However, in these models, the injection pressure only changes less than one order of magnitude; but the migration pathway changes from a stringer to piston pattern, indicating that the morphology of migration pathways is very sensitive to the driving force.

2.1.3 Remobilization in Migration Pathways The generation, migration and accumulation of oil and gas do not happen constantly everywhere in a sedimentary basin. Oil and gas migration is a relatively long and complex geological process which is composed of numerous component physical processes (Luo et al., 2012; Zhang et al., 2010). Therefore, the formation of oil and gas migration pathways does not mean that the migration process has completed. It is likely just the first step in the geological process of migration. Subsequent oil and gas migrations will occur periodically along the existing pathways; and the dynamic conditions of each episode of migration may differ from each other. 1. The backbone pathways The snap-off and segmented movement of the migrating oil clusters often occur only in a part of the existing pathways, called the backbone pathways (Yan et al., 2012a, 2012b, 2012c). Such phenomenon was observed in previous numerical simulation experiments by Stanley and Coniglio (1984), Meakin et al. (2000). During the displacement between immiscible fluids, once the expelling phase first formed a pathway through the medium, the subsequent expelling phase will only flow along some of the clusters in the pathway, which is called percolation backbone; and the rest of the clusters are called dead ends. This phenomenon is little known, because

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

most of previous physical migration experiments observed only the formation stage of migration pathways, and researches on the continuous migration stages after the formation of pathways are rare (Luo et al., 2004; Yan et al., 2012a, 2012b, 2012c). Kerosenes dyed respectively with oil red and oil blue are used in the experiments in order to clearly distinguish the backbone segments in the existing migration pathways. The variation in migration pathway in a plate model during the late stage is observed by injecting colored oil (Fig. 2.18). Red kerosene is firstly injected into the model at a rate of 0.1 ml/min to form a narrow stringer pathway that connects the injecting point and the top of the model. Once the injection is stopped, the oil in the pathway continues to move upward in segments, leaving the residual clusters in the pathway (Fig. 2.18a). Next, blue kerosene is injected at the same rate; and it still moves along the existing pathway (Fig. 2.18b, c), gradually connecting the residual clusters in the pathway with a tendency to form a continuous oil segment that travels vertically through the model (Fig. 2.18d). The blue kerosene does not completely displace all the residual oil, instead, forms a narrower “pathway” within the original pathway. This narrower pathway is the so-called “migration backbone.” Under the same experimental conditions, oil migrating in the pre-existing pathways usually only occupies a part of the pathways. This indicates that the residual oil in the pathways plays an important role in the later oil migration, and the migration efficiency of oil in the pre-existing migration pathways is significantly improved (Yan et al., 2012a, 2012b, 2012c). 2. Migration velocity The morphological characteristics of the backbone pathways is significantly different from the original pathways. The efficiency of oil migration along an existing pathway is better than that in the original pathways. This is reflected by the fact that the migrating oil in the backbone pathways occupies less conduit space to complete the same amount of oil in less time. This may be due to the more effective migration conditions within the existing pathways. The difference in the migration velocity between the original and existing pathways proves this point. In the experiment illustrated in Fig. 2.19 the migration velocity is measured twice: the first is the growth velocity of the red migration pathway while it was forming; and the second is the moving velocity of the front edge of blue backbone pathway. Figure 2.19 shows the changes in the height of the front edge height of the initial pathways and that of the growing backbones in six experiments. The horizontal axis is the time; and the vertical axis the height of the front edges of the pathways relative to the injection point. The red curves indicate the changes of the height of the front edges of original pathways with time; and the blue curves that of the backbone pathways. Comparing any pair of corresponding curves, the upward moving velocity of the backbone pathway is always greater than that of the initial pathway. The increase in migration velocity indicates that the amount of migrating oil per unit time increases. The results demonstrate that the residual oil in the existing pathway facilitates subsequent oil migration, because the oil migrating in the existing pathway does not need

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87

Fig. 2.18 Images illustrating the formation of migration backbone pathway in a plate model. a The original pathway outlined by residual oil after migration; b the early stage of newly-injected oil migrating in the relict original pathway; c the development of migration backbone pathway in the original pathway; and d the backbone pathway extends through the original pathway

to occupy all the porous spaces in the pathways; only the favorable conduits are selected, where the flow resistance is the least. 3. Effect of varying driving forces on pathway characteristics When would oil and gas migrate in an existing pathway? what would happen if the driving forces become greater than those during the initial migration? Would the moving oil break through the original pathway? If yes, to what extent? Would the expanded pathway be the same as the original pathways that could have formed by larger migration forces? These are some of the questions that need to be understood in the study of oil and gas migration.

88

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Fig. 2.19 Comparison of the migration velocities during the initial migration and that during later migration along the backbone pathways

We designed an experiment, where the oil was repeatedly injected into the model with different rates to form and then change the pathway. The pathway shapes formed right after each injection and 30 min later are illustrated in Fig. 2.20. First of all, oil is injected into the glass plate model at a rate of 0.1 ml/min. The injection is stopped when a stringer pathway is formed (Fig. 2.20a). In the following 30 min, the oil in the pathway continues to migrate until the residual oil forms and migration stops (Fig. 2.20a). Second, oil is injected the same rate; and injection is stopped when a backbone pathway is formed (Fig. 2.20c); a period of 30 min is also taken (Fig. 2.20d). Third, oil is injected at a greater rate of 0.5 ml/min; and injection is stopped when a backbone is formed (Fig. 2.20e); a period of 30 min is taken (Fig. 2.20f). Finally, oil is injected at an even greater rate of 2.0 ml/min until a wider pathway is formed (Fig. 2.20g); and a period of 30 min is used to allow the oil to continue to migrate until the residual oil cluster forms (Fig. 2.20h). Oil migration in the preexisting pathway will not easily change the morphological characteristics of the pathway, especially when the driving forces do not change or only increase a little (Fig. 2.20c, e). However, when the driving forces of re-migration increase significantly, the shape of the preexisting pathway may be changed, resulting in a wider pathway (Fig. 2.20g); but the pathway expansion is limited, and the outline of the original pathway still largely remain. In contrast, using the same model as that in Fig. 2.20, oil is injected, initially, at a large rate of 2.0 ml/min. The pathway appears to be of the piston pattern; and oil cluster moves upward as a whole (Fig. 2.21). This pattern is significant different from that in Fig. 2.20g, even though the two pathways were formed at the same injection rate and other conditions. This observation indicates that the migration pathways formed in the early stage provide an important constraint on later migration.

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89

Fig. 2.20 Images showing the morphological characteristics of pathways formed in a plate model during multiple injections and periods of no injection. a Initial migration pathway at an injection rate of 0.1 ml/min; b residual pathway of that in (a) after a 30-min break; c pathways formed during the second injection at a rate of 0.1 ml/min; d residual pathway of that of (c); e pathway formed after the third injection at a rate of 0.5 ml/min; f residual pathway of that of (e); g pathway formed after the last injection at a rate of 2.0 ml/min; and h the residual pathway of (g)

2.1.4 Pathway Saturation and Oil Saturation in Pathway The loss of oil and gas during migration is an important attribute in the study of oil and gas accumulation (Catalan et al., 1992; Dembicki & Anderson, 1989; Karlsen & Skeie, 2006; Luo et al., 2007a, 2007b, 2007c; Schowalter, 1979). Birovljev et al. (1991) defined a concept of pathway saturation. That is, the pathway saturation of expelling phase in porous media is the volume percentage of expelling phase remained in all passes and pathways at the end of the experiment. Thus, in our experiments, the saturation of migration pathway may be obtained as the ratio of the volume of water displaced from the model during migration to the volume of total pores in the model, as So =

q ·t V ·φ

(2.5)

90

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Fig. 2.21 Image of initial migration pathway formed using an injection rate of 2.0 ml/min in a plate model, same as that in experiments shown in Fig. 2.20

where S o is the oil saturation of migration pathway; q is the injection velocity; t is the time from the beginning of injection to the formation of the migration pathway through the model; V is the total volume of the interior space of the model; φ is the porosity of the porous medium in the model. In fact, the conditions for Eq. 2.5 are very strict: the expelling phase occupies all the internal porous space within the pathway during the experiment; the shape of the pathway and the saturation of the expelling phase in the pathway remain unchanged at the end of the experiment. The observations in the physical migration experiments described above indicate that the morphological characteristics of pathways and the oil situation within the pathways change at different migration stages, including initial formation of a pathway and after the stoppage of migration. Therefore, it is necessary to measure the oil saturation in the pathway first, before calculation of the pathway saturation in the experiment. 1. Nuclear magnetic resonance measurement of oil saturation in pathways The most reliable method to obtain the oil saturation in migration pathways is a direct measurement during the migration process. Nuclear magnetic resonance (NMR) technology is an effective method for measuring hydrocarbon saturation in rocks (Miao et al., 2004). Most important reservoir physical parameters, such as porosity, permeability, percentage of movable fluid, and oil saturation, can be quickly obtained using this method (Wang et al., 2001a, 2001b). The oil saturation within pathways was measured in the buoyancy driving migration experiments using the tubular models filled with glass beads or river sands, respectively. The grain size is 20–40 mesh. Based on the preliminary analysis of the experiments, three time points were set to analyze the oil saturation state within the migration pathways (Fig. 2.22): the initial state before oil migrating, the forming

2.1 Physical Experiments of Secondary Petroleum Migration

91

stage of the pathway, and the relict pathway stage after migration. The measured values are averages within specific ranges (Fig. 2.22). Tables 2.3 and 2.4 show the calculated values in the glass bead and river sand models, respectively. In Fig. 2.22, intervals a, d and h are located inside the initial oil column. For the glass bead model, the oil saturation in the initial oil column before migration is 83.11% (Table 2.3). During the pathway formation, the average oil saturation in the

b

Fig. 2.22 NMR images of migration pathways at different stages and positions. a The initial stage just before experiment; b a moment during the formation of the pathway; c at the stage after the experiment. The oil saturations are measured values over intervals of a–h

e

b c g

d

f

a

a

h

c

Table 2.3 Average values of oil saturation in different ranges within the migration pathway in glass bead model Initial column before migration Oil saturation in pathway during its formation Range a*

* Positions

83.12%

Range b

43.00%

Oil saturation in pathway after migration Range e

83.33%

Range c

74.55%

Range f

71.57%

Range d

79.4%

Range g

26.50%

Range h

29.40%

of the ranges a–h are shown in Fig. 2.22

Table 2.4 Average values of oil saturation in different ranges of migration pathway in the river sand model Initial column before migration Oil saturation in pathway during its formation Range a*

* Positions

83.50%

Oil saturation in pathway after migration

Range b

36.96%

Range e

83.01%

Range c

78.10%

Range f

81.14%

Range d

40.00%

of the ranges a-h are shown in Fig. 2.23

Range g

35.29%

Range h

32.35%

92

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Oil saturation (%)

111 83.3 55.6 27.8 0

a.

b.

c.

d.

e.

Fig. 2.23 Images of migration pathways in a plate model, showing the change of oil saturation in the pathways across and along the model

pathway is 74.55%; but the distribution of the saturation is not uniform, which may be as small as 44% in Interval b. After migration, the average oil saturation in the top accumulation part is 83.33%; and the oil saturation in the relict pathway is less than 27%. However, it can reach 71% locally (Interval g); and the residual oil saturation is about 30% over the range of initial oil column. For the river sand model, the oil saturations measured in different migration stages within the given ranges are basically similar to those of the glass bead model (Table 2.4). The oil saturation of the original oil column at the bottom is ~ 80% before migration; and the residual oil saturation after migration varies from 26% to 35%. To verify the oil saturation in the pathway measured by NMR imaging method, we measured the volume of internal glass tube and the weight of filled glass beads to obtain the porosity of the model. Then we injected oil into the top of the tubular model to form an original oil column. When the height of the initial column reaches the designed value, the height of the original oil column is directly measured and the oil volume injected into the model is recorded, and the oil saturation in the original oil column can be calculated (Hou, 2004). After the migration experiment under the condition of complete buoyancy, most oil in the original oil column is replaced by water, leaving only the residual oil (Fig. 2.22b). The residual oil saturation in the original column can be measured to reflect the lower limit of residual oil in the pathway. After the experiment, the tubular model was opened and divided into two parts: the original oil column and the migration path. The fluid and solid particles were taken out, and the quantity of oil in each part was collected by water washing. For each part, the particles and oil were weighed separately; then the saturation of residual oil in this part was estimated. Table 2.5 presents the saturation values of the residual oil. A comparison of the oil saturation data at different migration stages and different positions in pathways (Tables 2.3, 2.4, and 2.5) indicates that the NMR method is feasible. The residual oil saturation within the pathways is evidently influenced by particle size (Table 2.5). The residual oil saturation increases with the decrease of grain size in the model. The residual oil saturation of the model filled with glass beads of 20–40 mesh is between 20.0% and 30.0%, whereas that filled with glass beads of 60–80 mesh is close to 40.0%.

2.1 Physical Experiments of Secondary Petroleum Migration

93

Table 2.5 Saturation of residual oil obtained by water washing Number of experiments

No. 1

Grain size (mesh)

20–40 20–40 20–40 20–40 40–60 40–60 60–80 60–80

No. 2

No. 3

No. 4

No. 5

No. 6

No. 7

No. 8

Initial column height H (cm)

5.5

10.0

11.0

15.8

11.5

18.0

14.8

20.5

Internal volume of the section 11.20 of tube corresponding to H (cm3 )

20.36

22.49

32.16

22.75

35.2

26.44

37.93

Oil volume injected in to the model (cm3 )

8.0

15.0

17.0

25.0

18.0

27.0

20.0

27.0

Porosity of the model (%)

39.43

39.43

39.60

39.60

37.88

37.88

35.84

35.84

Total volume of the oil washed out (cm3 )

2.5

6.0

5.6

6.6

6.9

12.8

10.5

14.9

The oil saturation in the initial 71.43 column before migration (%)

73.67

75.59

77.74

79.12

76.70

75.64

71.18

The residual oil saturation in the initial column(%)

22.32

29.47

24.90

20.52

30.33

36.36

39.33

39.02

The residual oil saturation in the pathway (%)

31.25

40.00

32.94

26.40

38.33

47.41

52.00

54.82

Figure 2.23 shows the measurement results of one of the two-dimensional glass plate model experiments. During the NMR imaging, the oil is migrating; the imaging direction is parallel to the axial longitudinal direction; and the dark color represents the pathway. The curves superimposed on the images show the change of oil saturation on the horizontal scanning line. In Fig. 2.23a, the peak on the curve on the left side is the calibration value with oil saturation of 100%. Because the pixels in the MRI image are larger than the actual pore diameter, the oil saturation in Fig. 2.23 represents the average value of all pores in a single pixel. In each diagram, the cross tangent at the lower part of the image does not scan through the migration pathway, so the oil saturation curve shows only some noise. The scanning line in diagrams b–d cuts through the migration pathway, showing that the oil saturation in the pathway varies from place to place, and the largest saturation is up to 80% (Fig. 2.23b). The oil saturation in the migration pathway gradually decreases when the scanning line moves upward (Fig. 2.23b–d). In the experiment with a three-dimensional model (Figs. 2.6 and 2.7), the oil saturation and its change at different positions in the pathway at different migration stages were also observed and analyzed. The oil injected from the bottom of the box model moves upward under completely free conditions. The movement is driven by injection pressure and buoyancy. The pathway form a cylindrical pattern. When the front of the pathway reaches the roof, it gradually moves toward the updip direction laterally. The change of direction of the migration pathway was observed by the NMRI imaging method (Fig. 2.24). Figure 2.24a shows the vertical migration pathway when its front edge just reaches the glass plate roof. When lateral migration occurs beneath the roof, the segment of the pathway is massive, In the meanwhile, the vertical segment shrunk and, in some cases,

94

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Fig. 2.24 NMR images showing the three-dimensional vertical migration pathways at different stages in a three-dimensional model. a When the pathway front just reached the capping plate, the vertical pathway was roughly cylindrical. b When the front migrated laterally, the vertical pathway snapped off. c After a while, the migration pathway was refilled

broke off; and most of the oil occurred as drops or small clusters (Fig. 2.24b). After a while, the vertical segment was filled again by the oil (Fig. 2.24c). In comparison to the observations in aforementioned experiments, the change of oil saturation in the vertical segment (Fig. 2.24b, c) should be the manifestation of snap-off and segmented migration. The cluster size and oil saturation in the vertical segment changed continuously during the migration process; however, the clusters appeared significantly smaller than the those during the initial vertical migration when the pathway reaches the glass plate roof for the first time (Fig. 2.24a). The characteristics and changes of oil saturation in the pathway during the lateral migration have been described in Fig. 2.9. At most places along the lateral migration pathway, the oil saturation is 40–60%; only at a few places, the saturation reaches 80%. Comparing with the distribution of oil saturation in the initial vertical pathway (Tables 2.3 and 2.4), the influence of caprock on the oil saturation in the lateral pathway appears not significant. When the front of the pathway reaches the top of the model, oil is still being injected into the model. The entire pathway is observed and scanned; and the oil saturation in the pathway is calculated from the scanned images. Figure 2.25 shows the pathway images and oil saturation distribution on four scanned slices with a spacing of 10 cm; and the first slice is 25 cm from the injection point. The width of the pathway beneath the top plate is basically equal, about 2 cm, even though it is variable. The pathway is not completely close to the glass plate cover; but there are many small fingers on the top of the pathway, some of which may be in contact with the cover. Figure 2.26a, b show the changes in the area of the cross-section of the pathway and the oil saturation on the scanned slices perpendicular to the direction of lateral migration. The cross-sectional area of the pathway changes significantly along the migration direction; and the largest area can reach 14 cm2 , whereas the smallest area is only 1 cm2 . The average oil saturation in the pathway cross-section is basically stable, fluctuating around 45% along the direction of lateral migration.

2.1 Physical Experiments of Secondary Petroleum Migration Position （cm）

Content

Images oil saturation (%)

95 Average Saturation （%）

section 25

46.9

saturation distributi on

section 35

42.1

saturation distributi on

section 45

saturation distributi on

3

49.1

section 55

saturation distributi on

44.2

Fig. 2.25 Images showing the pathway morphology and oil saturation distribution on representative scanned slices perpendicular to the pathway

The variation of oil saturation in pathway during periods of different migration conditions can be documented on scanned slices perpendicular to the lateral migration direction at a 45-cm spacing starting from the injection point. The MRI scanning is done on the same slice for three migration states: (1) during the formation of the

96

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Fig. 2.26 Diagrams documenting the morphological characteristics of lateral migration pathway from the inlet at the right edge of the 3D box to the outlet at the left end (Fig. 2.9). a The area of cross sections of migration pathway on a series of scanned slices along the lateral migration pathway. b The average oil saturation on the slices

pathway, (2) the relict pathway after migration, and (3) renewed migration along the pathway after reinjecting oil (Fig. 2.27). The data derived from each image are the measured values of the cross-sectional area of the pathway and the average oil saturation. Variations in oil saturation within the pathway in the three states are not significant; but the cross-section area of the pathway changes over a wide range. The area of the relict pathway is smaller than that of the actively migrating pathways. The area of the remigration pathway is larger than that of the relict pathway, but smaller than that of initial pathway. The oil saturation in the pathway also changes correspondingly: the oil saturation in the initial migration pathway is greater than that in the remigration pathway; and the saturation in the relict pathway is the smallest.

Fig. 2.27 Images and values showing the morphology, cross-section area, and oil saturation of the pathways under three migration states

2.1 Physical Experiments of Secondary Petroleum Migration

97

2. NMR measurement of residual oil saturation of reservoir rocks The size of pore throats of porous media has a large effect on the residual oil in the migration pathway. To explore the effect, we measured the residual oil saturation in the reservoir core samples. The samples are injected completely with oil and then washed thoroughly with water; and the residual oil saturation in the samples is measured by the NMRI method. Since the rock samples have been completely filled, the oil that cannot be washed out with water is basically equivalent to the residual oil in the migration pathway. Twenty three sandstone reservoir samples are selected from Daqing, Shengli, and Liaohe oilfields in China for residual oil saturation analysis. The porosity of these rock samples ranges from 8.64% to 27.1%; and the permeability from 0.18 × 10–3 to 278.2 × 10–3 μm2 . The physical properties of these samples vary over a wide range and are likely representative to many other sandstone reservoirs. The test is done using the nuclear magnetic resonance instrument described before in this chapter. The specific workflow is as follows: (1) A standard cylindrical core of φ2.5 cm × 3.5 cm was obtained in the direction perpendicular to the side of the core. The actual core length and diameter are measured. (2) The core was cleaned with alcohol and benzene with the extraction method, dried in vacuum for 48 h, and dry-weighed. Then, the sample was vacuumed for 24 h, and saturated with water of a 5000 mg/L salinity, wet-weighed. The water saturation of each rock sample was calculated. (3) Oil was injected to displace the water in the core samples until to their bound water state. The oil is the crude oil from the Changqing Oilfield and kerosene. The T2 relaxation spectrum of each sample in the initial state was obtained by the NMR test; and the amplitude and sum of T2 spectrum were calculated. (4) Core samples were immersed in a MnCl2 solution with a salinity of 15,000 mg/L for 24 h. A NMR test was conducted on the sample. The integral sum of the T2 spectrum amplitude was then calculated. (5) The oil in the sample was expelled to the residual oil state, with a displacement velocity of 1.0 m/day. Then, the NMR test was done for the sample. The integral sum of the T2 spectrum amplitude was calculated. Table 2.6 shows values of some conventional parameters of 23 core samples and the measured values of oil saturation. S o1 is the initial oil saturation; S W the irreducible water saturation; E O the displacement efficiency of water to oil; and S o2 the residual oil saturation. The saturation of residual oil in the samples ranges from 25% to 35%, and mostly between 25% and 30% (Fig. 2.28a). The test results (Table 2.6) show that the physical properties of the cores affect the oil saturation in migration pathways, as related to the surface properties of the particles. The differences between glass beads and river sands are evident (Tables 2.3 and 2.4). The glass beads used in the experiment are treated to be strongly water-wet, whereas the river sands are not. In addition, the surface of river sands is not smooth and may be coated by microscopic particles and clay. Furthermore, the mineral

98

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Table 2.6 Conventional parameters of core samples as measured in the test Order

Diameter (cm)

Length (cm)

Porosity (%)

Permeability (10−3 μm2 ) 2.04

S o1 (%)

S w (%)

E o (%)

S o2 (%)

1

2.51

3.51

13.29

56.35

43.65

45.73

30.58

2

2.51

3.52

11.95

1.61

53.68

46.32

53.28

25.08

3

2.49

3.5

11.82

0.18

38.18

61.82

34.06

25.18

4

2.5

3.51

11.09

0.32

40.97

59.03

39.13

24.94

5

2.5

3.51

12.25

3.66

53.51

46.49

51.7

25.85

6

2.51

3.5

12.01

3.03

51.77

48.23

46.57

27.66

7

2.49

3.5

10.5

1.59

61.99

38.01

52.01

29.75

8

2.49

3.52

12.1

4.22

62.18

37.82

45.37

33.97

9

2.5

3.51

11.34

57.56

42.44

40.14

34.46

1.17

10

2.5

3.49

11.01

0.48

45.71

54.29

35.62

29.43

11

2.49

3.49

10.88

0.98

48.51

51.49

46.03

26.18

12

2.5

3.52

9.66

0.39

45.66

54.34

42.68

26.17

13

2.49

3.5

10.78

1.2

47.97

52.03

37.45

30.01

14

2.5

3.51

9.38

0.34

46.02

53.98

30.04

32.2

15

2.51

3.5

11.37

0.22

47.36

52.64

44.15

26.45

16

2.5

3.5

11.94

2.35

51.16

48.84

48.23

26.49

17

2.51

3.51

8.61

0.28

45.88

54.12

39.01

27.98

18

2.49

3.5

278.2

71.35

28.65

56.48

31.05

27.1

19

2.49

3.49

16.7

81.9

74.19

25.81

57.2

31.75

20

2.5

3.49

16

16.9

56.55

43.45

47.32

29.79

21

2.51

3.49

24.8

92.5

51.64

48.36

50.28

25.68

22

2.49

3.51

22.8

27.2

62.06

37.94

54.58

28.19

23

2.5

3.51

20.2

60.26

39.74

52.36

28.71

9.97

composition of grains in cores is complex; and the grain surfaces have been altered during diagenesis. So differences between these porous media in pore structure, particle surface properties, and material composition must be significant. The oil saturation in the relict pathways increases with the decrease of particle size (Tables 2.5 and 2.6). The trend of data in Table 2.6 is clear in Fig. 2.28, as shown by the correlation of porosity and permeability with the ratio of residual oil saturation (S r = S o2 /S o1 ), respectively. In Fig. 2.28, S r in the migration pathway increases with the decrease of porosity, especially when the porosity is less than 12% (Fig. 2.28a). Such inverse correlation between S r and porosity and permeability indicates that smaller pores (or more precisely, throats) in reservoir rocks tend to obstruct the migration of oil. This suggests that the heterogeneous distribution of residual oil within pathways may largely be controlled by the heterogeneities of the texture of the cores. 3. Oil saturation in pathway and pathway saturation

2.1 Physical Experiments of Secondary Petroleum Migration 0.7

0.6

0.6

So2/So1

So2/So1

0.7

99

0.5

0.5

0.4

0.4 8

12

16

20

Porosity (%)

24

28

0.1

1

10

100

Permeability (10-3μm2)

Fig. 2.28 Relationship between the residual oil saturation (S r = S o2 /S o1 ) and porosity (a) and permeability (b) in core samples

The results of our experiments show that the initial migration pathways occupy the largest space. The pathway will shrink in later stages. The variation is commonly very large and difficult to document. When oil, the non-wetting phase, passes through a water-wet porous medium, it can only occupy a part of the pore space as the pathway. Because of the existence of bound water and the detention caused by variations in pore structure, the oil saturation in the pathway is the highest at the initial stage of pathway formation with a saturation up to 80%. In later stages, the oil in the pathway can neither maintain the originally occupied space, nor flow along the pathway completely; some amount of oil has to be left on the pathway with an uneven distribution (Figs. 2.8, 2.10 and 2.22). Some clusters on the pathway can completely disappear, but some remain intact. So, a representative oil saturation in the pathway cannot be obtained by measurement at selected points along the pathway. A statistical analysis is required to document the residual oil saturation over a specific range of the pathway. Therefore, we used two parameters to document the morphological characteristics of migration pathway and the distribution of oil and gas saturation in the pathway, in order to characterize the migration pathway under different migration conditions and environments. The oil saturation in pathway is defined as the volume proportion of oil in a specific space within the initial migration pathway, while the pathway saturation is defined as the volume ratio of the initial migration pathway to the volume of the conduit. The meaning of these two parameters and the method used to derive their values are illustrated by analyzing the experimental results of oil migration in a plate model. Because the thickness of the filling materials in the model is small, the migration process in the model can be regarded as two-dimensional, and the migration pathways observed from one side of the model can be regarded as going through the entire pore space in the direction perpendicular to plate. In this way, the oil saturation at any

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

segment of the pathway may be estimated using images of the migration pathway during and after the experiment. Figure 2.29 illustrates the work flow of pathway image processing in an experiment. The images in Fig. 2.29 are rotated 90°; and the left side is the bottom and the right side is the top. Figure 2.29a, b are images of the pathway formed during the initial migration and after the completion of migration. The images are sharpened (Fig. 2.29c, d) and then superposed (Fig. 2.29e) to illustrate the distribution of residual oil (black clusters) in the initial pathway (black clusters plus grey ones). In the experiment, the pathway saturation is the ratio of the initial pathway volume (red pixels) and the conduit volume (all pixels in the black box; Fig. 2.29c). Although the oil saturation in the pathway changes at different stages and positions, the volume of the initial pathway can be considered as a constant. An important reason for such a definition is that it was adapted in previous studies on the pathway formation process, which have achieved many important and basic understandings on formation mechanism, morphological characteristics, and description methods (Catalan et al.,

Fig. 2.29 Images of a part of a Hele-Shaw Cell experiment showing the geometry of oil clusters (red) during initial pathway development (a), and when migration has ceased (b). c, d are sharpened images of (a) and (b), respectively. e are superimposed images of c (grey) and d (black). In all images, oil has migrated from bottom to top. The scale of the images is in 10 cm

2.1 Physical Experiments of Secondary Petroleum Migration

101

1992; Dembicki & Anderson, 1989; Hirsch & Thompson, 1995; Schowalter, 1979; Tokunaga et al., 2000). And these understandings can be used as references for our study. The ratio between the width of relict pathway and the width of the original pathway in a unit of pixel on the images is the residual oil saturation (Fig. 2.29): Si =

ni Ni

(2.6)

where N i is the number of pixels of the initial pathway along the line at the height of i (gray and black areas in Fig. 2.29e); N i the number of residual pathway pixels along the line (black in Fig. 2.29e). Because the image in Fig. 2.29e is sharpened, S i here is the relative saturation. The actual oil saturation can be obtained by normalizing S i with the oil saturation in the original oil column described previously. The change of the oil saturation along the residual pathway can be demonstrated by plotting S i against the model height (Fig. 2.30). After migration ceased, the change of residual oil saturation along the migration pathway is variable from less than 5% to 100%. Generally, the residual oil saturation ranges from 10% to 60% mostly and 30% on average. On this basis, the oil saturation (S m ) in any segment of the pathway can be given as: b+n 1∑ Sm = Si n i=b

(2.7)

Si

where b is the height of the starting point of the segment to be observed and n is the height of the segment. The value is an average of oil saturation in this segment.

Height of the pathway (pixel)

Fig. 2.30 Diagram showing the change of oil saturation (S i ) in the residual migration pathway along the height of the model in a unit of pixel

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

2.1.5 Oil Migration in a Single Fracture Open fractures, in general, greatly increase the permeability of rocks (Snow, 1969) and serve as conduits for fluid flow. Faults on a basin scale constitute important conduits for vertical oil migration (Hooper, 1991), and often control the distribution of hydrocarbons (Zhang et al., 2003). For many years, studies on oil and gas migration along faults have been limited to fault sealing (Knott, 1993; Zhao et al., 2001); or the fracture systems have been regarded as a pore medium approximately on a larger scale; and the multiphase Darcy’s Law have been used to describe the current characteristics of oil and gas in the faults (Kang, et al., 2003; Marle, 1965). Little attention has been paid to the characteristics of migration by displacement of water in the fractures. Here, we use a pair of glass plates with rough surfaces to construct a narrow fracture model to observe the formation of migration pathways in a fracture. The results will be compared with these from pore medium models to explore the dynamic characteristics of oil and gas migration in fractures. 1. Experimental model of plate fracture The experiment model is constructed using two rough undulating glass plates. The rough surfaces are facing each other and bonded with glass glue to make a parallel fracture. The random combination between the two rough surfaces forms a microscopic heterogeneity in the fracture. The glass plate is 40 cm long, 20 cm wide and 3 mm thick. It is important that the glass plates are parallel to each other. The sides and the bottom of the glass plates are sealed with a rubber ring surrounding the fracture. Oil is injected at the bottom of the model with a double syringe pump at a controlled flow rate. At the top of the model, the liquid is evacuated and spills over along the entire width of the fracture. The fracture is always vertical; so the fluid displacement is against the gravity. The displacement fluid is non-wetting dyed kerosene. At room temperature, the surface tension of the kerosene is 13 dyn/cm; the density 1.043 g/cm3 ; and the viscosity 21 m Pa s. In order to consider the influence of fluid density change on migration, sucrose solutions (WSMS, water–sucrose mixture solution) with a concentration of 15%, 20%, 25%, and 35% are used as the wetting phase to saturate the fracture. In experiments, the kerosene is injected into the model at three speeds of 5, 25, and 100 ml/h. Combined with four sucrose solutions of variable concentrations, a total of 12 experiments were carried out. 2. Experimental results The results of 12 migration experiments at three kerosene injection rates and four sucrose solution concentrations are shown in Fig. 2.31. For the same injection rate, the higher the concentration of sucrose solution, the lower the density difference between the two-phase fluids will be; as a result, the pathways of oil migration become narrower, even jam together. The influence of the injection rate is also evident. When the injection rate is 5 ml/h, the pathways occur as small clusters, although

2.1 Physical Experiments of Secondary Petroleum Migration

103

they get coarser with the increase of the concentration of sucrose solution; but the morphological characteristics of the pathway are basically unchanged. When the injection rate increases to 100 ml/h, the pathways form a network throughout the entire fracture. The lower the concentration of sucrose solution is, the wider the distribution of the pathway network in the fracture will be. When the concentration of sucrose solution is 15%, the migration pathway occupy almost the entire fracture, leaving only some “holes” surrounded by oil clusters. The injection rate affects the morphological character of pathways in fractures. When the rate is 5 ml/h (Fig. 2.31a), the pathway continuously breaks and forms a discontinuous pattern in a sucrose solution of a high concentration. When the rate increases to 25 ml/h (Fig. 2.31b), the pathway becomes wider and continuous. Obviously, when the injection rate is high, there is more oil available to form longer and continuous migration segments; and the pathways are also expanded, and vice versa. If the injection rate is kept constant, the pathways will be blocked with an increase of density difference between the two phases. When the rate is 5 ml/h (Fig. 2.31), with the increase of sucrose solution concentration, the pathways change from a continuous single branch to a frequently snapped-off pattern; and the pathways become narrower. This demonstrates that the increase in density difference between Fig. 2.31 Images showing experimental results of oil (black) migration in parallel plate fractures saturated with sucrose solution, under the conditions of oil injection speed of 5, 25, and 100 ml/h (a, b, c, respectively). The concentration of sucrose solution is 15%, 20%, 25%, and 35% from left to right in each panel

a) v=5ml/h

b) v=25ml/h

c) v=100ml/h

Density

height

Fig. 2.32 Images showing temporal evolution of pathway patterns along model fracture. Three combinations of oil injection rate and solution concentrations are used, rate is 25 ml/h and concentration is 20% and images are taken at every 480 s (a); 5 ml/h and 35%, and images are taken every 320 s (b); and 100 ml/h and 35%, and images are taken every 90 s (c)

height

2 Mechanisms and Processes of Secondary Migration of Oil and Gas

height

104

experiment time

oil and sucrose solution will affect the mode of oil migration. The larger the density difference is, the higher the probability of oil jamming, and the narrower the pathways will be, and vice versa. The effect of density difference between the two fluids is the opposite to that of injection velocity. The temporal evolution of oil saturation is shown for three scenarios of viscousdensity contrasts in Fig. 2.32. When the injection rate is at 25 cm3 /h, the displaced fluid sucrose solution is the densest with a concentration of 35%. The pathway pattern is a single and tortuous gravitational stringer, with the growth of a tortuous finger at a very high velocity at the tip. Comparing with the previous experiments, the growing finger appears thinner and less tortuous. Finally, the high density contrast here promotes snap-offs which cause segmented oil migration. In the six experiments in Fig. 2.32b, the injection rate is at 5 cm3 / h; and a lighter sucrose solution of a concentration of 20% is displaced. The results are similar to that at an injection rate of 25 cm3 /h; the pathway is a single and tortuous gravitational stringer. The mean velocity of the fingertip is estimated as 0.15 mm/s. The large density difference between the solution in pores and the injected oil produces a significant gravitational instability, which results in a single finger growing up at a high velocity. The large tortuosity of the finger indicates that the pathway is affected by the capillary force acting on the displacement. The absence of flattening at the oil-WSMS interface indicates that viscous forces are negligible.

2.1 Physical Experiments of Secondary Petroleum Migration

105

When the injection rate is 100 cm3 / h and the displaced fluid is dense with a concentration of 35%, three fingers form at the beginning, but rapidly merge, eventually form a complex pattern of randomly growing clusters and fingers. Trapping occurs where a non-invaded area filled with sucrose solution remains surrounded by the oil. Despite the high mean displacement velocity (∼ 65 cm/h), no stable pistonlike front is observed. Viscosity effects are too small to completely stabilize the interface. It is difficult to determine whether the finger growth is due to gravitational force and/or capillary force; but the resulting pattern is a tortuous cluster with many fingers. 3. Discussions of the experimental results of oil migration along fractures Snap-offs and segmented migration are very common in tubular and plate models (Meakin et al., 2000; Luo et al., 2004). This is mainly due to the fact that, under buoyancy, the supply of oil at the lower part of the breakpoint cannot compensate for the shortage of driving force, while the buoyancy of the oil segment above this point is large enough to snap off the originally continuous pathway at the breakpoint. And the upper oil segment moves up rapidly under the buoyancy, whereas the lower oil segment remains motionless below the breakpoint. Therefore, buoyancy and injection rate are the two main factors to promote or prevent snap-offs, respectively. The microscopic non-uniform distribution of pores and throats also plays an important role. In our experiments (Fig. 2.31), the greater the density difference between oil and sucrose solution, the greater the buoyancy per unit volume of oil segment, resulting a larger probability for snap-offs, and vice versa. The change in injection rate alters mainly the oil supply capacity below the breakpoint. The smaller the injection rate is, the less the oil supply is. As a result, the more likely the oil pathway to be snapped off. The larger the injection rate is, the more sufficient the oil supply is, thus, the less likely snap-offs will occur. As aforementioned, the mechanical characteristics of oil and gas migration are the result of interaction among capillary force, viscous force and buoyancy. Based on the relationships among these forces, the oil migration pathway can be divided into three modes: piston, finger and stringer (Figs. 2.12 and 2.13). The analysis of the experimental results (Figs. 2.31 and 2.32) indicates that the displacement of water by oil in a narrow fracture also behaves in similar patterns. The displacement of fluids in parallel plate fracture occurs basically in a two-dimensional space. The difference in injection mode needs to be considered. That is, oil can be injected at the bottom of the plate model (Tokunaga et al., 2000; Hou, 2004), or just at one point at the bottom (Fig. 2.5). We use two types of models to carry out oil migration experiments; and the results are very similar (Fig. 2.33) and comparable to the previous two-phase displacement experiments with two-dimensional porous media (Hou et al., 2004; Lenormand et al., 1988; Meakin et al., 2000). The results in Fig. 2.31 indicate that the results of Experiment c1 may be regarded as close to a piston pattern; experiments c2–c4, and b1 to a finger pattern; and the rest stringer pattern. Therefore, the characteristics of oil migration in a narrow fracture filled with water are similar to those in the porous media. The migration pathway only accounts for a part of the fracture conduit and has an irregular nature. The pathways show piston,

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Fig. 2.33 Images of pathway patterns for a comparison between that formed in a single fracture model (a) and that in a plate porous medium model (b)

a

b

finger, or stringer patterns. The effects of various factors on the formation of these patterns are similar to that in models of porous media.

2.2 Mechanisms and Processes of Secondary Migration of Hydrocarbon Secondary migration is the movement of hydrocarbons from source to trap in permeable rocks. From the point of view of physics, the secondary migration can be considered as a special case of two immiscible fluids flowing in porous media. Since the rock media are initially saturated with water and the surface of framework grains are water-wet, it is more accurate to say that the secondary migration is a process of invasion of non-wetting fluid into a rock to displace a wetting fluid (Dembicki & Anderson, 1989; Catalan et al., 1992; Carruthers and Ringrose, 1998; Luo et al., 2004). The main feature is that the density of the invasion fluid is less than that of the expelled fluid. Therefore, in static water environments, buoyancy is the primary force driving secondary migration, and is caused by the density difference between hydrocarbon and surrounding water. The resistances are mainly the capillary and viscous forces. The magnitude of these forces depends on the parameters characterizing the pore media and the fluids in them; and the relationships among the forces can be described by some dimensionless numbers (Hirsch & Thompson, 1995; Meakin et al., 2000; Thomas & Clouse, 1995; Wilkinson, 1984). Based on these dimensionless numbers, questions about the migration mechanism, such as the geometric shape of migration pathways, migration rates, and the change of the pathways with time, may be answered through experiments and theoretical analyses. The answers

2.2 Mechanisms and Processes of Secondary Migration of Hydrocarbon

107

and understandings can then be extrapolated to the studies on migration processes in actual basins. In this section, on the basis of analyses on a large number of physical simulation experiments, two physical dimensionless numbers, the Bond number and capillary number, are introduced to quantitatively describe the displacement characteristics of two immiscible fluids. The phase diagram of the secondary migration pattern is established. The importance of these dimensionless numbers in describing the relationship between various forces at different scales is further discussed. The relevant physical parameters and their order of magnitude under realistic basinal conditions of secondary migration are determined. As a result, some important understandings about the mechanism of secondary migration of hydrocarbon are achieved.

2.2.1 Dynamic Characterization of Hydrocarbon Migration and Pathway Patterns Through systematic physical experiments, we found that oil may migrate in three patterns in pore media: piston, finger and stringer. The three patterns differ in the front shape, and overall characteristics and migration rate of the pathways. Many factors affect the formation of pathways; and a change of any factor may cause the pathway pattern to change from stringer to piston. Therefore, it is very difficult to determine the dominant controlling factors. To apply the understandings gained through physical experiments to the actual geological conditions, we use appropriate dynamic parameters to characterize the pathways. 1. Characterization parameters of dynamics of hydrocarbon migration The buoyancy of migrating hydrocarbon depends on the height of continuous hydrocarbon column. Due to the limited size of the physical models, the buoyancy in the experiments is often small. When oil is injected into the lower part of the continuous oil column, the injecting pressure is directly transferred to the oil column, which can be regarded as increasing the height of the oil column in the lower part of the model; and the sum of the buoyancy of the continuous column in the pathway can be regarded as equivalent buoyancy. Therefore, only three forces may be considered involving in the migration process, namely buoyancy (gravity), capillary, and viscous forces. The first two forces always function in the models, while the viscous force only plays a role when the fluids are in motion. Some previous studies have analyzed the forces controlling the migration of fluids and the proportional relationship among them in the fluid displacement. The dynamic relationship was synthesized by constructing dimensionless numbers (Wilkinson, 1984, 1986). The capillary number (Ca) is the ratio of viscous force to capillary force: Ca =

μo υo σ cos γ

(2.8)

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

where υ o is the Darcy flow rate of oil; μo the viscosity of oil; σ the interfacial tension; and γ the contact angle. Hydrocarbon migration in basins occurs generally under the action of buoyancy. Wilkinson (1986) defined a dimensionless number, Bond number (Bo), to characterize the interaction between buoyancy and capillary forces. Bond number is the ratio of buoyancy to capillary forces: Bo =

(ρw − ρo ) · gr 2 σ · cos γ

(2.9)

where ρ w is the density of water; ρ o the density of oil; g the gravitational acceleration; and r the specific throat radius of a pore medium. Because of the difficulty in obtaining the throat radius in experiments, Tokunaga et al. (2000) modified the Bond number as: Bo' =

(ρw − ρo )g sin θ K σ cos γ φ

(2.10)

where φ is the porosity; K the absolute permeability; and θ the dip angle of migration conduit. For a random packing model, the absolute permeability can be obtained by Kozeny-Carman equation (Bird et al., 1960). 2. Phase diagram of secondary migration patterns Based on numerical simulation, Lenormand et al. (1988) put forward a diagram showing the relationship between the capillary number and the viscosity ratio of two fluid phases to discuss the morphological characters of pathways of the nonwetting phase displacing the wetting phase under the condition of no buoyancy. Three pathway patterns of the non-wetting phase, viscosity fingering, capillary fingering and stable displacement, are identified. Tokunaga et al. (2000) carried out oil migration experiments by using glass tubes filled with glass beads, under the conditions where the buoyancy plays an important role. They analyzed the relationship between capillary number and the modified bond number to discuss the characteristics of pathway patterns of stable displacement and capillary fingering. Based on their experiments, the distribution of results on the phase diagram is uneven; and there is a no-data zone between the ranges of piston and finger patterns, which presents two alternatives for the boundary between the two patterns on the diagram. The velocity in the capillary number is the Darcy velocity, i.e., the amount of flow through a unit cross-sectional area in unit time (m/s), and is actually an average velocity (de Marsily, 1981). In the injection experiments, the Darcy velocity is usually obtained by dividing the oil injected into a tubular model by the cross-sectional area of the tube in unit time (Tokunaga et al., 2000). For experiments under the condition of complete buoyancy, the amount of oil in the migration pathway can be directly obtained with the water washing method after the experiment. The amount of oil divided by the migration time and the inner cross-sectional area of the tube is the Darcy velocity. We think that the velocity derived in this way is equivalent to

2.2 Mechanisms and Processes of Secondary Migration of Hydrocarbon Fig. 2.34 An example of the phase diagram of secondary hydrocarbon migration. The horizontal axis is the Bond number and vertical axis the capillary number. A, B, C are areas representing the piston, finger, and stringer pathway patterns, respectively

109

Piston Finger Stringer

the average Darcy velocity calculated by the injection volume per unit time in the injection experiments. Using our experimental results and in reference to the experimental data published by Catalan et al. (1992), Tokunaga et al. (2000), we calculated the values of capillary number and Bond number using Eqs. (2.8) and (2.10), and constructed a logarithmic cross plot between the two numbers, which is the so-called phase diagram of migration pathways (Fig. 2.34). The phase diagram can be divided into three areas representing different pathway patterns (Fig. 2.34): Area A representing the dynamic conditions for the piston pattern, Area B for the finger pattern, and Area C for the stringer pattern. The phase diagram here is different from that of Tokunaga et al. (2000). Here, the stringer pattern is identified. This diagram is important for the analysis of dynamic conditions for displacements of immiscible fluids. In actual basins, buoyancy is usually the most important driving force; and the stringer pathways dominate (Luo et al., 2004, 2007; Vasseur et al., 2013). The relationship among the migration dynamic conditions is clearly illustrated in the phase diagram (Fig. 2.34), so are boundaries separating the three patterns. The systematic trend of the boundaries suggests the stability of the displacement interface of two immiscible fluids: the increase in capillary number corresponds to the increase in the viscosity of the displacement phase, resulting in a stable displacement interface and a piston pathway pattern. The increase in Bond number corresponds to a decrease in the density of the displacement phase and an increase of gravity (buoyancy), resulting in an unstable displacement interface and a stringer pattern.

2.2.2 Mechanisms and Processes of Pathway Formation The purpose of our experiments is to understand the forming mechanisms and processes of migration pathways in actual basins. Because the experiments must be completed in a very short time, the pores and throats of the porous media in the physical models are commonly much larger than those in reservoir rocks. Hence,

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

it is necessary to link the dynamic similarities between the model and actual basin to understand the mechanisms and processes of the migration mechanism under the realistic basin conditions (Thomas & Clouse, 1995; Vasseur, 2013). Based on the physical principle of multiphase seepage in macroscopically homogeneous porous media and on the basis of the results from our systematic experiments, the basic mechanisms and processes of migration are discussed in detail below. The results of four two-dimensional plate models are used (Table 2.7; Figs. 2.35, 2.36, 2.37 and 2.38). Table 2.8 presents the parameters applicable to natural migration as derived from generally accepted values for natural reservoirs. The values of fluid properties, including viscosity, surface tension, and density difference, are close to those expected in natural conditions (Hantschel & Kauerauf, 2007), and are similar between the experiments and natural reservoirs (Tables 2.7 and 2.8). The values of the parameters are usually larger in experiments than those in natural conditions. 1. Capillary number and Bond number under physical experimental and natural migration conditions Table 2.7 Values of parameters related to our experiments Variable

Unit

Value

Symbols

Variable

Unit

Value

g

gravity acceleration

m/s2

9.81

μ

viscosity of oil

Pa/s

0.00169

Δρ

density difference

kg/m3

208

φ

Porosity

0.36

σ

surface tension

N/m

0.0289

F

Coefficient for permeability

395

2r

pore diameter

mm

0.6–0.8

k

Permeability

Bo

Bond number

8.65 × 10−3

Ca

Capillary number

Symbols

Fig. 2.35 Images of oil migration pathways in 2-D plate model experiments using an oil injection rate of 0.1 ml/min (a) and 3.0 ml/min (b). The height of the model is 30 cm

m2

3.10×10-10 9.17×10-5

2.2 Mechanisms and Processes of Secondary Migration of Hydrocarbon

111

Fig. 2.36 Images of pathway patterns in 2-D oil injection models filled with glass beads of 1– 1.2 mm in size (a), 1.2–1.5 mm in size (b), and 1.5–2 mm in size (c), using an injection rate of 0.5 ml/min. The height of the model is 30 cm

a

b

c

d

e

f

Fig. 2.37 Images at variable time points of a 2D plate model experiment, showing pathway snapoffs and segmentation

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Fig. 2.38 Images at variable time points of a 2D plate model experiment, showing the development of a pathway. The model is filled with a layer of glass beads of a size of 1.5–2.0 mm in the basal part and glass beads of a size of 0.8–1.0 mm in the rest of the model

Table 2.8 Values of parameters related to variable oil injection rate in our experiments Symbols

Variable

Unit

Value (a)

Value (b)

υ

Injection rate

ml/min

0.1

3

q

Injection velocity at 5 cm

m/s

3.54 × 10-6

1.06 × 10-4

8.17 ×

2.45 × 10-3

Ca

Capillary number

10-5

Despite the differences in experimental and subsurface conditions, the understanding of the micro-physics and the introduction of characteristic ratios allow us to apply experimental results to subsurface conditions. In fact, the characterization of a two-phase flow in porous media can be simplified by the introduction of two non-dimensional numbers (Lenormand, 1985; Wilkinson, 1984, 1986). The Bond number (Bo) is the ratio of gravity to capillarity at the pore scale. It is given as (Wilkinson, 1984)

2.2 Mechanisms and Processes of Secondary Migration of Hydrocarbon

Bo = Δρε2 gσ −1

113

(2.11)

where Δρ is the density difference; ε the pore size; σ the surface tension; and g the acceleration of gravity. For the experimental cell, the parameters are presented in Table 2.7 and, for natural migration, in Table 2.11. The main fluid parameters characterizing the flow are the density difference Δρ between water and oil and the oil–water interfacial tension σ. Average values Δρ of 0.2 × 103 kg/m3 and σ of 0.03 N/m are chosen. The key parameter considered in this study is the pore size ε relative to the grain size. In the experiments, is defined by the bead radius in the range 0.1–1.0 mm, and corresponds to a Bond number ranging from 1.1 × 10–3 to 5.4 × 10–2 . Since the experiments have to be performed in hours to days, it is necessary to use relatively large bead radii, implying relatively large permeability coefficients. In the case of natural migration, ε of reservoirs is assumed to range from 5 to 500 μm, leading to a Bo number in the range of 1.6 × 10–6 –1.6 × 10–2 . The size of the beads in models is relatively large compared to that of reservoirs (0.005–0.5 mm. See Table 2.11). The non-dimensional capillary number Ca is the ratio of viscous force to capillary force at the pore scale. It is expressed as (Hantschel & Kauerauf, 2007): Ca = μυr 2 (gk)−1

(2.12)

where μ is the dynamic viscosity of the fluid in Pa·s; υ flux per unit surface in m/s or Darcy velocity; and k the permeability of the porous medium in m2 . The viscosity μ of natural oil at depth is estimated as 2 × 10–3 Pa·s (Ungerer et al., 1990). In reservoirs, k can be evaluated as a function of the pore size ε as ε2 /F, F being a geometrical factor of the order of 103 –102 , taking into account the porosity and tortuosity of pore throats. Ca reduces to Ca = Fμq/ γ (Hantschel & Kauerauf, 2007). In papers dealing with physical aspects of multiphase flows, Ca is often defined as C = μq/σ, omitting the factor F (Wilkinson, 1986). Here, F is evaluated as a function of the porosity φ by the law F = 45 (1 – φ)2/ φ 3 . This formula, known as Ergun’s formula (Ergun, 1952), is a specific application of the Koseny-Carman permeability estimate (de Marsily, 1981) for porous media composed of mono-dispersed packed spheres, with ε as the diameter of the sphere. The parameters for the physical experiments and reservoirs are given in Tables 2.8, 2.9, 2.10 and 2.11. In the model, oil is injected at a point source; and the flow per unit surface area is estimated at a given distance from the point source. In the vicinity of this point source in a range of a few cm, we can assume a radial flow leading to a local Ca number. For most of the experiments presented here, this Ca number is on the order of 10–5 –10–4 . The evaluation of the natural flux of oil during migration is a rather difficult task. A conservative estimate starts from the expulsion rate from source rocks, qs . From the work of England et al. (1991), qs is estimated as 8 × 10–15 –8 × 10–16 m/s. The corresponding Ca is in the order of 10–10 –10–12 . The Ca number for laboratory experiments is much larger than those in real reservoirs. The limitations in the accuracy of fluid pumps and observation time do not provide accurate data to physically simulation the extremely low flow rates during

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Table 2.9 Values of parameters related to variable grain size in our experiments Symbols

Variable

Unit

Value

υ

Injection rate

ml/min

0.5

q

Injection velocity at 5 cm

m/s

1.77 × 10-5

Ca

Capillary number

2r

Pore diameter

Bo

Bond number

ξ

Stringer width

4.09 × 10-4 mm

1.0–1.2 2.14 ×

mm

1.2–1.5 10-2

4.95

3.17 ×

1.5–2.0 10-2

4.82

5.41 × 10-2 4.64

Table 2.10 Values of representative parameters in our experiments Symbols Variable

Unit

υ

Injection rate

ml/min 0.05

q

Velocity in m/s main part

Ca

Capillary number

2r

Bead diameter

Bo

Bond num.

mm

Value

Symbols Variable

Unit Value

ξ

stringer width mm

5.1

1.11 × 10−6

zr/Bo

Stringer height

31.5

2.75 × 10−5

V /φ

Instantaneous m/s velocity

1.73 × 10−3

0.8–1.0

D

Distance between stringers

2.8

mm

m

1.43 × 10−2

natural hydrocarbon migration. Laboratory experiments can only be used as a start point for us to understand the real processes. The extremely low value of Ca expected in the basin seems to indicate that viscous effects are negligible in comparison to capillary effects and that oil flow is entirely governed by the balance between the driving force (buoyancy) and the resisting capillary force. However, the estimated flow/expulsion rate qs is an average over a time period on the order of 1013 s (i.e. million years) and over horizontal surfaces of some 108 m2 (10 km × 10 km). Moreover, as observed in experiments, the oil flow within pathways is probably not continuous in time but likely occurs as a succession of short-term unstable bursts. Thus, in a particular area and time, the flow rate may be much larger, so that the effect of viscosity should not be completely neglected (Hantschel & Kauerauf, 2007). During such bursts, the instantaneous flow velocity reflects the competition between buoyancy and viscosity during changes at the oil– water interface along pore-scale menisci. Therefore, in specific locations and at some times, the velocity of short-term flows in pathways is relatively high, indicating the need to consider the effect of viscosity in hydrocarbon migration (Luo et al., 2004; Hantschel & Kauerauf, 2007).

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Table 2.11 Values of parameters that are appropriate for actual migration conditions and process Symbols

Variable

Unit

Value

g

Gravity acceleration

m/s2

9.81

Δρ

Density difference

kg/m3

200

μ

Surface tension

N/m

3 × 10−2

υ

Viscosity of oil

Pa·s

1 × 10−2

φ

Porosity

0.1

F

Coefficient for k

3650

2r

Pore size (diameter)

Bo

mm

0.5

0.05

0.005

Bond number

1.64 × 10−2

1.64 × 10−4

1.64 × 10−6

z

Stringer height mm

31

306

3060

ξ

Stringer width mm 3D

3.4

3.0

2.6

k

Permeability

m2

6.9 × 10−12

6.9 × 10−14

6.9 × 10−16

q

Flux from source rock

m/s

8× 10−15

8× 10−16

8× 10−15

8× 10−16

8× 10−15

8× 10−16

Ca

Ca number

9.72 × 10−11

9.72 × 10−12

9.72 × 10−11

9.72 × 10−12

9.72 × 10−11

9.72 × 10−12

V /φ

Instantaneous m/s rising velocity

1.35 × 10−5

1.35 × 10−5

1.35 × 10−7

1.35 × 10−7

1.35 × 10−9

1.35 × 10−9

Ns

Surface density of stringers

m−2

5.06 × 10−5

5.06 × 10−6

6.78 × 10−3

6.78 × 10−4

9.1 × 10−1

9.1 × 10−2

D

Distance between stringers

m

141

444

12.1

38.4

1.1

3.3

2. Flow stability and its relationship with Bo and Ca The physical characteristics of two-phase fluid flow in porous media have been intensively studied in laboratory experiments (Lenormand et al., 1988; Luo et al., 2004, 2007a). When the invading flux is large, the oil–water interface is stable; and the pathway is piston-like (Fig. 2.12d), because the viscous effects of the pressure gradients dominates (Løvoll et al., 2004, 2010; Toussaint et al., 2005). At a lower flux, viscous effects become less important, leading to an unstable interface and occurrence of gravitational fingering that produces a few (or even a single) oil fingers rising from the water (Fig. 2.12a–c). It is predicted by theory and verified by experiments that these two processes (stable and unstable displacement) are indicated by two separated

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domains in the Ca-Bo phase diagram (Tokunaga et al., 2000; Luo et al., 2004). In the case of Ca > Bo, the stable interface dominates, whereas for the case of Ca < Bo, the interface is unstable. The opposite would be true if water is pumped back into the oil, or a low-viscosity fluid displaces a high-viscous one with a minimal effect of gravity (Løvoll et al., 2010; Toussaint et al., 2005). For viscous oil displacing less viscous water, the stabilizing role of viscosity (i.e. the role of a relatively large Ca) is illustrated in Fig. 2.35, which shows the invasion of oil by point injection in a 2-D model. Keeping the other properties of the model constant, two injection rates are used. Near the point source, the pattern of invading oil at a high injection rate tends to follow a radial structure (Ca is larger near the point source; Fig. 2.35b). On the contrary, at a low injection rates (Fig. 2.35a), except near the injection point, a single narrow oil finger rises across the cell, indicating that the unstable regime dominates. Natural oil migration is characterized by an extremely small Ca (~ 10–11 ) relative to Bo (~ 10–4 ). Thus, it is reasonable to speculate that natural oil migration does indeed belong to the unstable domain. 3. Invasion percolation and width of pathway fingers An alternative to laboratory experiment to obtaining further insight to the nature of two-phase fluid flow in the case of a very small Ca number is to model the flow using the principle of a physical process called “Invasion Percolation (IP) in a Gradient” (Birovljev et al., 1991; Carruthers, 2003; Hirsch & Thompson, 1995; Meakin et al., 2000; Wilkinson, 1986; Zhou et al., 2006). This process can be thought of as an asymptotic behavior for negligible viscous effects (Ca approaching zero). Invasion percolation process is easy to simulate numerically. It has been used to study the geometry of the pathways associated with unstable two-phase flows, notwithstanding its dynamic aspects (Frette et al., 1992; Zhou et al., 2006). Nevertheless, it is a useful tool to study pathways of complex geometrical structures (Corradi et al., 2009; Hirsch & Thompson, 1995; Luo, 2011; Luo et al., 2007b; Meakin et al., 2000). A basic result of invasion percolation (IP) modeling in macroscopically homogeneous porous media (Birovljev et al., 1995; Frette et al., 1992; Zhou et al., 2006) is that gravity destabilizes the upward flow of light and non-wetting fluid (i.e., oil) invading a wetting fluid. This instability results in oil fingers of pathways with a width ξ as: ξ = ε · Bo−ν/(1+ν)

(2.13)

where ν, a critical percolation exponent, is 4/3 in 2D models and 0.88 in 3D models. This estimated width ξ is only weakly dependent on the pore size ε because it varies as εa , with a = − 0.14 and 0.064 in 2D and 3D models, respectively. The validity of this relationship was verified using numerical simulations on regular networks (Birovljev et al., 1991; Zhou et al., 2006) as well as both 2D and 3D experiments of porous media subjected to immiscible flows (Frette et al., 1992;

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Yan et al., 2012a), as illustrated in Fig. 2.36. In three of our experiments using three sizes of glass beads, the width of the rising fingers is in the order of 1 cm (Tables 2.7 and 2.9). The value of ξ estimated from Eq. (2.13) is about 5 mm for the glass bead media, similar to the observed width. The differences in finger width between the three experiments (Fig. 2.36a, e, c) are quite small, although it appears that the width decreases with an increase in bead size. These ideas can also be applied to natural migration using the 3D approach, i.e., using ν = 0.88. Using values of capillary number in Table 2.11, finger widths ξ of several millimetres are expected to occur for a wide range of pore size from few micrometre–millimetre, although this also depends on the degree of heterogeneity of the porous medium. When the medium has a broad pore size distribution, the width of fingers increases whereas, for a narrow distribution, each finger tends to be short. In other words, poorly-sorted reservoir rocks tend to develop scattered fingers of migration pathways (Zhou et al., 2006). 4. Segmented migration within pathways 2D and 3D physical experiments clearly show that fingers of a non-wetting fluid are not continuous and, in fact, divided into several disconnected segments which are repeatedly being reconnected and fragmented. This type of flows with snap-offs in rough fractures has also been studied by Schmittbuhl et al. (2000), Auradou et al., (1999, 2003). The fragmentation/segmentation appears to be an essential feature of fluid displacement (Meakin et al., 2000). If a finger does not fragment, its buoyancy force would continuously increase. Such snap-offs in porous media have been shown to affect the size distribution of the trapped, non-wetting clusters, which, in turn, affect migration (Jankov et al., 2010; Tallakstad et al., 2009a, 2009b). The fragmentation and reconnection phenomena are not accounted for by standard invasive percolation theory. Invasion percolation predicts that invasive clusters are continuously growing structures. However, the fragmentation and reconnection can be explained by advanced concepts of invasion percolation taking into account both the advance of non-wetting fluid and its retreat, as proposed by Birovljev et al. (1995), Wagner et al. (1997). This was also observed in numerical Lattice Boltzmann two-phase simulations (Aursjø et al., 2010). Roof (1970) revealed the mechanism of snap-offs when the non-wetting oil migrates in porous media saturated with water. He points out that in the migration of a continuous oil cluster, when the front of the cluster enters a sufficiently large water-saturated pore, the capillary force suddenly decreases, which allows the oil to flow into the pore rapidly and causes the imbibition of water on the oil–water interface of the continuous oil cluster. A water ring will appear in the relatively narrow throat and cuts off the oil cluster. According to Roof (1970), when the pore radius met by the continuous oil cluster is seven times of the minimum radius of throats, a snap-off and segmented migration will occur. Schowalter (1979) pointed out that the mechanical condition for snap-off of an oil pathway is that the capillary force is kept between 1/2 and 1/4 of the driving force. Wagner et al. (1997) found that when buoyancy becomes the prevailing driving force, it is conducive to the occurrence of snap-offs.

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

Physically, fragmentation occurs when and where the wetting fluid (i.e. water) is able to overcome the surface tension generated by the meniscus of the non-wetting cluster. This happens when the buoyancy exerted by a cluster is in the order of the capillary pressure exerted on a pore throat. This buoyancy threshold can be reached when the height of the cluster ζ is in the order of ε/Bo. The size distribution of the fragmented clusters can be described by the simple scaling law (Wagner et al., 1997; Vasseur et al., 2013): ζ ∼ ε/Bo

(2.14)

Examples of snap-offs and segments of migrating oil in the pathway are shown in Fig. 2.37, which shows the images at six time points in the same experiment. Once the pathway reaches the top of the experimental device, snap-offs develop, breaking the pathway into several segments, each several centimeters long (Fig. 2.37f). The relationship in Eq. 2.14 predicts a ζ value of ~2 cm (Tables 2.10, 2.2.11 and 2.12), which has the same order of magnitude as the observed. For natural oil migration under the conditions defined by the range of parameters in Table 2.11, the vertical length of oil clusters (ζ = ε/Bo) is 3 cm–3 m, which is much larger than the width of the cluster in the order of 3 mm. Thus, the cluster formed by the pores invaded by oil has an upward-rising narrow stringer shape. 5. Forming mechanisms of pathway patterns The migration phase diagram (Fig. 2.34) shows that the change of migration pathway patterns is not significantly affected by the Bond number, because a small variation in absolute permeability and porosity between each model and a large variation in Darcy velocity. With a decrease in the Capillary number, the pathway pattern changes from piston, finger, to stringer. Because the Darcy velocity is directly proportional to the amount of oil migrated in the pathway per unit time, and the latter is determined by the driving force of migration, the greater the force is, the more likely the oil will migrate in a piston pattern. Tokunaga et al. (2000) emphasized that the role of capillary force cannot be ignored. If the driving force is large, oil and gas can break through not only large pores, but also small throats at the same time. As a result, the entire oil column migrates upward and forms a piston pathway pattern. When the driving force is reduced, the possibility of oil breaking through the small throats becomes small; and oil can only migrate upward along some large throats and, thus, form a finger pattern. When buoyancy is the only driving force, the upward flow and displacement of oil in a confined glass tube must be accompanied by the downward flow of pore water. Therefore, under such conditions, it is difficult to produce a piston pattern, but finger and stringer patterns. With the decrease of original oil column height, the probability to form stringer patterns increases. Moreover, the experiments show that a small height of continuous oil cluster is favorable to form a narrow pathway, whereas a large height tends to form a wide pathway. Appling this observation to a real basin, the type of oil source may determine the dynamic conditions and pathway patterns during oil and gas migration. In an actual basin, the rate of direct supply of oil and gas from source rocks is generally

2.2 Mechanisms and Processes of Secondary Migration of Hydrocarbon

119

small, resulting in a stringer pathway pattern of migration. When accumulated oil and gas migrate again because of trap tilting or seal leakage due to faulting, the initial height of oil and gas column in the reservoir may be very large, resulting a piston or finger pattern of re-migration pathways. The finger pattern is more common in basins which have experienced multiple periods of tectonic activities. However, in an actual basin, buoyancy always exists with oil and gas, and with the progress of migration, the migration pathway will gradually return to the finger and stringer patterns. Therefore, the stringer pathway should be common and important for oil and gas migration in actual basins. This is demonstrated by the experiments shown in Fig. 2.38. The basal 2 cm of the two-dimensional plate-like model is filled with glass beads of a large size (1.5–2 mm in diameter), whereas the upper part is filled with small glass beads (0.8–1.0 mm in diameter; Table 2.10). The injected oil initially fills the coarse basal medium and, then, tends to flow everywhere into the overlying fine-grained medium at the same time. This model is similar to the situation that oil is expelled from the underlying source rock into a reservoir. Due to buoyancy, oil intrudes into overlying fine-grained medium and forms a migration pathway upward and invades more and more pores (Fig. 2.38). Although such process is difficult to be fully presented in a limited number of images with time discontinuity in Fig. 2.38, the observation during the experiment shows that the formation of the pathway takes place by oil rapidly but discontinuously invading pores one by one. A reasonable assessment of migration process in a basin is to assume that the average hydrocarbon flow is constrained by the expulsion of hydrocarbons from the source rocks (Carruthers & Ringrose, 1998). In order to build an oil cluster, it is assumed that some discrete stringers of continuous oil phase slowly emerge from the nearby source rock (Hirsch & Thompson, 1995). Once the buoyancy of the amalgamated oil cluster becomes large enough to overcome the capillary force, the cluster will form finger and develop continuously. At the beginning, Bo « 1, the buoyancy force of a single oil-filled pore is much smaller than the capillary force, which prevents the isolated, non-wetting oil in a single pore from moving. However, buoyancy of oil in connected pores is additive; and a cluster with a height ζ of many pores with a vertical height in the order of ε/Bo may overcome this capillary barrier. Once a stringer gains a sufficient buoyancy to overcome capillary force, it will migrate upward until the motion is blocked by a group of small pore throats. Once a stringer has acquired a sufficient driving force, it can grow and move upward. Eventually, a snap-off will occur; and the stringer will shrink and leave below some truncated segments and isolated droplets. This stringer generally becomes trapped behind a barrier of throats until it is fed again by a new finger arriving from below. The later stringer may collect the droplets left behind or link the truncated segments. Moreover, this new stringer, as observed in our experiments, will follow the backbone pathway of the original pathway. When lower and upper fingers merge, depending on the size of the surrounding throats, the buoyancy of the merged finger reaches the threshold to overcome the capillary force to start the upward movement again. The process of snap-offs and segmentation may follow to start another cycle of snap-off and reconnection.

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

The upward movement of the oil stringer occurs as a succession of jumps during which its geometry is modified by invasion of one or several new pores at its upper front and/or retreat of one or several pores at its base. As observed in the experiments, these jumps occur suddenly, demonstrating that the viscosity would set the limit on the speed of upward oil migration by balancing viscous and buoyancy forces when the capillary force is reduced (or even accelerate the motion). This statement is now used to evaluate an upper limit of the instantaneous velocity of a stringer. An immobile stringer of a height ζ can be viewed as a bubble in dynamic equilibrium between its buoyancy force Δρgζ and the capillary force at its interface with the wetting fluid (g/ε). When the stringer is fed by new oil, its buoyancy increases and eventually overcomes the capillary force, resulting in upward movement. Once the upward motion has started, the capillary forces on all sides of the bubble alternate in direction, either pushing or pulling it. During this upward motion it is reasonable to assume that the net effect of capillary forces, averaged in time and space, is about zero, so that the upward velocity V of the stringer is limited only by the viscous force. This velocity can be estimated as a function of the pressure gradient associated with gravity, using the standard, single phase Darcy law: V = (k/μ)Δρg

(2.15)

The estimate is the upper limit of the velocity of the stringer. The application of this equation to 2D experiments is illustrated in Fig. 2.30 (see also Table 2.10). The observed velocity of the upper tip of the stringer is 1.5 × 10–4 m/s, whereas the estimated maximum velocity using Eq. 2.15 is 1.73 × 10–3 m/s. The application of Eq. (2.5) for natural migration is presented inn Table 2.11. The maximum instantaneous velocity largely depends on the pore size (via permeability). For a large pore size ε of 0.5 mm, the velocity is in the order of 500 m/year, whereas for small ε of 0.005 mm, the velocity decreases to 5 cm/year. If a large pressure gradient can be set up by oil injection, a similar effect on migration can be achieved. In this case, the maximum segment formed by snap-off is ε/Ca; and the mobile clusters are those with a size comparable to the maximum characteristic value (Aursjø et al., 2010; Tallakstad et al., 2009a, 2009b). The supply source in this case is not the expelled hydrocarbon from source rocks, but the spillover of accumulated oil in a trap. 6. Spatial density of migration pathways Hantschel and Kauerauf (2007) pointed out that secondary migration pathways overall resemble a percolation of stringers. For the range of parameters in our experiments (Table 2.11), the stringers can be characterized by a horizontal size ξ of a few mm and a vertical height ζ of 3 cm–3 m. The stringers are subject to snap-off and reconnection and are moving upward in steps and sweeping left-over droplets. The spatial density of this type of stringers is a fundamental attribute in migration and is tackled by using a simple assumption on the dynamics of individual stringers.

2.3 Numerical Simulations of Secondary Hydrocarbon Migration

121

In general, the formation of pathways during vertical oil migration is independent of each other (Hirsch & Thompson, 1995). An estimate of the instantaneous migration velocity of a stringer invading new pores can be derived from Eq. (2.15). Assuming that this velocity is necessary for oil migration to keep pace with the expulsion rate qs of oil from source rocks, the velocity has to be in the range from 8 × 10–15 to 8 × 10–16 m/s (England, 1994). Thus, the minimum number of such stringers per unit area (i.e., the density of stringers) can be expressed as: N = qs /(Vi · ξ 2 )

(2.16)

and the average distance D between stringers is, for the 3D case, ~ N −0.5 (Hirsch & Thompson, 1995). Applying this equation to natural migration, the values depend on the expulsion rate of oil from source rocks (Table 2.6). The upward velocity of stringers and their spatial density are controlled by two factors, the pore size ε and the expulsion rate qs . The distance D between pathway stringers ranges from about one meter for a small pore size to several tens of meters for large pore size. Of course, the estimated distance D is an upper limit because it is implicitly assumed that the instantaneous stringer velocity applies for the entire duration of migration and that the upward movement of the stringers is only limited by the expulsion rate of hydrocarbons from source rocks. The pathways are sparsely distributed as indicated by the small ratios of ξ /D. Because the migration along existing pathways basically does not change the shape of pathways, in the actual basin, such pathway spacing is adequate to support largescale oil and gas migration, even though the spacing is difficult to be observed through drilling. This also implies that the efficiency of vertical oil and gas migration may be very high. This understanding provides the theoretical base for estimating the amount of hydrocarbon loss in the process of migration from a new perspective (Luo et al., 2007a, 2007b, 2007c).

2.3 Numerical Simulations of Secondary Hydrocarbon Migration Physical experiment is an effective means to study oil and gas migration, but has some major limitations. The duration of the experiment is limited; and the cost may be too much. The model size is far too small from the actual size of the migration conduits, so that it is usually difficult to simulate the migration processes in the complex conduits. Therefore, it is imperative to study the migration process by using the mathematical modeling method, which is the future direction of oil and gas migration and basin research (Luo, 2011; Shi, 2009; Welte et al., 2000). With the development of computer technology, the progress of numerical calculation methods and the improved understanding of multiphase fluid displacement mechanisms in

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2 Mechanisms and Processes of Secondary Migration of Oil and Gas

porous media, the mathematical simulation method in oil and gas migration research becomes more widely applied. In the last two sections, three patterns of oil migration pathways are discussed through physical experiments. Under actual basin conditions, when buoyancy plays a leading role in secondary oil and gas migration, the migration pathways appear mostly as stringer pattern. Thus, we developed a mathematical model, MigMOD, of oil and gas migration based on the principle of invasion percolation (Luo, 2011; Luo et al., 2007a; Zhao & Luo, 2003), to simulate quantitatively the formation of migration pathways at the basin scale. This section introduces the principle and method of the numerical model, analyzes its applicability to simulate oil and gas migration process, and extends the scope and scale of the mathematical model using the principle of dynamic similarity (Luo et al., 2007a, 2007b; Zhang et al., 2007; Luo, 2011). Finally, the heterogeneity of migration pathways and its influencing factors are discussed; and the migration pathways in typical hydrocarbon migration and accumulation are simulated and analyzed.

2.3.1 MigMOD and Its Applicability 1. Fundamentals and principles of migration simulation The main points of our understanding of the mechanisms of oil and gas migration, as discussed in detail in the last two sections, are summarized below. In addition, the theoretical basis of invasion percolation model and its adaptability to migration simulation will also be discussed. First, in conventional oil and gas migration and accumulation, oil and gas migrate mostly in separate phases in various types of conductive bodies. The main driving force of migration is buoyancy; and the main resistance force is the capillary force of the pore throats of conductive rocks. For regions with intense hydrodynamic activities, the migration dynamics is dictated by the hydraulic potential (Hubbert, 1953). However, only buoyancy is considered in most simulations in this section. Second, oil and gas migration is commonly a discontinuous process (Carruthers, 2003; Luo et al., 2004). This is caused by many factors: (1) snap-off and segmented migration (Carruthers, 2003; Meakin et al., 2000), (2) temporary accumulation of oil and gas in local small traps, and (3) discontinuity in hydrocarbon expulsion from source rocks (Chapman, 1982; Luo et al., 2004). These factors greatly reduce the migration speed, even though the migration speed of a single oil segment in porous media is quite fast (Catalan et al., 1992; Luo et al., 2004). Third, snap-off and segmented migration are common during migration in porous media that are essentially micro-heterogeneous (Carruthers, 2003). This has several consequences. First, the migrating oil and gas are always searching for a breakthrough throat where the difference between the driving force and capillary force is the largest among all the throats at the interface between the pathway and the surrounding water. Second, if the driving force acting on a continuous oil segment is insufficient to push

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123

the oil to break through at any throat on the interface, the migration of this segment will stop until the segment is joined by a segment from below so that the buoyancy of the combined segment is enough to push the oil to break through at a throat. The minimum height of moveable oil segment required is H limit = H lh + δP/ρg, where H lh is the height of the oil segment and δP the fluid pressure gradient in a conduit. For a conduit where δP = 0, that is, buoyancy is the only driving force, H limit can be estimated from Eq. (2.14). Fourth, if the supply of oil and gas is limited, the amount of movable oil will decrease during the growth of pathways. As a result, the length of a pathway will be limited. The length can be estimated at a microscopic scale (Hirtch & Thompson, 1996; Luo et al., 2007a, 2007b, 2007c), but not at a basin scale. Even if the characteristics of a pathway can remain unchanged at the micro and macroscopic scale, the geometric variations of the top surface of a carrier bed and the heterogeneity of lithology may cause temporary accumulations of oil and gas in small traps along the pathway. The amount of such accumulations may be much greater than the loss of oil and gas along the pathways (Bjølykke, 1996; Ringrose & Corbett, 1994). Finally, the physical experimental results show that when oil is injected into a water-saturated porous medium from the top of a trap, the largest oil saturation can only reach to ~ 80% of the pore space (Tables 2.23, 2.24, and 2.25). With such saturation, the driving forces, if present, can cause both oil and water to flow (Catalan et al., 1992; Tokunaga et al., 2000; Luo et al., 2004). The reason is that under the condition of the highest oil saturation, the water in an oil-bearing reservoir still maintains a certain degree of continuity. Therefore, as long as the velocity of fluid displacement is slow enough, water and oil can flow freely. 2. Realization of the simulation model MigMOD The MigMOD model is a numerical simulator based on the percolation concept. The simulation process is implemented according to the percolation theory. In an invasion percolation model (Wilkinson & Willemsen, 1983), the initial network nodes are occupied by the wetting phase water. Then, the non-wetting phase oil occupies the designated nodes. Later, oil expands from the original nodes to the adjacent ones. With the increase of injected oil, the oil cluster expands continuously. When the cluster passes through the network and reaches the target boundary, the migration process is completed. The MigMOD model uses a quadrilateral network for 2D modeling and a hexahedral network for 3D modeling. Nodes in the network represent pores; and bonds between nodes represent intervening throats. The pores are assumed to be spherical spaces; and the bonds to be tubular with a circular cross-section. The diameters of the pores and throats are given random values following specific probability distributions (Zhao & Luo, 2003). In general, a network can be characterized by relatively few parameters, including the average radii of pores and throats, their variance, and the number of nodes and/or bonds in the network. In our model, fluids are regarded as incompressible and immiscible. A pore can contain only one fluid phase at any time. Initially, both the pores and throats are

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saturated with water. To match different migration conditions, oil can enter the model in several ways, for example, from a single point, multiple points, a restricted area, or random points. When oil enters the model, a throat, which connects an oil-saturated pore with a water-saturated pore, will experience a capillary pressure in the direction of the oil-saturated pore and a buoyancy force in the opposite direction. The driving condition defined in the model is P = Pb − Pc . The capillary pressure (Pc ) depends on the pore-throat radius and the fluid properties: Pc =

2γ cos θ r

(2.17)

where γ is the interfacial tension between oil and water; r the radius of the throat; and θ the contact angle of the oil–water interface with respect to the surface of the capillary throat. The buoyancy force (Pb ) depends on the relative height, h, and the density difference between the two fluid phases, Δρ: Pb = P0 + Δρgh i

(2.18)

where P0 is the initial fluid pressure at hi = 0, which can be regarded as the injecting pressure associated with the initial oil column; and g the gravitational acceleration. Only when ΔP is greater than 0 at the water–oil interface in a throat, oil may flow into a water-saturated pore and occupy it. The oil will not return to the pore it came from. In 2D conditions, when some water-saturated networks are surrounded by oil, the replacement of water by oil may still occur, as in the 3D networks. Our physical experiments (Luo et al., 2004) show that if buoyancy is the only driving force, oil and gas tend to migrate in a stringer pattern, in which the migration pathway appears as a single, very narrow filament which tends to fragment and snap off (Meakin et al., 2000; Zhang et al., 2003). To simulate such migration behavior, the migrating oil in the system will, at each step, only break through at a single break-boundary throat at which ΔP is at a maximum value in comparison to all the other break-boundary throats with ΔP > 0. The oil and gas migration and accumulation model is coded with the VC language. The simulation flow chart is shown in Fig. 2.39. The parameters involved include mean pore and throat radii and their variances, oil and water densities, interfacial tension, wettability, initial oil column height, and grid size. At the beginning, an initial oil column height is given and each node of the grid is filled with pore water. During the simulation, the state at the nodes is constantly updated in the process of migration. After the model is established, all the grids are used to represent a stratum. The part of the stratum, where permeability is high and oil and gas can migrate, is called a conduit. The part that contains clusters of nodes and lines occupied by oil and gas is called a migration pathway. Figure 2.40 shows the oil migration pathway characteristics in a 2D and a 3D model simulated using the MigMOD model. Figure 2.40a shows a vertical rectangular 2D model; and Fig. 2.40b a columnar 3D model with square top and bottom faces.

2.3 Numerical Simulations of Secondary Hydrocarbon Migration

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Fig. 2.39 Flow chart of the buoyancy—percolation migration model

All grids in the model represent the combination of pores and throats with a high permeability and are the migration conduit. The clusters consisting of nodes and bonds occupied by oil and gas in the conduit constitute the migration pathways. Under the buoyancy force, oil and gas enter the model from the bottom or bottom surface and migrate upward to form a series of finger-like pathways, which compete with each other. Only a few pathways can eventually reach the top of the model. 3. Applicability of the simulation model Numerically-simulated results should be similar to those of physical experiments under the same conditions (Hirsch & Thompson, 1995; Meakin et al., 2000). The aforementioned model (Fig. 2.40) does not consider the time factor during two-phase fluid displacement and, thus, is only descriptive. To realize the similarity between physical and numerical experiments, the number of breakthrough points needs to be adjusted and/or some parameters of the mathematical models must be calibrated. A plate model is constructed with a basal layer of all connected pores occupied by oil and using parameters corresponding to those of the physical experiments. The distribution of the number of breakthrough pore throats during each iteration can be determined by changing the injection pressure on the oil in the basal layer. The model results can be compared with the result of physical experiments (Fig. 2.41). Oil migration pathways in some physical experiments display the characteristics of fractal distributions (Lenormand et al., 1988). So the fractal distributions have been used in some studies to compare physical and numerical experimental results

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Fig. 2.40 Images showing the results of simulation of oil migration pathways in porous media with the MigMOD model in a 2D (a) and 3D (b) model

Fig. 2.41 Images of physical and numerical models of secondary hydrocarbon migration. a Results of physical experiment with 2D model (Fig. 2.20). b–e Results of numerical simulation using a number of breakthrough points ranging from 5, 10, 15, and 20, respectively

(Hirsch & Thompson, 1995; Wagner et al., 1997; Wilkinson, 1986). Fractal analysis of the image in Fig. 2.41a indicates a fractal dimension of 1.28, and those of the images in Figs. 2.41b–e have fractal dimensions of 1.28, 1.27, 1.28, and 1.29. To delineate the dissimilarities among the images, a ratio between the number of pixels of the migration pathways and that of the entire model/plate is defined to calculate the percentage of pathway area to entire area of the model. The ratio is termed as model oil saturation (S o ). The model oil saturations for models with variable density difference between oil and water (Fig. 2.41a–e) are 20.42%, 12.42%,

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Fig. 2.42 Images showing pathways generated in simulation models with variable water–oil density differences. a Water–oil density difference (Δρ) of 0.1 kg/m3 , model oil saturation (S o ) = 26.11, fractal dimension (D) = 1.32; b 0.3 kg/m3 , S o = 11.85, D = 1.23; c 0.5 kg/m3 , S o = 8.62, D = 1.24; d 0.7 kg/m3 , S o = 8.25, D = 1.20; e 0.9 kg/m3 , S o = 8.25, D = 1.22

15.42%, 20.18, and 30.42%, indicating that images a and d are quite similar to each other, whereas images b, c, and d are quite different from image a. Furthermore, the effect of a variety of factors such as grain surface wettability, fluid density, viscosity, surface tension, and model tilting angle on migration pathways is assessed through modeling. The results are similar to those of the physical experiments. Figure 2.42 shows model results of migration pathways in a rectangular carrier with variable water–oil density differences.

2.3.2 Simulation Using Conceptual Geological Models The objective of constructing the MigMOD simulator is to analyze semiquantitatively the characteristics and processes of hydrocarbon migration, and even accumulation, at a basin scale. Thus, upscaling of the simulation results is necessary. This can be accomplished by setting up a network consisting of a small number of grids. Such upscaling must maintain the hydrodynamical similarity between models and actual geological models. 1. Upscaling Migmod model uses the principle of renormalization group to describe the carrier space and hydrocarbon migration pathways represented by the model network at different scales (Luo, 2011; Luo et al., 2007a). Based on the upscaling method used in reservoir characterization (Arbogast & Bryant, 2000; Bryant, 1998), parameters, such as displacement pressure in rocks, are used to replace throat capillary force at

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a basin scale; and the values of the parameters are randomly assigned on the nodes and bonds in the grid representing migration carriers. Generally, the upscalling of hydraulic properties regards reservoirs as statistically homogeneous. This requires the values of average, or effective, properties in most fluid dynamical models based on Darcy’s law. The effective values are obtained from lower-scale networks as arithmetic or exponential averages (Durlofsky, 1991; Kruel & Noetinger, 1994; Warren & Price, 1961). In percolation models, however, the permeability of the system changes qualitatively when the occupying probability of nodes increases to the threshold value, and the correlative length of the system tends to be infinite. This indicates that upscaling may be realized easily in the percolation model using the method of renormalization group (King, 1989; Sun et al., 2003). Figure 2.43 illustrates upscaling using the theory of renormalization group. From diagrams (a)–(b), the size of the network has doubled. In (a), a unit of network composed of 9 nodes is regarded as an element. If the nodes occupied by oil in an element are connected to each other and extend across the element, this entire element is considered as being occupied by oil, and visa verse. In Fig. 2.43b, the original element with 9 nodes is coarsened/upscaled to a grid with 4 nodes as a new element. The new element is a quarter of the original element. If the original element is occupied by oil, the bottom-right node of new element will be occupied; otherwise, the new element is considered empty and unoccupied. In case that an original element and its neighbors are all occupied but its corresponding new element does not have oil nodes extend cross, an occupied node has to be inserted to force the new element to be occupied. The procedure is repeated. As a result, the original migration pathway in (a) is upscaled to a new pathway in (b), and eventually to (c), to (d). To simulate oil migration in porous medium, a network is designed at a small scale and in the same size as that of a physical model. A node corresponds to a pore and a link to a throat. At a larger scale, the nodes and lines in the model grid represent the characteristic pores and throats in a specific spatial unit. The parameters reflecting the porous space and permeable capacity, like porosity, permeability, or expulsion

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Fig. 2.43 Diagram illustrating the principle and procedures of upscaling using the theory of renormalization group

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pressure, are used to characterize the conduit. These parameters can be conveniently measured in the laboratory. 2. Migration simulation of a typical source-reservoir-anticline- seal model A typical carrier-fold assemblage is used to simulate the migration processes. It consists of source rock, carrier/reservoir and seal, where the reservoir serves also as carrier (Fig. 2.44). The hydrocarbon expelled from the lower source rock enters the basal carrier bed and then migrates upward. When the vertically migrating hydrocarbon reaches the top of the reservoir that is overlain by a seal with a large capillary pressure, oil migrates laterally toward the up-slope direction and finally accumulates in the trap (England et al., 1987). The cross-section of the assemblage Fig. 2.44) is 5000 m long; the dip angle of the left limb of the anticline is 20°. The carrier is a macroscopically homogeneous porous medium and initially is saturated by water. So the grains in the carrier are water-wet. The average displacement pressure of the seal is two order of magnitude greater than that of the carrier bed. A series of injection points are set at the interface between the source rock and carrier bed. A specific amount of oil is injected from the lowest expulsion point into the carrier bed (Fig. 2.44a). The episodically expelled oil from the source rock enters at the bottom of the carrier and accumulates gradually at the bottom. Once a column grows high enough to move upward, secondary migration begins. The growing migration pathways extend up vertically through the carrier bed. When oil

Fig. 2.44 Images showing simulation results of oil migration in a source-carrier-anticline assemblage from the initial to final stages (a–f). Elements of the assemblage are shown in (a). The color scheme of relative oil flux in pathways is shown in (f)

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meets the overlying seal, it migrates laterally along the up-dip direction beneath the seal until it reaches the trap (Fig. 2.44a, b), and finally accumulates there (Fig. 2.44c). If the oil discharged from other points of the source rock encounters existing pathways during its migration, it will use these pathways instead of opening up a new one (Fig. 2.44d–f). To illustrate the varying amount of migrating hydrocarbon along pathways, the MIGMod model couples the driving and resisting force, properties of carrier bed, and rate of hydrocarbon supply during continuous migration. This is accomplished by assigning specific hydrocarbon expulsion rates and amounts at the source sites and registering and color coding the amount of migrated hydrocarbon crossing each grid node in the carrier bed. The heterogeneity of migration is manifested by the varying width of pathways and varying flux of hydrocarbons within the pathways (Fig. 2.44). 3. Simulation of hydrocarbon migration in a conceptual basin model The dominant pathways form not only during vertical migration, but also during lateral migration. In the case where groundwater circulation can be neglected, the driving force is buoyancy, so that the migration pathways are mainly controlled by the geometry of the carrier beds (Allen & Allen, 1990; Hindle, 1997; Hao et al., 2007, 2009). Once oil is expelled from source rock into the carrier bed, it migrates vertically upward under the action of buoyancy until reaches the seal. The seal forces the oil to migrate updip along the upper part of the carrier bed. If the trap is far away from the source area, the migration distance would be long. The amount of hydrocarbon loss in the migration pathways determines the amount of oil and gas accumulation in the reservoir in the trap. Many scholars believe that oil and gas tend to migrate in the area with dense isolines of fluid potential isolines, and structural highs over the source area are the places most likely to form dominant pathways (Allen & Allen, 1990; Hindle, 1997). Using two conceptual models of migration and accumulation in foreland and synclinal basins proposed by Allen and Allen (1990), we constructed 2D carrier bed models (Fig. 2.45). The MigMOD model is used to simulate the characteristics of oil migration pathways. In the foreland basin model, the subsidence center is located on the left side; and two structural ridges extend into the depression from the slope to the right (Fig. 2.45a). The synclinal model has a simple geometry with steep slopes on both sides of a central trough (Fig. 2.45b). In order to make the simulation suitable for the realistic geological conditions, with the consideration of the constraints on calculation capacity and machine time constraints, our model adopts 250,000 element grids, assumes a normal distribution of pore throat size, and ignores the hydrodynamic effect. In the subsidence center, oil and gas migrated laterally under the caprock after migrating vertically from bottom to top in the carrier bed. The driving force of migration is buoyancy caused by structural relief and density difference between oil and water. The driving force is converted to oil–gas flow potential in the model. The simulation results of the foreland basin model show that (Fig. 2.45a) the migration pathways form mainly along the two structural ridges extending into the

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Fig. 2.45 Images showing the MigMOD simulation results of oil and gas migration pathways for the foreland basin model (a) and oval-shaped synclinal basin model (b). The color bar is the scale for relative hydrocarbon flux in pathways. Isoclines of fluid potential and flow lines are from Allen and Allen (1990)

source kitchen. The pathways in the center grow toward the updip in all directions, most of which tend to converge toward the structural ridges and merge into a main pathway. The migration of oil and gas in the elliptic syncline (Fig. 2.45b) follows a main pathways in the updip directions perpendicular to the syncline axis. The migration on the two limbs of the syncline is not controlled by the elongate source kitchen, but by the steep gradients of the fluid potential field related to the structural relief. Allen and Allen (1990) originally intended to use these two basin models to represent two different types of hydrocarbon migration patterns—the pathways converging to the structure ridges in the foreland basin model and diverging from the source kitchen in the syncline model. The hydrocarbon loss in the foreland basin is less than that in the syncline, unless some conditions in the syncline (such as heterogeneity of carrier bed) make the migration pathways merge. In the macroscopically homogeneous carrier bed models, the simulation results basically match the migration directions controlled by the fluid potential (Fig. 2.36). The pathways are mainly distributed on both limbs of the elongate synclinal source area and structural nose extending into the source area. However, the pathways are not parallel to each other as the flowlines governed by fluid potential, and merge with each frequently. The distribution of pathways in the syncline basin model is not simply dispersed, but overall divergent and locally convergent. Finally, in the dominant migration directions, migration pathways are concentrated and the oil and gas flux in the pathways is relatively high (Fig. 2.45). As a consequence, prolific oil and gas reservoirs can be identified by following these main migration pathways.

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2.3.3 Heterogeneity of Hydrocarbon Migration Pathways and Influencing Factors The migration direction and pathway characteristics are highly heterogeneous as determined by the driving and resistance forces of migration (England & Muggoridge, 1995). The migration in basins is commonly driven by buoyancy. The capillary force generally resists again migration. In this section, results of some previous studies on the formation and amalgamation of migration pathways (Hou et al., 2005; Luo et al., 2004, 2007a, 2011; Zhang et al., 2003) are summarized. And the heterogeneity of migration pathways at three levels from simple to complex will be simulated and analyzed using the MigMOD model, to explore the geological factors affecting the heterogeneity of oil and gas migration. For most simulation cases presented in this subsection, the density difference between oil and water is assumed as 0.2 g/cm3 , surface tension between oil and water is 40 dyne/cm, and the gravitational acceleration is 9.80 m/s2 . The variation of Bond number is realized by assigning different average radii of throats. The average radii of pore throats are assigned as 2.0, 0.63, 0.20, 0.063, 0.02, and 0.0063 mm, which correspond to Bond numbers 10–1 , 10–2 , 10–3 , 10–4 , 10–5 , and 10–6 , respectively. 1. Non-uniform migration in macroscopically homogeneous carrier beds A porous medium is statistically defined as macroscopically homogeneous but microscopically heterogeneous if the radius of pore throats follows a specific probability distribution. A uniform flow-potential field is characterized by parallel and equally spaced equipotential lines. Fluid dynamics analysis of a model through simulation on the basis of hydraulic potential or Darcy’s Law shows that flow pathways are perpendicular to equipotential lines (Allen & Allen, 1990; Hindle, 1997). An inclined plate is defined as z = Cy, where y and z are the coordinates in the dip and vertical directions, respectively; C is the dip angle, which determines the magnitude of buoyancy force (Fig. 2.46a). The plate is covered by a 500 × 500 grid; and oil is injected along a zone of 20 × 500 sites at the base of the plate and migrates upward driven by the buoyancy force. The spacing of injecting points is one inject point every 10 × 10 sites. Different pathways form, corresponding to different Bond numbers (Fig. 2.46b–f). The results of simulation indicate that the ratio between driving and resisting forces controls the pathway geometry in the porous medium of a given pore throat size distribution. In the case of a relatively large driving force (i.e., a large ratio between driving and resisting forces; Bo = 10–1 ; Fig. 2.46b), pathways are generally perpendicular to the equipotential lines; and pathways originated from individual injection points are separated. Migration appears to have an overall homogeneous pattern. With a decreasing driving force (Fig. 2.46c), however, pathways become irregular; and pathways originated from different injection points merge to form a rootlike network with a general direction perpendicular to the equipotential lines. Nevertheless, all pathways appear evenly distributed on the plate. With a further decrease in the driving force (Fig. 2.46d), pathways become more complex; their

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Fig. 2.46 Images showing results of simulation of hydrocarbon migration pathways in an inclined plate composed of a macroscopically homogeneous medium in a uniform flow-potential field. a The inclined plate with a dip angle 5.72°. Dots along the base line show the position of hydrocarbon injection points. The color bar scale is the relative flux of migrated oil in pathways for simulation results in panels b–f. b–f Results of simulated pathways with Bond numbers 1.0 × 10–1 , 1.0 × 10–2 , 1.0 × 10–3 , 1.0 × 10–4 , and 1.0 × 10–6 , respectively. Numbers along the horizontal and vertical axes are grid coordinates. Oil is injected along a zone of 20 × 500 sites at the base of the plate. The spacing of the injecting zone is one inject point every 10 × 10 sites

average width increases; and pathways from different injection points merge extensively, resulting in only a few of the pathways reaching the top of the plate. The overall migration direction is perpendicular to the equipotential lines, but locally, migration direction deviates from the direction of the driving force. The amount of migrating hydrocarbon varies among pathways. Finally, when the driving force is very weak (Fig. 2.46e, f), pathways originating from individual injection points merge rapidly. As a result, the number of pathways is small; and pathways form patchy networks and are not perpendicular to equipotential lines. 2. Non-uniform migration in structurally undulating and macroscopically homogeneous carrier beds The upper boundary of carrier beds is commonly not a planar slope in sedimentary basins, but tectonically deformed to form an undulating surface. Thus, we simulated the distribution characteristics of migration pathways in fluid potential in simple anticline and syncline models (Fig. 2.47a–f). Here, except the change of the shape of the top surface of the carrier bed, other conditions are the same as those in the model as shown in Fig. 2.46.

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Fig. 2.47 Images showing the migration pathways formed by hydrocarbon migration axially along an inclined anticline (a–c) and syncline (d–f) in a macroscopically homogeneous carrier bed. The Bond number characterizing the driving force and resistance forces is Bo ≈ 4.0 × 10–5 , so the results can be compared with those in Fig. 2.46e. The amplitudes of folds (height/width of the arches on cylinder section) are 0.09 (a, d), 0.12 (b, e) and 0.15 (c, f). The color scale for oil flux in the pathways is the same as that in Fig. 2.46a

Using Fig. 2.46d as a reference, the migration pathways in an anticline tend to be concentrated toward the axis of the anticline (Fig. 2.47a–c), and the greater the amplitude of the anticline is, the faster the migration pathways are concentrated. On the contrary, the migration pathways in a syncline tends to diverge toward the two limbs. The larger the amplitude of the syncline is, the faster the migration pathways diverge toward the two limbs. However, due to the random changes of migration pathways, the convergence of the pathways toward the axis of anticline and the divergence toward the limbs of syncline are not the same as that in the plate model (Fig. 2.46). When the amplitude of the folds changes, the pathways may change. But when the amplitude difference is small, the pathways are similar, as indicated by a comparison between Fig. 2.47d, e, f. If the amplitude difference is large, the pathways become completely different, as shown by a comparison between Fig. 2.47a, c, d, f. Furthermore, another homogeneous carrier bed model with structural undulation is established (Fig. 2.48a). The carrier bed covers a pair of adjacent syncline and anticline. An area of hydrocarbon expulsion is set in the center of the syncline (Fig. 2.48b–f). The uneven fluid potential field associated with the adjoining syncline and anticline causes complex pathway morphology (Fig. 2.48b–e). When the driving force is large (Fig. 2.48b; Bo ≈ 4.0 × 10–3 ), the pathways are perpendicular to the fluid

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Fig. 2.48 Images showing the model and results of simulation of pathways in a macroscopically homogeneous carrier bed in a pair of adjoining syncline and anticline, where only buoyancy is the only driving force. a Structural configuration of the adjoining syncline (lower left) and anticline (upper right). The color bar scale is the relative flux of migrated oil in pathways. b–f Simulation results with the meandering pathways corresponding to Bond numbers 1.0 × 10–3 , 1.0 × 10–4 , 1.0 × 10–5 , and 1.0 × 10–6 , respectively. The number on the contour line represents the relative relief. The dotted green contour line at the bottom of the syncline is the expulsion area

potential isolines. Oil and gas migrate from each expulsion point independently. The pathways are approximately uniformly distributed. With the weakening of driving force, the pathways become thicker and more tortuous (Fig. 2.48c; Bo ≈ 4.0 × 10–4 ); and the apex of the anticline direction is no longer the only target of migration. When the driving force further weakens, the topographic variation of the carrier bed basically does not affect the growing direction of migration pathways, which form migration clusters around the hydrocarbon expulsion range (Fig. 2.48d, e; Bo ≈ 4.0 × 10–5 , Bo ≈ 4.0 × 10–6 , respectively). 3. Migration pathways in heterogeneous carrier beds Carrier beds are inherently heterogeneous (Weber, 1986). The heterogeneous physical properties of carrier beds control significantly the selection of breakthrough points by migrating hydrocarbons (Bekele et al., 1999). A simple model is designed to simulate the migration in macroscopically heterogeneous carrier bed. The carrier bed forms a hyperbolic syncline (Fig. 2.49a). The permeability of the carrier bed differs significantly between the left and right limbs different (Fig. 2.49b). The bed on the left side has an average throat radius twice larger than that on the right side. The hydrocarbon expulsion area is in the central

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Fig. 2.49 Model and simulation results showing the effect of a heterogeneous carrier bed on hydrocarbon migration pathways. a Geometry of the upper surface of a carrier bed in a structural basin/syncline. The color bar scale shows the flux of hydrocarbon migration along pathways. b Two parts of the carrier bed with different physical properties characterized by the Bond number. The green dotted circle in the center is the area of hydrocarbon expulsion. The Bond number of the left part of the carrier bed is Bo ≈ 4.0 × 10–3 (c), Bo ≈ 4.0 × 10–4 (d), Bo ≈ 4.0 × 10–5 (e), and Bo ≈ 4.0 × 10–6 (f)

bottom of the syncline (Fig. 2.49b). The migration pathways form under variable Bond numbers (Fig. 2.49c–e). The simulation indicates that when the Bond number is large, the effect of buoyancy is dominant; the influence of the heterogeneity of the carrier bed is very small; the pathways form equally in all directions and are very narrow (Fig. 2.49c). With a decrease in the Bond number, the influence of the heterogeneity begins to appear, as indicated by the slightly thicker pathways and the shift of pathways to the left part of the carrier where the permeability is higher than that in the right part. When the Bond number decreases further, the pathway pattern becomes increasingly wide, the number of pathways decreases sharply, and the number of pathways and the amount of oil migrating through the pathways are obviously skewed to the left, reflecting the effect of heterogeneity (Fig. 2.49e). When the Bond number decreases to 4.0 × 10–6 , the influence of the heterogeneity of the carrier bed is significant. The pathways in the right part are very wide; and the area of distribution is limited. Most of the oil and gas migrate to the left (Fig. 2.49f). In the next model, the carrier bed in the hyperbolic syncline is subdivided into eight octants (Fig. 2.50a). Each octant consists of one of three types of carriers. The average pore-throat radii of the three types of carriers are 0.63, 0.2, and 0.063 mm

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with a variance of 0.31, 0.1, and 0.031 mm, respectively. These parameters correspond to Bo values of 10–2 , 10–3 , and 10–4 . When the buoyancy force dominates, the impact of carrier bed heterogeneity on pathway formation is minimal. The pathways extend in all directions with equal chances and are narrow (Fig. 2.50a). However, the pathways in three types of carriers differ significantly. In Type I carriers, pathways are narrow, long, and numerous. In Type II carriers, pathways are wide and merging with each other. In Type III carriers, migration is sluggish, forming only one or two short pathways. Considering the effect of expulsion intensity on migration pathways (Fig. 2.50a), the poor pathway development in Type III carriers may be caused by the preferential migration in types I and II carriers with larger pore throats and, thus, larger Bond numbers. The competition among different types of carriers intensifies with increasing Bond numbers. Migration is limited in Type III carriers within the source area; oil is

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Fig. 2.50 Images showing the results of simulation of pathways in a structural basin/syncline composed of macroscopically heterogeneous carriers in a uniform flow-potential field. Configuration of the syncline is the same as that in Fig. 2.49a. The color bar scale in c is the relative flux of migrated oil in pathways; and the dashed circle in the center outlines the source area. The carriers have three types and have an average pore-throat radius of 0.63, 0.2, and 0.063 mm, with a variance of 0.31, 0.1, and 0.031 mm, respectively. a–c The three types of carriers alternate with each other, and four Type III carriers separate types I and II carriers. d–f The three types of carriers alternate with each other; and types I, II, and III carriers are arranged counterclockwise so that a Type I octant will be adjacent to a Type II octant at one side and to a Type III quadrant at the other side. Oil is injected uniformly from the source area and migrates outward. The Bond number decreases 10 times in panels a and d and increases 10 times in panels c and f. Numbers along the horizontal and vertical axes are grid coordinates on the XY plane, and the Z coordinate is not shown. Thin light green circles are topographic contour lines

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concentrated in a few pathways in types I and II carriers (Fig. 2.50b). In the extreme case where the resisting force dominates, most migration occurs along pathways in types I and II carriers (Fig. 2.50c). Furthermore, pathways in types I and II carriers have similar characteristics when they are separated by a Type III carrier (Fig. 2.50a– c). The likely cause is that type I or II carriers are always preferential migration media in comparison with adjacent Type III carriers. However, when types I and II carriers are adjacent to each other at one side and to a Type III carrier at the other side (Fig. 2.50d–f), competition between types I and II carriers for preferential migration within the source area occurs. Type I carriers win the competition, as shown by the abundant pathways in Type I carriers and limited pathways in Type II carriers (Fig. 2.50d–f). As the area of Type III carriers increases in the basin, migration becomes more important in Type III carriers (Fig. 2.50d, f). To further assess the influence of heterogeneous carrier bed on the migration process, another heterogeneous carrier bed model is constructed (Fig. 2.51). The carrier bed is composed of porous media with specific permeability (Fig. 2.51b) in a pair of adjoining syncline-anticline (Fig. 2.48a). In the case of (b), the carrier bed is composed of macroscopically homogeneous porous media with an average porethroat radius of 0.2 mm and a variance of 0.1 mm, corresponding to a Bond number of 10–3 . Next, a permeable meandering channel sandstone is superimposed on the carrier bed and through the center of the syncline and crosses the top of the anticline (Fig. 2.51c–f). The area of hydrocarbon expulsion is in the center of syncline, as indicated by the dotted circle in the left half of Fig. 2.51b–f. Figure 2.51c–f show the migration simulation results in the cases with different bond numbers. Migration pathways are controlled by the meandering carrier bed (Fig. 2.51c–f). Hydrocarbons which entered initially into the meandering belt can only migrate within the belt. Pathways in the low-permeability carrier bed, once intercepting the meandering belt, also enter the meandering belt. In the case of large Bond numbers (Fig. 2.51c), pathways are narrow as controlled dominantly by the buoyancy force, and abundant outside the meandering belt, indicating a weak control of carrier bed heterogeneity. Nevertheless, in comparison with the simulation result with a homogeneous carrier bed (Fig. 2.51b), pathways in the upper slope are affected to a certain degree by the presence of the meandering belt. In addition, hydrocarbon accumulates in two separate traps at the top of the anticline, one outside the meandering belt, the other within the belt in a nose structure (Fig. 2.51c). When the nose structure is filled up, some hydrocarbons spill over the meandering belt into the adjacent trap. The impact of heterogeneity of the carrier beds increases when the Bond number decreases (Fig. 2.51c–f). Pathways increasingly converge toward and merge into the meandering belt and widen. As a result, a lesser amount of hydrocarbons is available to charge directly to the anticline outside the meandering belt. When the Bond number is 10−5 in the meandering belt and 10−6 outside the belt, most of the pathways are confined in the meandering belt, deviating from the direction of the driving force (Fig. 2.51f). In addition, pathways outside the meandering belt are widened and more prone to intercept and merge with the meandering belt and, as a result, do not extend far upslope.

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Fig. 2.51 Model and images showing the results of simulation of pathways in an adjoining syncline and anticline composed of macroscopically heterogeneous media in a uniform flow-potential field. a Structural configuration of the adjoining syncline (lower left) and anticline (upper right) constructed using z = x × exp (− x 2 − y2 ). The color bar scale is the relative flux of migrated oil in pathways. b Simulation results corresponding to a Bond number of 1.0 × 10 −3 with a homogeneous carrier medium with an elliptic source area. c–f The meandering carrier bed has an average pore-throat radius of 0.2 mm with a variance of 0.1 mm; and the channel sandstone has an average pore throat radius of 0.63 mm with a variance of 0.31 mm. Simulation results with the meandering carrier bed corresponding to Bond numbers 1.0 × 10−3 , 1.0 × 10−4 , 1.0 × 10−5 , and 1.0 × 10−6 , respectively. And the values of Bond number corresponding to the channel sandstone are 1.0 × 10−2 , 1.0 × 10−3 , 1.0 × 10−4 , and 1.0 × 10−5 , respectively. Numbers along the horizontal and vertical axes are grid coordinates on the xy plane, and the z coordinate is not shown. Thin black circles are topographic contour lines and labeled with elevation

4. Discussion on heterogeneity of hydrocarbon migration The sedimentary fills of sedimentary basins change laterally and are heterogeneous. Many heterogeneous carrier models can be combined into scenarios with variable heterogeneity of carrier beds fluid potential field and source supply. We simulate and discuss some of the typical scenarios. The practical targets encountered in exploration and development may be a composite of multiple models and cannot be generalized and should be dealt on a case-by-case basis. In the simulations described above, we attempt to consider as many heterogeneous scenarios as possible. For any simulation of a practical target, the result may differ from our examples, but the overall trend should be valid.

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Simulations described above deal with only the simplest cases where oil migrates through a single carrier bed underneath a seal and do not consider pathway heterogeneity during vertical hydrocarbon migration. Pathways would merge and concentrate upsection during vertical migration (Hirsch & Thompson, 1995; Luo et al., 2007c), and their heterogeneity and efficiency in hydrocarbon pathways are significantly controlled by vertical heterogeneity of physical properties of carrier beds (Karlsen & Skeie, 2006). In fact, our simulations are pseudo–3D; and lateral migration in all simulations occurs in the top zone of carrier beds just underneath a perfect seal. In order to assess the efficiency of migration pathways, we use the concept of unit hydrocarbon expulsion to express the hydrocarbon expulsion quantity from source rocks. In the case of a uniform grid covering the area of migration, the smallest hydrocarbon expulsion rate in a grid unit in the effective source rock area is taken as the basic unit value. Thus, N points on each unit grid surface are randomly selected as hydrocarbon expulsion sources; and each point expels a basic unit of hydrocarbon. N is the ratio of the hydrocarbon expulsion rate of the grid to the minimum rate of hydrocarbon expulsion. With this definition, the simulation of heterogeneous hydrocarbon expulsion rate of source rocks can be easily realized. Our simulations indicate that a pathway, once intercepting a preexisting pathway, will merge with the preexisting pathway. This is because hydrocarbons will follow pathways that have the largest difference between driving and resisting forces (Hirsch & Thompson, 1995; Luo et al., 2004; Karlsen & Skeie, 2006), and preexisting pathways are commonly places with the least resistance. For episodic migration processes, the preexisting network of pathways has, in fact, modified the originally macroscopically homogeneous carrier medium, which becomes heterogeneous to later migration. This type of heterogeneity may be exacerbated by the interaction and feedback mechanisms among pathways. Hydrocarbons migrating later through this medium will preferentially select the preexisting pathways. Moreover, the grain surface of the preexisting pathways has been modified/coated by previously migrated hydrocarbons (Dullien, 1992), resulting in a heterogeneously oil-wetted carrier. Finally, our physical migration experiments indicate that the initial pathways created by an initially weak driving force are commonly narrow but will not change until the weak force is increased several times (Hou et al., 2005). Even then, the widened pathways are much narrower than those formed initially by a large driving force. Systematic experiments indicate that the fundamental cause of pathway heterogeneity is the competition among pathways of different characteristics as caused by the microscopic heterogeneity of carrier media (Catalan et al., 1992; Lenormand, 1985; Tokunaga et al., 2000). The degree of pathway heterogeneity is mainly determined by the relative strength of the driving force to the resisting force (Hou et al., 2005; Luo et al., 2004; Tokunaga et al., 2000). All porous media are inherently microscopically heterogeneous (de Marsily, 1981; Weber, 1986); hence, pathway heterogeneity is intrinsic to the hydrocarbon migration process. The heterogeneity of migration pathways in macroscopically homogeneous carrier beds is represented by not only the number and geometry of pathways themselves, but also the amount of migrated hydrocarbons through the pathways

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(Fig. 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.50 and 2.51). This phenomenon reflects the control of preexisting pathways on later migration (Luo et al., 2004, 2007b) and is described above in this chapter by the flux of migrated hydrocarbon in a pathway. This chapter demonstrates another kind of heterogeneity of migration, that is, the amount of migrated hydrocarbons in individual pathways differs significantly. This understanding offers a potential method to assess the efficiency of pathways to identify the optimal pathways for hydrocarbon migration.

2.3.4 A Case Study on Hydrocarbon Migration in Middle Jurassic of Paris Basin In order to verify and improve the applicability of the MigMOD model in oil and gas migration to actual basins, we use the data from Paris Basin to carry out a case analysis, and compare the results with previous simulation results of Hindle (1997), Bekele et al. (1999). The Paris Basin is selected because of its laterally continuous carrier beds, relatively simple structural configurations, and well-studied petroleum systems (Chiarelli, 1973). In addition, Hindle (1997) and Bekele et al. (1999) have conducted simulations of hydrocarbon migration pathways. Bekele et al. (1999) specifically emphasized the migration heterogeneity associated with the Jurassic Dogger Formation. Hindle (1997) applied a flow path method; and Bekele et al. (1999) used a hybrid method. Thus, it is interesting to compare the results obtained with different methods and research ideas. The Paris Basin in northern France is a Mesozoic-Cenozoic cratonic interior basin, covering 50,000 km2 . Sedimentary deposits in the basin center are 3000 m (9843 ft) thick and include mainly Mesozoic and Tertiary siliciclastic rocks intercalated with carbonate rocks. Hydrocarbons discovered in Triassic, Middle Jurassic, and Lower Cretaceous rocks account for, respectively, 50, 40, and 10% of the total discovered hydrocarbons in the basin (Hindle, 1997). Model results of Hindle (1997), Bekele et al. (1999) indicate that the Lower Jurassic source rocks became matured and hydrocarbon was expelled during the Cretaceous-Oligocene (Espitalié et al., 1988), when the structural configurations of the seal beds capping the Middle Jurassic carrier beds started to stabilize. Hindle (1997), Bekele et al. (1999) have simulated and analyzed the migration and accumulation of oil and gas in the basin, and established a model of migrationaccumulation unit with the Middle Jurassic as the reservoir/carrier bed. In their model, the oil in the Middle Jurassic reservoir comes from the underlying Lower Jurassic source rock; and the driving force of migration is mainly buoyancy. The oil that first migrated into the Middle Jurassic reservoir/carrier bed migrated vertically to the top of the reservoir, then migrated laterally and accumulated to form a series of oil reservoirs. Hindle (1997) gave the hydrocarbon expulsion rate of source rocks for migration simulation (Fig. 2.52c), constructed the structural configuration of the upper surface of the Middle Jurassic carrier bed, and marked the oil and gas

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discoveries (Fig. 2.52a). Bekele et al. (1999) presented a heterogeneous carrier bed model (Fig. 2.52b). We use the geologic migration models of Hindle (1997), Bekele et al. (1999), including the present structural configuration at the top of the Middle Jurassic carrier bed, hydrocarbon discoveries (Fig. 2.51a), the rate of hydrocarbon expulsion from the Lower Jurassic source rocks to the Middle Jurassic carrier bed (Fig. 2.52b), and basin-scale permeability variations of the Middle Jurassic carrier bed (Fig. 2.52d, e). Numerical simulation is conducted using the MigMOD codes of Luo et al. (2007b). The study area is covered by a 600 × 590 grid. The Middle Jurassic carrier beds were considered as uniform (Hindle, 1997; Fig. 2.52c) or subdivided into three types with distinctive ranges of permeability (Fig. 2.51d). The expected value of porethroat distribution (r) is obtained from the average permeability of three types of carrier beds using a simple theoretical tube model of Dullien (1992): √ √ r = 2 2k φ

(2.19)

where k is the average permeability; φ the average porosity. The average permeability of the three types of carrier beds is 150 for Type I carrier bed, 10 (Type II), and 0.1 (Type III) md, as adapted from Bekele et al. (1999). The average porosity is 15% (Matray & Chery, 1998); and the tortuosity factor is 3 (Dullien, 1992). The porethroat size is assumed to follow the Gaussian distribution; and the pore-throat radii are 0.05, 0.0125, and 0.0012 mm for types I, II, and III carrier beds, respectively. The superimposition of a structural map, localities of oil fields, map of expulsion rate, and simulated pathways in homogeneous carrier beds (Fig. 2.52a–c) shows that when the heterogeneity of the carrier is not taken into account, the simulation result (Fig. 2.52c) is similar to those simulated with a flow path method by Hindle (1997). Nearly all discovered oil fields lie on or near the optimal pathways, except in the northern part of the basin, where simulated pathways are intensive but no oil field has been discovered. Hindle (1997) thought that this may be due to less drilling in the northern part of the basin. However, Bekele et al. (1999) did not agree with this explanation. They thought that it should be caused by the heterogeneity of the carrier bed. The heterogeneous carrier bed model of Bekele et al., (1999; Fig. 2.52c) is a dynamic model based on Darcy’s Law and can take into account of the heterogeneity of the carrier bed. However, the migration pathways simulated by Bekele et al. (1999) appear far from satisfaction. The migration pathways cover the northern part of the basin, and are rare in the other parts (Bekele et al., 1999). The distribution of hydrocarbon migration pathways simulated with MigMOD model using the carrier bed model in Fig. 2.52c is shown in Fig. 2.52e. The results with a heterogeneous carrier (Fig. 2.52d) also match well with the discovered fields. Most oil fields lie on or near the optimal pathways. This suggests that the pathways are principally controlled by the distribution of high-permeability carrier beds (Fig. 2.52d) and by the rate of hydrocarbon expulsion (Fig. 2.52b). The number of pathways is high within the area of hydrocarbon expulsion; but pathways merge

Fig. 2.52 Simulation of migration pathways in the Dogger Formation of the Paris Basin. a Schematic map showing the structure map at the top of the Middle Jurassic Dogger Formation with depth in meters, the Bray fault, and the discovered oil fields. b Map showing the intensity of expelled oil from the Lower Jurassic source rock underlying the Dogger Formation. Modified from Hindle (1997), Bekele et al. (1999). c Permeability characterization results of heterogeneity of Dogger Formation carrier bed. d Simulation result of pathway development in the Dogger Formation with a uniform carrier bed, where the permeability is assumed to be 10 mD. The red line is the Bray fault, which was open during secondary migration. e Simulation result of pathway development in the Dogger Formation with a heterogeneous carrier bed, where the average permeabilities of the three types of reservoirs are 150, 10, and 0.1 mD, respectively. f Simulation result of pathway development in the Dogger Formation with a heterogeneous carrier bed similar to that in panel D. However, the type III carrier is divided into two subtypes, where the average permeabilities are 0.1 mD for the southern part and 0.01 mD for the northern part. The color bar scale is the relative flux of migrated oil in pathways shown in panel b. (After Luo, 2011)

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Fig. 2.52 (continued)

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quickly with each other outside of the expulsion area. Optimal pathways with a relatively large amount of migrated hydrocarbons are developed within the area of the high-permeability carrier bed (Fig. 2.52d). In addition, the Bray Fault (Fig. 2.52a) is assumed to open for secondary migration. As a result, two optimal pathways form along the fault of a the northwest-southeast orientation. However, in the northern part of the basin, there are still a few areas with concentrated migration pathways. This is not consistent with the actual discoveries. According to the permeability model given by Bekele et al. (1999), we assume that the physical properties of Type III carrier bed can be further subdivided. The permeability of Type III carrier beds in other areas of the basin is classified as Type III1 with an average permeability of 0.1 mD, while the permeability of Type III-2 carrier bed in the north of the basin is 0.01 mD. The simulation result (Fig. 2.52e) indicates that the migrating oil is limited in the low-permeability carriers and the pathways did not merge to form optimal pathways. As a result, no significant accumulations had occurred. This is because the expelling pressure is smaller than the average capillary pressure (Luo, 2011). Thus, most of the oil may not be expelled from the source rock to the carrier. In summary, our simulation result supports the hypothesis that the low permeability of the carrier beds in the northern part of the Paris Basin hampered hydrocarbon migration and accumulation in that region.

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Tokunaga, T., Mogi, K., Matsubara, O., Tosaka, H., & Kojima, K. (2000). Buoyancy and interfacial force effects on two-phase displacement patterns: An experimental study. AAPG Bulletin, 84, 65–74. Toussaint, R., Løvoll, G., Méheust, Y., Måløy, K. J., & Schmittbuhl, J. (2005). Influence of pore-scale disorder on viscous fingering during drainage. Europhysics Letters, 71, 583–589. Ungerer, P., Burrus, J., Doligez, B., Chenet, Y., & Bessis, F. (1990). Basin evaluation by integrated two-dimensional modeling of heat transfer, fluid flow, hydrocarbon generation, and migration. American Association of Petroleum Geologists Bulletin, 74, 309–335. Vasseur, G., Luo, X. R., Yan, J. Z., Loggia, D., Schmittbuhl, J., & Toussaint, R. (2013). Flow regime associated with vertical secondary migration. Marine and Petroleum Geology, 45, 150–158. Wagner, G., Birovljev, A., Meakin, P., Feder, J., & Jossang, T. (1997). Fragmentation and migration of invasion percolation cluster: Experiments and simulations. Physical Review E, 55(6), 7015– 7029. Wang, W. M., Guo, H. K., Sun, D. Q., & Zhang, S. Z. (1997). A study on polymer flooding by MRI techniques. Acta Petrolei Sinica, 18(4), 54–56. Wang, W. M., Lang, D. J., & Liu, W. (1996). The application of NMR imaging to the studies of enhanced oil recovery in China. Magnetic Resonance Imaging, 14(5), 951–953. Wang, W. M., Li, P., & Ye, Z. H. (2001b). Multi-exponential inversion of NMR relaxation signal. Science in China (Series D: Earth Sciences), 44(11), 730–736. Wang, W. M., Guo, H. K., & Ye, C. H. (2001b). Experimental studies of NMR properties of continental sedimentary rocks. Chinese Journal of Magnetic Resonance, 18(2), 223–227. Warren, J. E., & Price, H. S. (1961). Flow in heterogeneous porous media. SPE Journal, 153–169. Weber, K. J. (1986). How heterogeneity affects oil recovery. In L. W. Lake & H. B. Carroll, Jr. (Eds.), Reservoir characterization (pp. 487–544). Academic Press. Welte, D. H., Hantschel, T., Wygrala, B. P., Weissenburger, K. S., & Carruthers, D. (2000). Aspects of petroleum migration modelling. Journal of Geochemic Exploration, 69–70, 711–714. Wilkinson, D., & Willemsen, J. F. (1983). Invasion percolation: A new form of percolation theory. Journal of Physics A General Physics, 16, 3365–3376. Wilkinson, D. (1984). Percolation model of immiscible displacement in the presence of buoyancy forces. Physical Review A, 30, 520–531. Wilkinson, D. (1986). Percolation effects in immiscible displacement. Physical Review A, 34, 1380– 1391. Yan, J. Z., Luo, X. R., Wang, W. M., Chen, F., Toussaint, R., Schmittbuhl, J., Vasseur, G., & Zhang, L. K. (2012a). Testing oil saturation distribution in migration paths using MRI. Journal of Petroleum Science and Engineering, 86–87, 237–245. Yan, J. Z., Luo, X. R., Wang, W. M., Toussaint, R., Schmittbuhl, J., Vasseur, G., Chen, F., Yu, A., & Zhang, L. K. (2012b). An experimental study of secondary oil migration in a three-dimensional tilted porous medium. AAPG Bulletin, 96(5), 773–788. Yan, J. Z., Luo, X. R., Zhang, L. K., & Lei, Y. H. (2012c). Experimental study on percolation backbone of secondary migration of crude oil. Petroleum Geology & Experiment, 34(1), 99–103. Yan, J. Z. (2009). Mechanism reaseach of secondary oil migration by physical simulation. Doctor thesis of Institute of Geology and Geophysics. Chinese Academy of Sciences. Zeng, J. H., & Jin, Z. J. (2000). Physical simulation experiment of oil and gas secondary migration and accumulation (pp. 1–243). Petroleum Industry Press. Zhang, B. Q., & Wang, X. Y. (1989). Physical property of oil (gas) reservoir (pp. 39–48). China University of Geosciences Press. Zhang, J. L., Zhu, B. Q., & Zhang, P. Z. (1997). The new development of pb-sr-nd isotopic systematics in petroleum geochemistry. Advances in Earth Science, 12(1), 58–61. Zhang, L. K., Luo, X. R., Liao, Q. J., Yang, W., Guy, V., Yu, C. H., Su, J. Q., Yuan, S. Q., Xiao, D. Q., & Wang, Z. M. (2010). Quantitative evaluation of synsedimentary fault opening and sealing properties using hydrocarbon connection probability assessment. AAPG Bulletin, 94, 1379–1399.

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Zhang, L. K., Luo, X. R., Liao, Q. J., Yuan, D. J., Xiao, D. Q., Wang, Z. M., & Yu, C. H. (2007). Quantitative evaluation of fault sealing property with fault connectivity probabilistic method. Oil & Gas Geology, 28(2), 181–191. (in Chinese). Zhang, S. W., Wang, Y. S., Shi, D. S., & Xu, H. M. (2003). Meshwork-carpet type oil and gas poolforming system-taking Neogene of Jiyang depression as an example. Petroleum Expoloration and Development, 30(1), 1–10. Zhao, M. F., Xin, Q. L., Li, Y. H., Gao, H. Y., & Xu, Z. Z. (2001). Progress in study on fault sealing. Xinjiang Petroleum Geology, 22(3), 258–362. Zhao, S. X., & Luo, X. R. (2003). Simulating studies on hydrocarbon migration. Acta Simulata Systematica Sinica, 15(10), 1477–1480. Zhou, B., Loggia, D., Luo, X. R., Vasseur, G., & Ping, H. (2006). Numerical studies of gravity destabilized percolation in 2D porous media. The European Physical Journal B-Condensed Matter and Complex Systems, 50(4), 631–637.

Chapter 3

Hydrocarbon Conduit System and Its Quantitative Characterization

For conventional Hydrocarbon, the conduit system is commonly a three-dimensional framework composed of various interconnected migration carriers, which serve as bridges between hydrocarbon sources and reservoirs in a hydrocarbon migration accumulation unit (MAU). The conduit system is also a key link in the study of Hydrocarbon migration and accumulation. For a long time, the understanding of the conduit system is very limited in comparison to the understanding of the characteristics of hydrocarbon migration driving forces. In the study of hydrocarbon migration and accumulation, it is often assumed that the migration carrier bed is a relatively uniform geological formation (Hindle, 1997; Karlsen & Skeie, 2006), which is far from the reality (Bekele et al., 1999; Dutton et al., 2002; Luo, 2011; Luo et al., 2015). An increasing amount of evidence shows that the migration process in the conduit system is highly heterogeneous and occurs only along a limited range of pathways (Berg, 1975; Harms, 1966; McNeal, 1961; Schowalter, 1979; Smith, 1966). The fundamental cause for the heterogeneity of migration pathways is the heterogeneity of the rock formations. The micro-heterogeneity of rocks, which comprise the conduit, leads to the bending and merging of pathways, resulting in the macro-heterogeneity of the pathways, while the macro-heterogeneity of the migration conduit commonly significantly enhances the heterogeneity of the migration pathways (Luo, 2011; Luo et al., 2016). Therefore, on a basin scale, the connectivity of the conduit system greatly affects the hydrocarbon migration direction and the locality of reservoir forming, and determine the efficiency of hydrocarbon migration and accumulation and the amount of hydrocarbon loss during migration (Luo et al., 2007b). In order to accurately describe the process of migration and accumulation, we should not only pay attention to the characteristics of hydrodynamic field of oil and gas migration, but also quantitatively study the heterogeneity of migration conduits (Hao et al., 2000; Karlsen & Skeie, 2006; Luo, 2003, 2011). Some urgent questions include, with limited data, how to identify geological bodies that can serve as a path, how to develop a suitable geological model and determine the connectivity among the bodies, and to determin whether or not oil and gas can migrate along them.

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With respect to its effect on hydrocarbon migration and the feasibility in research, the conduit system is generally regarded as being composed of carrier beds, carrier faults (fracture zones) and geological bodies associated with unconformities, as well as their assemblages. Each type of carriers has a strong heterogeneity. Their transmission capability and the stacking relationship among carriers in an assemblage have been constantly changing during the basin evolution. As a result, quantitative study, which aims to quantitatively characterize the connectivity and transmission capacity of a conduit system during hydrocarbon migration, is essential for any research on hydrocarbon migration and accumulation. The study should focus on the dynamic evolution of the conduits, the method of quantitative characterization, the architectural framework of the carrier assemblage during the critical period of hydrocarbon migration and accumulation, in order to successfully evaluate quantitatively the effect of heterogeneity on hydrocarbon migration and accumulation.

3.1 Sandstone Carrier Beds and Their Characterization Sandstone bodies are typical carriers in siliciclastic basin fills. A sandstone body may be regarded as a carrier when it is hydrodynamically connected with other sandstone bodies through direct contact or open faults or fracture zones. The formation that contains interconnected permeable sandstone bodies constitute the main conduit for large-scale and long-distance lateral migration. The quantitative characterization of their performance is the basis for the study of lateral migration mechanism and process, and is the frontier of migration research (Luo et al., 2020). Traditionally, a carrier bed for oil and gas migration is generally assumed as a relatively uniform plate on the basin scale (Allen & Allen, 1990; England & Muggoridge, 1995; Thomas & Clouse, 1995). The migration directions and pathways were estimated according to the trend of hydrocarbon flow from a high potential area to a low potential area (Hubbert, 1953; Hindle, 1997; Tao, 1993). However, in sedimentary basins, especially continental basins dominated by fluvial and lacustrine deposits, the sandstone carrier beds are commonly formed by lateral and vertical superposition of sandstone bodies (Pranter & Sommer, 2011). The spatial distribution, internal structure and physical properties of the bodies are complex and highly heterogeneous (Weber, 1986). Obviously, a sandstone carrier bed cannot be simply regarded as a homogeneous plate at all scales. That is, it is not enough to only consider the fluid potential in the study of oil and gas migration in heterogeneous carrier beds (Bekele, et al., 1999). It is very difficult to quantitatively characterize the heterogeneity of a carrier bed. The main reason is the limited subsurface data. Even in mature basins, well data are very limited; and the geophysical methods, such as seismic imaging, can only provide low-resolution data. Reservoir characterization, which has emerged since the 1980s from the progress and integration of geology, mathematics and computer technology, seismic technology and other fields (Fowler et al., 1999), offers a powerful method for understanding reservoir lithology, geometry, continuity, heterogeneity, physical

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properties, fluid characteristics and their relationship at the oilfield scale (Fowler et al., 1999; Jia, 2011; Yu, 2009). Many research ideas and analytical techniques in reservoir characterization can be used as a reference for in-depth research and quantitative analysis of sandstone carrier beds. In recent years, the authors have carried out geological modeling, quantitative characterization, and evaluation of sandstone migration carriers (Lei et al., 2010; Luo et al., 2007a, 2007b). This section describes the concept and practical models of clastic carrier beds and the methods of quantitative characterization of the connectivity and conductability. The goal is to quantitatively evaluate the heterogeneity characteristics of clastic sandstone carrier bed.

3.1.1 Concept of Carrier Bed Permeable sandstone bodies are typical carriers in siliciclastic formations. In most clastic reservoir beds, sandstone bodies are confined by mudstone, so they can only serve as reservoirs. However, once the sandstone bodies are hydrodynamically connected by direct contacting or opening faults or fractures, they can be regarded as carriers. Similarly, when a stratum containing interconnected permeable sandstone bodies constitute the conductor for large-scale and long-distance lateral migration, it is a carrier bed, that may offer affective conduit from the hydrocarbon source to the trap. Otherwise the strata can only serve as a reservoir bed. Thus the seepage space among carriers, that is, the connection state between the sandstone bodies, changes continuously with the evolution of the basin, and the time interval is limited in the definition of carriers. In this way, the reservoirs and the carriers in a reservoir bed possess the same sandstone bodies but behave differently for hydrocarbon accumulating or migrating, and similarly, the reservoir bed and the carrier bed possess the same strata but behave differently, on a larger scale. Both sedimentation and diagenesis can cause significant heterogeneity within sandstone formations at different scales (Weber, 1986). From the perspective of geostatistics, when describing the characteristics of the carrier that may provide conduits for lateral migration, the data from each collection site can only represent a small volume. To ensure the representativeness of the relevant parameter values on a larger spatial scale, the data from sandstone bodies over a certain thickness range need to be collected for statistical analysis. In the analysis of these data, the following three principles should be fully considered: (1) For continental sedimentary basins, the actual subsurface information available at any level of exploration cannot fully determine the sandstone properties in the carrier; even at the stage of hydrocarbon field development, it is difficult to directly describe the geometrically connected sandstone bodies (King, 1990; Pranter & Sommer, 2011). (2) For formations with interbedded sandstones and mudrocks, the distribution of sandstone bodies is often highly heterogeneous, and laterally migrating hydrocarbons did not necessarily migrate along a thin sandstone layer under the caprock (Bekele et al., 1999;

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Luo et al., 2020); hydrocarbons subject to migration dynamics tend to follow dominant conduits during migration (Carruthers & Ringrose, 1998; Luo, 2011), which may allow hydrocarbons to be migrated in sandstone bodies that are some distance from the bottom of the caprock but have better transport capability (Luo et al., 2015). (3) According to Walther’s Law, the areal distribution of sedimentary facies has a genetic relation with the vertical stacking of those facies. Therefore, to conduct basin-scale hydrocarbon migration studies, quantitative characterization of the transport capability of sandstone bodies and their interrelationships should be carried out within a stratigraphic interval of a certain thickness. The specific interval thickness and its spatial variation should be determined based on the extent of an MAU in the study area, the availability of data, and the knowledge of structure of the local hydrocarbon migration conduit. We propose that a carrier bed is the sum of all carriers in a stratigraphic interval of a certain thickness under a regional caprock. The individual carriers are geometrically connected macroscopically and hydrodynamically connected with each other when hydrocarbon migration occurs. This concept emphasizes that the studies of the carrier bed must be conducted in a certain temporal and spatial extent, because hydrocarbon migration and accumulation often occur over several short geologic time periods (Hentschel & Kauerauf, 2007). Geological activities between two migration and accumulation episodes can cause important changes in the physical properties of the carrier bodies, as well as their connectivity, and even hydrodynamic regime within the MAU (e.g., opening and sealing of deep faults leading to connection or isolation between MAUs), resulting in fundamental changes in the distribution of the carrier beds and their transport characteristics. Therefore, the relationship of hydrocarbon source-carrier bed-trap, and the physical properties of a carrier bed at the present time often cannot be directly used to describe the conditions when hydrocarbon migration occurred in geological past. The hydrocarbon migration-accumulation unit (MAU) naturally defines the spatial extent, as well as the effective period, of a carrier bed. In the first chapter, we described the principles and methodology of dynamics study of hydrocarbon reservoir formation, and defined the MAU that is limited in temporal and spatial extent. An MAU is a unified dynamic environment system from hydrocarbon sources to reservoirs within the period of hydrocarbon migration and accumulation. An MAU is a simplified system for researchers to focus on the dynamic characteristics and evolution of driving forces and conduit frameworks for hydrocarbon migration and accumulation, in order to conduct quantitative study on the mechanism, controlling factors and dynamic process of hydrocarbon supply, migration and accumulation. In an MAU, the coupling of migration and accumulation dynamics and conduits can be easily identified to study the pathway characteristics, migration direction and amount. The study of carrier beds and development of relevant models should be confined to and target at a specific MAU where oil and gas migration and accumulation had occurred during a specific time period.

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3.1.2 Delineation of Carrier Beds If the general spatial and temporal range of a carrier bed is known, the stratigraphic units that comprise the carrier bed need to be determined according to the characteristics of the source-reservoir-seal assemblage. The estimation of the carrier bed thickness depends mainly on the data availability and the role of the carrier bed in the lateral migration and accumulation of hydrocarbons. Since the depositional age of the carrier bed is generally much older than the time of hydrocarbon generation, migration and accumulation, a set of diachronous stratigraphic units can be the same carrier bed if they served as the carrier during a specific episode of hydrocarbon migration and accumulation. The Chang 8 producing interval of the Yanchang Formation in the Longdong area of the Ordos Basin is a good example to illustrate the delineation of carrier beds (Chen et al., 2006; Luo et al., 2007a). The cross-section in Fig. 3.1 contains Chang 82 , Chang 81 , and Chang 73 producing intervals, which extend across the Xifeng channel sandstone complex from southwest to northeast. The Chang 8 interval and its overlying Chang 73 interval are mainly composed of distributary channel, channelmouth bar, and sheet-like sandstones, which contain intercalated interdistributary and lacustrine mudrocks. The sandstones of Chang 82 subunit are generally isolated and lenticular, with poor continuity, and encased in mudrocks, although few are thick and relatively continuous (Fig. 3.1). The sandstone bodies of Chang 81 are more abundant and continuous; the upper part contains thick sandstones with high production as the main producers of Xifeng Oilfield. The lower part of Chang 81 contains small distributary channel and channel mouth bar sand bodies with a reduced continuity. Chang 73 subunit consists of mainly lacustrine deposits with few sand bodies and dominantly mudrocks, which

Fig. 3.1 Characteristics and delineation of sandstone carrier beds in the Chang 8 producing interval of Yanchang Formation in Longdong area of Ordos Basin, China

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Fig. 3.2 Sandstone isopach maps of carrier bed of a Chang 81 and carrier bed of b Chang 82 in Longdong area of Ordos Basin, China

are not only high-quality hydrocarbon source rocks, but also regional caprocks (Xi et al., 2004; Yang & Zhang, 2005). The thickness of Chang 8 unit in Longdong area is 80–90 m. With respect of sedimentary environment, stratigraphic characteristics and lateral relationship of sandstone bodies, it may be reasonable to regard the entire unit as a carrier bed. However, there are differences in reservoir characteristics between Chang 81 and Chang 82 subunits (Fu et al., 2004; Xi et al., 2004). The two subunits are regarded as two exploration target intervals. Therefore, the Chang 8 unit contains two carrier beds (Luo et al., 2007a), namely, the Chang 81 as the carrier bed and Chang 73 as the associated caprock, and the Chang 82 carrier bed and the lower part of mudrock interval Chang 81 as the caprock. Figure 3.2 shows the different distribution patterns of the two carrier beds of Chang 81 and Chang 82 . There are also thicker superimposed sandstone bodies in Chang 82 . The sufficient data allowed us to subdivide Chang 8 unit into two carrier beds.

3.1.3 Development of Carrier Bed Models The heterogeneity in sandstone composition and sedimentary structures as well as distribution requires gridding on the map to quantitative characterize the bed in order to develop a numerical model of the bed. The specific procedures in model development are described here. The studied reservoir–caprock interval is divided into grids, so that the carrier bed consists of

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columnar bodies at the scale of the grid (Fig. 3.3a). The grid size is determined by the accuracy of the geological data. For clastic carrier bed, the basic unit is generally the lithologic strata, and the transport capability and connectivity characterized by each columnar carrier are independent of each other (Fig. 3.3b). The advantage of this method is that each column within the carrier bed can be deposited simultaneously or diachronously, or even belong to different stratigraphic levels, as long as the carrier bed composed of different columns can constitute conduits for fluid flow during migration. Many characteristics of the carrier bed need to be obtained by statistical analysis of parameters within the entire unit column. Therefore, various information recorded on these unit columns, such as sandstone ratio, sandstone properties, and transport capability, are uniformly distributed over each column (Fig. 3.3c). Furthermore, all the unit column data of the carrier bed can be interpolated using a stochastic method to obtain the distribution of the characterization parameters for the entire carrier bed (Fig. 3.3a). As the petrophysical properties in each unit column is expressed by the average value of the parameters, so that the migration of oil and gas in the column tends to occur at the top. Therefore, the three-dimensional carrier bed model shown in Fig. 3.3a can be simplified into a two-dimensional one (Fig. 3.3b). The characteristics of the carrier bed model matches the presentation of the fluid potential in the previous

Fig. 3.3 The realization of the characterization of the petrophysical parameters of a carrier bed. a The three-dimensional geological model of a carrier bed; b vertical lithologic column of a grid unit of the model; c homogenization of the values of any one parameter in the lithologic column; d a two-dimensional carrier bed model that can quantitatively characterize various petrophysical parameters

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migration studies, and the simulation analysis of oil and gas migration can be easily carried out on the two-dimensional “carrier bed”.

3.1.4 Geometric Connectivity of Carrier Beds As for the connectivity mode of sand bodies, many previous studies have been conducted in the description of hydrocarbon field development, and many methods have been proposed to identify and characterize the connectivity among sand bodies (Allen, 1978; Jackson et al., 2005; King, 1990; Qiu, 1990). The geometric connectivity of a carrier bed is defined to characterize the connectivity between sandstone bodies in a geometric space. If the connection or barrier effect of faults or fracture zones is not considered, the necessary condition for the connection of carrier bed is the direct contact between sandstone bodies, that is, the mutual connection between sandstone bodies in a geometric space. This is the basic connection mode for sandstone bodies to form carrier bed. Allen (1978) proposed a theoretical model to determine the sand body connectivity by using the critical value of net gross ratio. For petroliferous basins in China, Qiu (1990) modified the critical value of Allen (1978), and concluded that, when the net gross ratio of sand bodies is larger than 50%, the sand bodies are widely connected; when the net gross ratio is less than 30%, sand bodies are mostly isolated; when the net gross ratio is between 30% and 50%, the connectivity between sand bodies changes with the value of the ratio. King (1990) used percolation theory to study the connectivity between superimposed sandstone bodies, and found that there exists a specific threshold of sandstone net gross ratio, below which the sand bodies are basically disconnected; with the increase of the ratio, sand bodies start to superimpose on each other and form connected sand body clusters; finally, when the net gross ratio reaches the threshold, there will be connected sand clusters through the units to form connected carrier bed, which is called “percolation threshold”. King (1990) found that for a conceptual system containing infinitely extended isotropic sand bodies (Fig. 3.4a), the three-dimensional percolation threshold is 0.276, while for a two-dimensional flat sand bodies model, the percolation threshold is about 0.60 (Fig. 3.4b). Jackson et al. (2005) proposed a comprehensive model of reservoir with interbedded lamellar sandstone and mudstone. They estimated a horizontal percolation threshold of 0.28, but the vertical percolation threshold is greater than the horizontal threshold, about 0.5. The latter value is similar to that of King (1990). Jackson et al. (2005) attributed this difference to the fact that lamellar sandstone bodies are easily separated by mudrocks stratigraphically. A mathematical model is established by Gaussian fitting to describe the connectivity among sand bodies in the carrier bed (Lei et al., 2014). The model is based on the connectivity probability model of sandstone bodies of King (1990), using the data obtained in oilfields, through statistical analysis of the relationship between sand body connectivity and the sandstone net gross ratio (Fig. 3.5):

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Fig. 3.4 Model used to evaluate the connectivity of fluvial sandstone reservoirs, after King (1990). a Stacking patterns of sandstone bodies in a fluvial reservoirs; b simulation results of the relationship between sandstone body connectivity probability and sandstone net gross ratio

 P=

h ≤ C0

0 1 − e(−(h−C0 )

2

/b ) 2

h > C0

(3.1)

√ where h is the net gross ratio, b = (C−C 0 )/ 3 is the connectivity index, C 0 and C are respectively the net gross ratio values corresponding to the percolation threshold of sandstone connectivity and to the state where all the sandstone bodies are complete connected (connectivity probability P > 0.95). The above method is used to analyze the connectivity of sandstone carrier beds of Shahejie Formation on the southern slope of Niuzhuang Subsag in Dongying Sag of Bohai Bay Basin. Figure 3.6 shows the geometric connectivity characteristics of sandstone carrier bed in the middle part of the Third Member, with C 0 and C values as 0.20 and 0.50, respectively. The green dots in the figure indicate connected sand bodies, and the denser the green dots, the greater the connection probability between sand bodies. The dark green dots indicate sandstone bodies which are not only locally connected but also form regional connected conduits. Although the cumulative thickness of sandstones is relatively large, it is mainly turbidite sandstones in prodelta,

Fig. 3.5 Model of geometric connectivity of sandstone bodies in carrier beds, where C 0 is the percolation threshold, and C the complete connectivity coefficient

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Fig. 3.6 Distribution of geometrical connectivity probability of sandstone bodies in carrier beds within the middle part of the Third Member of Shahejie Formation in Southern Slope of Niuzhuang Sag, Dongying Sag, Bohai Bay Basin. The dark green area indicates regionally-connected sandstones and light green area locally-connected sandstones

which is lenticular in shape, with a low net gross ratio and poor connectivity between sand bodies. In Wangjiagang Oilfield, Bamianhe-Caoqiao and G127-T11-C25-W13 wells in the southern part of the study area, the sandstones are mainly distributary channel and channel mouth bar deposits. The sand bodies overlap longitudinally; and the sand body connectivity probability is relatively high, generally greater than 60% (Fig. 3.6), because of the continuous progradation and superposition of Dongying Delta on the southern slope.

3.1.5 Fluid Connectivity of Carrier Beds Geometric connectivity of sandstones in a carrier bed mainly reflects the “static” distribution, which is controlled by sedimentation. Geometric connectivity of sandstones in space does not necessarily mean that sand bodies are hydrodynamically connected to each other. The possible mudstone beds or diagenetic intercalation between sand bodies may be hydrodynamic barriers (Zhang et al., 2003); so the spatially-connected sand bodies are only “potential” migration conduits, not necessarily the actual flow conduits. On the contrary, the carriers previously separated by a certain distance may be connected with each other in a geological period due to

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the formation and activities of faults or fractures (Lampe et al., 2012). Therefore, whether the sandstone bodies in the carrier bed can become fluid migration conduits during an episode of migration also depends on whether fluid flow has occurred between the sandstone bodies, which is termed as hydrodynamic connectivity of the carrier bed. The present hydrodynamic connectivity of carrier beds can be assessed by using the production data from the field. The specific procedure is: (1) assign appropriate grids on the map of the study area with reference to the distribution of production wells and the average size of known composite sand bodies (Fig. 3.7); (2) the hydrodynamic communication between adjacent-grid production wells is analyzed using the production performance information of each well; and (3) values of sandstone net-gross ratios in the area are divided into several intervals from small to large, and the ratio of “connected” grid to all effective grids is counted in each interval, which is the hydrodynamic connection probability. The common methods to obtain production dynamic information include tracer technique, hydrocarbon geochemical analysis, formation pressure discrimination. and pseudo-interference well test (Yu et al., 2007). Figure 3.7 shows the method for connectivity analysis of sandstones between wells in the oilfield. According to the density and distribution of development wells, the study area is divided into grids with a certain density, and the grids containing wells are defined as data grids. Every three adjacent data grids are taken as an analysis unit, and the hydraulic connectivity between the sandstones is obtained from the wells at the two tip-grids, and the sandstone percentage of the reservoir is obtained from the well in the middle grid. In the range shown in the figure, a total of five data analysis units can be obtained. In this process, the three effective data grids distributed at right Fig. 3.7 A schematic diagram of the method of analyzing the connectivity between sandstone bodies through the dynamic data of development wells. The numbers in the figure are the names of development wells, and the capital letters represent the data analysis units

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angles cannot be used to compose a data analysis unit, as the grids of wells 7, 8, and 12 in the Fig. 3.7, because it is difficult to judge the connectivity between wells 7 and 12 through the data from well 8. Figure 3.8 shows the hydrodynamic connectivity mode of the carrier bed in Shahejie Formation on the southern slope of Niuzhuang Subsag in Dongying Sag of Bohai Bay Basin. The bed experienced weak diagenesis and little change in the physical properties from the time of main hydrocarbon migration and accumulation to the present. This is interpreted from inter-well fluid characteristic analysis, chromatographic fingerprint comparison of crude oil, and dynamic/static production data analysis. An inter-well sand body geometric connectivity model of the block T61 in Wangjiagang Oilfield was calibrated and revised. The value of C 0 is determined as 0.15 and C 0.62. Thus, a mathematical model is established to discriminate the hydrodynamic connectivity between sand bodies by sandstone ratio (Fig. 3.8). The model results show the hydrodynamic connectivity probability between sand bodies in the carrier bed (Fig. 3.9). Comparing to the corresponding geometric connectivity results in Fig. 3.6, the hydrodynamic connectivity of sandstone bodies in Fig. 3.9 is different. The general trend is that sandstone bodies with local connectivity are more widely distributed, but the range of sandstone bodies that can form regional connectivity conduits is reduced. The reason for this result is that in the determination of fluid connectivity, various connection/barrier conditions between sandstone bodies are considered, such as the barrier formed by fracture healing, fracture connection between sandstone bodies that are not superimposed on each other or separated by low-permeability beds, and the heterogeneous effect of fluid flow. The hydrodynamic connectivity characteristics between sandstone bodies obtained using the above method is the present-day connectivity. To assess such connectivity during hydrocarbon migration in the past, it is necessary to analyze the

Fig. 3.8 Relationship between hydrodynamic connectivity probability and sandstone percentage in the Wangjiagang Oilfield. The black dots are points of calculated connectivity probability and average sandstone percentage of each 5% increment. The black line is a fitted line showing the relationship between connectivity probability and sandstone percentage

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Fig. 3.9 Connectivity probability map of sandstone carrier beds in the middle part of the Third Member of Shahejie Formation on the southern slope of Niuzhuang Subsag in Dongying Sag of Bohai Bay Basin. The dark green color indicates areas containing regional hydrodynamically connected beds; the light green color areas represent local hydrodynamically connected beds

geological parameters that can be easily obtained in the sandstone bodies at present and can be used to indicate the fluid flow characteristics in the past. Diagenesis needs a certain amount of groundwater circulation. The diagenetic products and diagenetic sequences can be used to indicate that sandstone bodies are hydrodynamically connected with each other when diagenesis occurred, regardless of the kind of spatial relationship and fluid conduits between the bodies. Especially for MAUs with a single source rocks, the hydrocarbon accumulation in different periods is the best indicator of fluid connectivity (Luo et al., 2010). The identifiable hydrocarbons can be used as markers to differentiate the periods of fluid flow in the basin, and then the diagenetic products formed during the main hydrocarbon reservoir forming stage, which can be regarded as the fluid flow products in the period. The spatial distribution of products in the carrier bed can be identified to determine the fluid connectivity between sandstone bodies (Luo et al., 2010; Wu et al., 2006; Zhao et al., 2011). Some studies have tried to determine the paleo-hydrodynamic connectivity of carrier beds by diagenetic products of the main hydrocarbon reservoir forming stage and identifiable hydrocarbons or their residues within the carrier bed, and achieved good results (Chen et al., 2007; Luo et al., 2007a, 2010; Wu et al., 2006). For example, Silurian fossil oil reservoirs around the Manjiaer Depression of Tarim Basin were generally destroyed, forming widely distributed bituminous sandstones.

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Fig. 3.10 Distribution of Silurian bituminous sandstones in Tazhong area of Tarim Basin

Solid bitumen in Silurian bituminous sandstones are distributed in various states as stratified beds and uniform blocks and patches. The bitumen is a special diagenesis product and leads to the deterioration of reservoir physical properties. The bitumen is abundant in cores and cuttings and can be used as an effective marker for hydrodynamic connectivity between sandstone bodies. The distribution of bitumen in Silurian reservoirs in wells is delineated from petrophysical and petrographic data. Among 105 exploration wells in Tazhong area, bituminous sandstones have been found in 62 wells. Bituminous sandstones are distributed in a sheet geometry throughout the study area (Fig. 3.10). The thickness of the sandstones on inherited paleo-uplifts, such as the central main horst zone, No. 10 fault zone and No.1 fault zone, is relatively large. The sandstones also occur to a varying degree in structural lows, such as Well T37, and margins of the main horst, such as Well T4. The occurrence of bituminous sandstones reflects the wide range fluid connectivity of Silurian sandstones at the end of early Paleozoic.

3.1.6 Quantitative Characterization of Conductivity of Carrier Bed The quantitative characterization of the conductivity of a carrier bed uses geological parameters, such as porosity, permeability, effective thickness, and hydrodynamic connectivity. Previous quantitative characterization of reservoirs mainly focuses on

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the description of the spatial distribution of reservoir porosity and permeability, and various algorithms for grid upscaling. We have used the relevant methods and techniques for reservoir characterization and also considered the differences between the carrier beds and reservoir beds. The knowledge on the geometric connectivity between different carrier bodies in a carrier bed makes quantitative characterization of conductivity of carrier bed relatively simple, similar to quantitative reservoir characterization. Many commonly used parameters for quantitative characterization of the reservoir are present, such as porosity, permeability, median pore-throat radius, capillary displacement pressure, storage coefficient (porosity multiplied by sandstone thickness), and seepage coefficient (permeability multiplied by sandstone thickness). Permeability is the first choice of characterization parameter, after analyzing the correlation between various reservoir parameters and hydrocarbon-bearing states in carriers, effect of hydrodynamic connectivity, and normalization of characterization parameter for different carriers, such as carrier beds and faults. An example is from the Dongying Sag. During the Cenozoic, the southern slope of Dongying Sag has experienced two periods of hydrocarbon charging (Jiang et al., 2003; Zhu et al., 2004), of which the Guantao-Minghuazhen time is the dominant period. Using the hydrocarbons from the two periods as markers, three diagenetic sequences are identified. To study the hydrocarbon migration and accumulation in the late Minghuazhen time, we need to consider the diagenetic product after this time. We documented the spatial distribution of various parameters related to hydrocarbon migration and accumulation in the period within the carrier bed to determine the paleo-hydrodynamic connectivity between sandstone bodies (Luo et al., 2010; Zhao et al., 2011). Using the method of reconstructing paleo-physical property of sandstone carrier bed (Chen et al., 2007; Lei et al., 2010), the paleo-porosity and paleo-permeability of sandstone bodies are estimated. Figure 3.11 shows the conductivity of the carrier bed in the middle part of the Third Member of the Shahejie Formation characterized by paleo-permeability. In summary, the basic procedures for model development and quantitative characterization of a carrier bed in the clastic MAUs are: (1) determine the carrier interval using various data and the understanding of hydrocarbon migration and accumulation characteristics, to estimate the thickness under the regional caprock; (2) construct a sandstone isopach map and a map of sandstone net-gross ratio in the carrier bed and quantitatively analyze the distribution of sandstone bodies under the constraint of sedimentary facies; (3) analyze the geometric connectivity of carriers, and use the discrete stochastic method to interpret the distribution and superposition of sandstone bodies in various sedimentary facies; (4) analyze the hydrodynamic connectivity of the carrier bed, use various paleo/present fluid indicators to modify the geometric connectivity model; (5) quantitatively characterize the conductivity of the carrier bed, and the parameters describing the porosity and permeability of the carrier bed can be used to characterize the heterogeneity of the carrier bed.

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Fig. 3.11 Quantitative characterization of conductivity in the middle part of the Third Member of Shahejie Formation. Gray color indicates areas of null conductivity. The green part is the geometrically-connected sandstones. The scale from dark green to light green represents the range of the conductivity

3.2 Quantitative Characterization of Fault Carriers In petroliferous basins, faults play an important role in hydrocarbon migration and accumulation (Berg, 1975; Fisher & Knipe, 1998, 2001; Galloway et al., 1982; Hooper, 1991; Knipe, 1992; Smith, 1966; Weber et al., 1978), because they can act as conduits or barriers (Boles & Grivetti, 2000; Boles et al., 2004; Eichhubl & Boles, 2000; Sample et al., 1993) during fluid migration. The dual behavior has been the focus of discussions over several decades (Losh et al., 1999; Sibson, 1981). Nevertheless, the role of faults in hydrocarbon migration still needs to be better understood. Through field observations, laboratory physical experiments and numerical simulation, previous researchers have studied the geometric characteristics, material composition, deformation processes, and diagenesis of the fault zones, and analyzed the opening and sealing of the fault through fluid activities related to the fault and the physical/chemical anomalies generated (Allan, 1989; Antonelini & Aydin, 1994; Engelder, 1974; Gibson, 1994; Knipe, 1992, 1997; Smith, 1966; Sorkhabi & Tsuji, 2005; Weber et al., 1978; etc.).

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A variety of approaches have been used to characterize the sealing ability of faults to study their impact on fluid flow. Perkins (1961) emphasized that the juxtaposition of sandstones may destroy the sealing capability of faults. However, he noticed that faulted sandstones may be sealed by mudstones squeezed into the sandstones during faulting. Allan (1989) stated that stratal discontinuity across a fault must be studied three dimensionally. Smith (1966) introduced some theoretical considerations on sealing and nonsealing faults based on the capillary model of Hubbert (1953). To characterize the sealing heterogeneity along fault planes, several parameters, such as clay smear potential, shale smear factor, and shale gouge ratio (SGR), were proposed to estimate the degree of mudstone smearing within fault zones and to evaluate the sealing capacity of faults (Bouvier et al., 1989; Lindsay et al., 1993; Yielding & Freeman, 1997). Using outcrop observations, Hasegawa et al. (2005) and Kachi et al. (2005) designed several seal capacity parameters to describe the threedimensional (3D) sealing characteristics of faults and emphasize the heterogeneity of fault-seal capacity. These detailed studies have pushed the concept of fault-seal capacity from qualitative toward quantitative (Sorkhabi & Tsuji, 2005). However, most of these previous studies are based on the present characteristics of a fault, whereas hydrocarbon accumulation involves the sealing characteristics of the faults during the entire past migration process. Indeed, this entire migration process may be very long because of the limited hydrocarbon migration flux through fault zones (Byerlee, 1993; Haney et al., 2005) and/or because of the very slow maturation of hydrocarbons in the source rocks. Because the formation and activities of faults are very complex, and the influencing factors of opening and sealing are diverse, the methods proposed by previous researchers often consider only one or two factors that cause fault sealing, which are not enough to summarize the heterogeneity and complexity of actual fault planes. Therefore, the methods effective in some areas are not applicable in other areas, even in another location of the same fault (Karlsen & Skeie, 2006), making it difficult to generalize them. In other words, the method to consider the comprehensive effects of various factors on the opening and sealing of faults is a challenge that must be overcome in the study of hydrocarbon migration. It is necessary to explore a suitable method to characterize fault opening and sealing and to effectively evaluate the transport effect of fault carrier in the process of hydrocarbon migration. We have abandoned the traditional research idea of fault sealing, and recognized that the commercial amount of hydrocarbon migration along the fault is attributed to the comprehensive geological process of multiple migrations during episodic faulting activities. We propose that the hydrocarbon accumulated in the two walls of the fault should be used to identify whether the fault interval has opened during the hydrocarbon migration process. Through opening and sealing analysis and parameter statistics on the fault plane point-by-point, the concept of fault connectivity probability is put forward, and the effectiveness evaluation of quantitative parameters of fault opening and sealing is carried out to obtain a multi-factor parameter model to characterize fault opening and sealing, and a new quantitative evaluation method of fault opening and sealing is established (Zhang et al., 2007, 2010, 2011, 2013). This method can more objectively quantify and characterize the heterogeneous

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characteristics of fault connectivity, and has been well generalized and applied in the quantitative research of tensile fault carriers in different regions (Luo et al., 2007b, 2013; Zhou et al., 2010).

3.2.1 Geological Factors Affecting Fault Opening and Sealing Whether a fault acts as a conduit or a barrier during hydrocarbon migration is controlled by many factors (Harding & Tuminas, 1989; Knipe, 1997; Knott, 1993; Zhang et al., 2011). The factors that can influence the opening and sealing characteristics of fault are numerous, most of them are illustarted in Fig. 3.12. Among them, some essential factors had in detail been discussed in Zhang et al. (2011), such as burial depth of a fault segment (Fig. 3.12d), dip angle of a fault plane (Fig. 3.12g), throw (vertical displacement) of a fault (Fig. 3.12e), strike of a fault plane (Fig. 3.12f), sandstone content of a faulted interval (Fig. 3.12i), and fluid pressure within mudstone (Fig. 3.12l). In fact, some other factors that may also play very important role to affect fault opening or sealing. Fault plane tightness: It is the key factor affecting the opening of faults (Lu & Ma, 2003). If the fractures in a fault zone are tight and the fault sealing is good, it is difficult for hydrocarbon to migrate along the fault plane. Otherwise, the fault will be open as a conduit for hydrocarbon migration. The fault plane tightness mainly depends on the magnitude of normal stress on fault plane. Larger normal stress reduces the porosity and permeability of rocks in the fault zone and even leads to fault closing (Fig. 3.12a). Fault activity: Active faults and fractures provide highly permeable conduits for fluid flow (Fig. 3.12b). Fault activity is an important factor causing fault opening (Jones & Hillis, 2003). In general, fault activity can usually be measured by the dislocation distance of one stratum on both sides of the fault during one episode of active faulting. Fault type: Generally, reverse faults and strike-slip faults are more likely to form seals than normal faults (Harding & Tuminas, 1989; Fig. 3.12c). Because the formation of normal faults is affected by the tensile stress in the dip direction of the fault plane, normal stress on fault plane is only derived from the gravity of the hanging wall strata. The reverse fault is formed in a compression stress field; and the normal stress on the fault plane includes not only the stress derived from the gravity of the hanging wall strata, but also the stress derived from tectonic compression. The increase of normal stress not only makes the fractures between fault walls tend to seal, but also may cause friction between strata in two walls, forming impermeable materials, such as fault gouge and mylonite, between the fault planes. When the tectonic stress is far greater than the gravity of hanging wall strata, similar changes will occur on the fault plane of strike-slip faults.

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Fig. 3.12 Schematic illustrations showing possible effects of some geologic factors on fault activities, which can be quantified using routine exploration data to characterize fault opening and sealing

Lithologic juxtaposition across the fault: When sandstone strata on both sides of the fault are juxtaposed, it is easy to form fluid flow conduits; and the fault sealability depends on the physical properties of rocks in the fault zone (Yielding & Freeman, 1997; Fig. 3.12h). When mudstone or other impermeable rocks with a high capillary force are juxtaposed with sandstones, the probability of hydrocarbon migration through the fault is greatly reduced (Allan, 1989; Smith, 1966). This type of sealing can be identified on a lithologic juxtaposition map of the two walls of the fault (Allan, 1989). Fault zone mudstone smearing: Argillaceous smear layer on fault planes can form when country mudrocks were incorporated into the fault zone. The smearing is the main factor causing fault sealing in clastic strata (Weber et al., 1978; Yielding & Freeman, 1997). When the faulted strata are mainly mudstone, the more mudstone content involved in the fault zone, the worse the porosity and permeability of the fault zone; the higher the expulsion pressure, the smaller the possibility of hydrocarbon migration (Fig. 3.12j). As mudstone compaction increases with depth, smeared layer in fault zone is more likely to occur at shallow depths where the sediments are not consolidated (Bouvier et al., 1989); and the sealing ability of the mudstone smears will also increase with burial depth.

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Cementation in fault zone: During the inactive period of a fault, mineral precipitation occurs due to changes in physical environment or fluid-rock interaction during the fluid flow along originally opening and permeable fractures (Fisher & Knipe, 1998). The cement makes the fault zone partially or completely lose pore space, eventually leading to the sealing of the fault zone (Fig. 3.12k). Cementation depends on the distance of fluid penetrating into rock, the amount of dissolved minerals in fluid and the fluid pressure anomaly during seepage. However, it is difficult to predict cementation sealing of the fault zone by simple mathematical methods (Knipe, 1992). From the above analysis, it can be seen that the opening or sealing of fault is affected by many geological factors. The role of each factor may also mingle with those of other factors. Therefore, the evaluation of fault opening and sealing has always been a challenge in petroleum geology research. It is necessary to determine the important parameters controlling fault opening and sealing from these factors, in order to conduct quantitative/semi-quantitative characterization of the fault connectivity during hydrocarbon migration.

3.2.2 Principles and Model of Quantitative Research on Fault Opening and Sealing In reality, active tectonics associated with faults are discontinuous processes (Hooper, 1991; Sibson, 1981), and faults open and close episodically as a result of a variety of geologic processes (e.g., earthquake cycles; Hooper, 1991; Sibson, 1981). Therefore, any comprehensive study of hydrocarbon migration through faults should take this dual behavior into account as well as its temporal evolution. Our approach in studying fault-sealing capacity is to consider the long-term hydrocarbon migration process as a cumulative effect of many small increments of migrating hydrocarbons through a segment of a fault zone, such as the migration episodes that occur during earthquakes. The presence of hydrocarbon accumulations in footwall and hangingwall reservoirs is therefore used as a calibration criterion indicating cumulative fault opening and closing cycles for migration over geological time. A method of quantitative assessment of 3D fault opening and closing at different locations on a fault plane is proposed, and the opening and closing behavior on a fault plane during the entire migration history may be semi-quantitatively described using a concept of fault connectivity probability. 1. Internal Characteristics of Fault Zones Outcrop observations and core analysis of fault zones have confirmed that the fault zone is not a simple physical surface, but a zone with a certain thickness and complex internal structure (Berg & Skar, 2005; Bouvier et al., 1989; Forster & Evans, 1991). The thickness and structural characteristics of fault zones change greatly, and the width of a fault zone may range from tens of centimeters to tens of meters, for example, the fault structure in the northern wing of Tugulu Anticline observed at

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Fig. 3.13 Photo showing the fault structure developed in the northern wing of Tugulu Anticline, exposed on the west bank of Taxi River

the outcrop on the west bank of Taxi River in the southern margin of Junggar Basin (Fig. 3.13). The width of the fault zone is about 6–8 m; fissures on the hanging wall of the fault are well developed; and hydrocarbon intrusion is found along the fissures. Under the action of hydrocarbon impregnation, the red stratum appears to be fading (yellow-green). Due to the relative dislocation of the two walls of the fault, the deformation bandwidth along the fault core formed by friction is only about 20 cm, and fault gouge is developed inside it. Generally, a relatively complete fault zone includes a strongly deformed fault core, damage zone and its surrounding undeformed host rock (Caine et al., 1996; Chester et al., 1993; Fig. 3.14). The fault core represents the part of a fault zone where most of the displacement is accommodated and consists of slip surfaces, fault gouge, breccias, and cataclasites (Caine et al., 1996; Chester & Logan, 1986; Chester et al., 1993; Clausen et al., 2003). The damage zone may be composed of subsidiary faults, faulted rocks, and fractures (Bruhn et al., 1994; Caine et al., 1996; Chester & Logan, 1986; Chester et al., 1993; Jourde et al., 2002). Fault movement is mostly episodic (Byerlee, 1993; Hooper, 1991; Xie et al., 2001): stress accumulating over a period would create new faults and/or cause the movement of preexisting faults, resulting in a sudden release of stress and fluids. Faulting would cease once stress and strain reach equilibrium, and another cycle of stress accumulation would follow (Roberts & Nunn, 1995). In general, faults are open during faulting and can serve as vertical and lateral hydrocarbon migration

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Fig. 3.14 Conceptual structural model of a fault zone (modified from Zhang et al., 2010)

pathways (Anderson et al., 1994; Hooper, 1991), whereas faults are generally closed during the period of quiescence and would serve as barriers to hydrocarbon migration (Bouvier et al., 1989; Fowler, 1970). In fact, a fault zone is not entirely open during faulting; instead, it is open in some segments or places and closed in others (Haney et al., 2005). Fault closure during faulting may be caused by several processes: for example, ductile strata matching across the fault zone, sealing by fault gouge, smearing of mudstones, or enclosure of a segment of an open fault zone by lowpermeability strata across the zone and closed fault zones above and below, resulting in an increase of the length and complexity of pathways for fluid flow (Byerlee, 1993; Lockner & Byerlee, 1995). When faulting ceases, some of the open fractures close due to a change in stress field, whereas other open fractures are cemented or welded by precipitates from trapped fluids (Wästeby et al., 2014). The cementation is very fast and can be regarded as instantaneous once faulting ceases at a geologic time scale (Caputo & Hancock, 1999; Claesson et al., 2004). Veins, filled with quartz and calcite of different periods, are commonly distributed throughout fault zones, indicating that the fault zones have experienced multiple episodic fluid flows. Therefore, the formation of a fault is often not the result of a single tectonic activity, but the result of the accumulation by numerous smallscale activities. These small-scale activities and fluid migrations often correspond to seismic activities (Rojstaczer et al., 1995; Tokunaga, 1999). Because of the very long geological time of hydrocarbon generation in source rocks, the hydrocarbon migration in fault zone should also occur over a considerable geological period. Comparing with this long migration process, the time of faults acting as hydrocarbon migration conduits is very short (Hooper, 1991). The hydrocarbon distributions related to fault carrier observed at the present are the result of numerous fault activities: fault opening-fluid flow-fault sealing-static fault (Anderson et al., 1994) processes. Therefore, it is unrealistic to regard hydrocarbon migration along a fault as the result

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of a specific fault event and to attribute the opening and sealing characteristics of the fault and the corresponding hydrocarbon flow to a single physical process. The study of fault carrier should choose an appropriate time scale to analyze the fault activity, rather than focusing on the dynamic characteristics of fluid passing through the fault zone at a specific moment (such as a specific earthquake). On the other hand, the internal structure of a fault zone is commonly very complex. The faults that play important roles in the process of basin fluid activities are composed of a series of small faults and fissures. Fluid activities along the fault zones occur in a three-dimensional space; and not all fracture surfaces and fissures are open to form fluid flow conduits during each faulting. The observed fluid activities are the results of the combined actions of all fracture surfaces, fissures, fracture caves and permeable rock blocks in the fault zones (Claesson et al., 2007). Therefore, when analyzing opening and sealing characteristics of a fault at a basin scale, the entire fault zone should be considered as an entirety. Regardless of the scale and characteristics of fault development, absolute certainty on sealing or no sealing is impossible (Lu et al., 1996). For hydrocarbon migration, we pay attention not only to whether hydrocarbon can migrate from the carrier bed in one wall to that in the other wall of the fault, but also to whether hydrocarbon can enter the fault zone and continue to migrate along the fault zone. Generally, as long as the migration dynamic conditions permit, the hydrocarbon migrating in the fault zone may enter the carrier bed with relatively good permeability at any position and continue to migrate or accumulate in it. Figure 3.15 shows a conceptual model of the relationship between fault activities and hydrocarbon migration, which represents the comprehensive geological process of “fault opening-hydrocarbon migration-static fault sealing” in a specific geological period. 2. Identification of Fault Opening and Closing A good understanding of the geometric elements and influencing factors of a fault is the basis for determining the quantitative parameters to characterize fault opening and closing, as well as for establishing effective evaluation methods. Therefore, it is necessary to first identify which faults play an opening role and which ones play a sealing role in the process of hydrocarbon migration. In reality, it is very difficult to accurately identify the opening and sealing of faults in the process of hydrocarbon migration. This can be done by identify the difference in the oil–water interface or pressure between two sides of a fault (Yielding & Freeman, 1997). However, these observations can only reflect the lateral leakage information of present vertically sealed faults; and the interpretations are non-unique. It is not accurate and complete to identify, with such observations, whether a fault acts as a hydrocarbon migration conduit in the past. When a fault opens, the flow of fluid along the fault zone inevitably leaves various traces, including scattered diagenetic minerals, fluid inclusions in veins and surrounding rocks of fault zones. To use these

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Fig. 3.15 Conceptual model of fault activities and hydrocarbon migration. Blue curve indicates the change of fault zone permeability corresponding to each earthquake activity during the fault active period (T); and the red curve indicates the volume change of migrated hydrocarbon

traces as indicators to determine whether the fault was open (Sorkhabi & Tsuji, 2005), a large number of samples need to be collected in and near the fault zones. Such work may be feasible in the outcrops, but almost impossible in the basin, because cores in fault zones are very limited. In fact, since hydrocarbon migration along faults is affected by many geological factors, even in mature fields, the opening and sealing analysis of faults is nearly impossible. Therefore, we propose some general approach to characterize the complex fluid behavior in the fault zone. The temporal and spatial relationship of hydrocarbon reservoirs near the fault can be used to infer fault connectivity and the geological parameters that affect the opening and sealing of the fault, by using geostatistical methods. The optimal parameters are selected to establish a probability model to characterize the opening and sealing of the fault. To achieve this goal, with an understanding of fault activity, internal structure of fault zone and fluid flow characteristics, we (Zhang et al., 2007, 2010) proposed that the fault connectivity in the process of hydrocarbon migration can be interpreted from whether there are hydrocarbons in the reservoirs in the two walls of a fault. Only the data that can be used to determine whether the fault is open or sealed is used in the statistical analysis, avoiding many uncertain factors. This method has achieved good results in its application to the Chengbei Fault-step Zone of Dagang Oilfield (Zhang et al., 2007). In the application, we selected a mature exploration area where faults that once played a critical role in migration and accumulation, and collected data in the stratigraphic intervals where many wells have penetrated both walls and have many reservoirs. The intersection line between a bed and fault plane is taken as the horizontal line, and that between the fault plane and the cross section perpendicular to the fault plane as the vertical line. Following this set up, the fault plane is gridded. The size of the grids depends on data resolution, thickness of reservoir beds in the target interval,

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and the amount of seismic and well data near the fault. Subsequently, data of various geological parameters related to fault opening and sealing are obtained at each node. On a cross section perpendicular to the fault plane, grid nodes are at the top of each bed in both the hanging wall and the footwall (Fig. 3.16a). Assuming that the fault is the only possible vertical migration path, the opening and closing state of such a node on the fault during the migration period is determined by identifying the hydrocarbon presence or absence in reservoirs above that node on both sides of the fault plane. Four possible scenarios may occur and are used to identify whether the node had served as a migration path. The four scenarios can be described as follows (Fig. 3.16b): (1) the reservoirs below contain hydrocarbons and the ones above do not (the case of point D on Fig. 3.16b); (2) reservoirs both above and below the node contain hydrocarbons (the case of point C on Fig. 3.16b); (3) the reservoirs above the node contain hydrocarbons and those below the node do not (the case of points A and B on Fig. 3.16b); and (4) reservoirs both above and below the node do not contain hydrocarbons (the cases of points E and F on Fig. 3.16b). It is not difficult to assess that, in scenarios 2 and 3, the node must have been open at some time during migration; on the contrary, in scenario 1, no hydrocarbon accumulation above this node exists at present, and thus, the node was probably closed during migration. In the last scenario, it is unknown whether the node was open or closed during migration. In establishing the statistical model of fault opening and closing, only the presence and absence of hydrocarbons in reservoirs above and below a given grid node, along the same vertical plane, were considered. Fluid flow is, in reality, three dimensional, and fluid may flow horizontally between grid nodes. Moreover, some open microcracks in flexure zones of extensional basins could divert part of the

Fig. 3.16 Determination of fault connectivity on the basis of hydrocarbon occurrence. N i is defined as the fault connectivity index. For a given node, N i = 1 indicates the fault has opened for migration, N i = 0 indicates the fault kept closing, and N i = ? means the effect of the fault cannot be identified and the node will not be considered in statistical analysis

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migrating flow. This could introduce erroneous interpretation of reservoir leakage or accumulation. Another bias could be introduced by the fact that stresses and fluid pressures are obtained from present measurements, whereas migration occurs over long periods during which these physical parameters could have varied. Therefore, the various inaccurate interpretations could introduce unpredictable errors to the proposed analysis and, thus, justify a probabilistic approach.

3.2.3 Concept of Fault-Connectivity Probability It is impossible to determine the hydraulic connectivity for hydrocarbon migration at every node on a fault plane, even in mature exploration areas. However, a statistical treatment of available data points may be used to characterize the hydraulic connectivity of faults as a whole. Fault-connectivity probability serves as a parameter that links fault connectivity with some controlling parameters. Fault-connectivity probability (N p ) for hydrocarbon migration is defined as the ratio of the number of hydraulically connected nodes (n) to the total number of effective nodes (N): Np = n/N

(3.2)

Commonly the range of values of a parameter at all effective nodes on all fault planes is subdivided into several intervals. In each interval, the value of N p is calculated from (2) and plotted at the mid-point of the interval over the entire range of the parameter to illustrate the variability of N p as a function of the corresponding parameter (Zhang et al., 2011). Using the Chengbei Fault-step Zone as an example, the statistical analysis method of fault-connectivity probability is illustrated (Fig. 3.17). Burial depth values at 117 nodes range from 1000 to 4000 m. The range is subdivided into nine intervals with a spacing of 250 m (Fig. 3.17a). Although a quadratic relationship between the burial depth and Np is quite possible, the fitting trend is still poor with a low correlation coefficient of 0.25 (Fig. 3.17b). However, the overall trend that fault-connectivity probability decreases with increasing depth is consistent with qualitative empirical geological interpretation. Note that if the first point located at the 1500 m interval and the last one located at the 3300 m interval are discarded in the regression, the correlation coefficient increases significantly to 0.93 and the fitting curve becomes much more consistent with what is expected (Fig. 3.17b). The validity of the relationship between a parameter and N p is artificially influenced by the subdivision of the range of values of the parameter. If the width of the interval is too large, the number of intervals is limited and the regression relationship is not significant. On the contrary, narrow intervals may limit the number of data points within some intervals and the N p values of those intervals is not statistically representative and the observed trend is poorly defined. These rules are demonstrated using examples of burial depth (Fig. 3.18). If the interval is small (100 m), the number of data points in some intervals is too small

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Fig. 3.17 Example of statistical analysis of fault-connectivity probability. a Histogram of the number of opening nodes and the number of all effective nodes on all fault planes in the study area tabulated for individual intervals of buried depth, as an example of subdividing the value range of a parameter and calculating fault-connectivity probability (N p ) of each interval. b Relationship between N p and burial depth (BD). The value of N p in each interval is plotted at the mid-point of the interval. A statistical relationship is established through regression. The solid regression line is obtained using all data points, whereas the dashed line without using two end points. See text for discussion

(Fig. 3.18a), whereas the number of data points is adequate for large intervals of 200, 300, and 400 m (Fig. 3.18b–d). However, in all these cases, the correlation coefficients are much smaller than that of the case in Fig. 3.17b, where the interval width is 250 m. The correlation coefficient of 0.25 is the largest among many subdivision schemes. The dependance on interval width in establishing the relationship between a parameter and N p emphasizes the arbitrariness of this approach in assessing the effectiveness of this parameter in characterizing fault connectivity for migration. Our empirical rules for optimal subdivision schemes are: (1) the width of the interval should be as small as possible, as long as the number of data points within an interval is statistically reasonable (i.e., n > 5); and (2) the correlation coefficient of a relationship established through regression is the largest among all possible subdivision schemes. These rules are demonstrated using examples of burial depth (Fig. 3.18). Generally, there may be four correlation models between various geological parameters and fault connectivity probability (Fig. 3.19). In Fig. 3.19, the step function relationship represented by curve A reflects that this parameter is the best characterization parameter of fault opening and sealing: If the parameter value is less than a threshold, the fault connectivity probability is 0, indicating fault sealing; If the value is greater than the threshold, the fault connectivity probability is 1, indicating fault opening. If the fitting curve is similar to curve B, that is, the fault connectivity probability does not change with this parameter, it reflects that this parameter is invalid. In reality, because fault connectivity is affected by many factors that cannot be characterized by a single parameter, the relationship between most geological parameters and fault connectivity probability should be similar to curve C, while curve D may represent the best characterization parameter in practice.

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Fig. 3.18 The relationship between fault-connectivity probability (N p ) and burial depth (BD). The four charts are for various interval width of burial depth

Fig. 3.19 Possible scenarios of the relationship between a parameter and fault connectivity probability (N p ), as references to determine whether a geological parameter is effective in characterizing fault connectivity for migration

3.2.4 Effectiveness Evaluation of Parameters for Characterizing Fault Opening and Sealing To quantitatively characterize the opening and sealing of faults, it is necessary to select appropriate geological parameters. However, due to the differences in the influencing factors of fault opening and sealing in different regions, before using specific geological parameters to quantitatively characterize fault opening and sealing, the parameters should be evaluated with the geological conditions of a study area. As

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shown in Fig. 3.17b, in the relationship between burial depth and opening probability, the fault opening probability decreases with burial depth in the range from 1500 to 3250 m. It is conceivable that the greater the burial depth, the larger the normal stress on the fault plane, and the fault tends to be sealing. However, for the ranges of burial depth less than 1500 m and greater than 3250 m, it does not conform to the above rule, indicating that there are other influencing factors. A case study is described below. Based on the oilfield data from the Chengbei StepFault Area in the Nikou Depression, Bohai Bay Basin, NE China, (Zhang et al., 2007, 2010), using the fault connectivity probability method, we analyze and compare the fault opening and sealing characterized by various geological parameters, and evaluate the effectiveness and representativeness of these geological parameters. Thereby, the best parameters to characterize the fault opening and sealing for hydrocarbon migration and accumulation can be identified. 1. Geological Background of Chengbei Step-Fault Area The Chengbei Step-Fault Area (CSFA) is located on the slope between the Qikou Depression and the Chengning Uplift in the Bohai Bay Basin, NE China (Fig. 3.20). The structural sketch of the study area consists of a gentle slope cut by multiple step-like faults (Fig. 3.20a). There are two major tectonic phases in the study area: a Paleogene syn-rift phase and a Neogene to Quaternary post-rift phase (Li et al., 1998; Wang et al., 2003). The Cenozoic strata are composed of continental clastic deposits, with a total thickness of 2000–5000 m. The Paleogene deposits include the Shahejie and Dongying formations (Fig. 3.20b); the former contains lacustrine and fan delta deposits and the latter contains lacustrine and deltaic deposits. The Neogene to Quaternary succession consists of fluvial deposits of the Guantao, Minghuazhen, and Pingyuan formations (Fig. 3.20b). A regional rifting-drifting unconformity separates the Paleogene and Neogene strat (Yuan et al., 2004). Hydrocarbons in the CSFA mainly accumulated in the thin sandstones in Sha-3, Sha-2, and Sha-1 members of Shahejie Formation, Guantao Formation, and Ming-2 Member (Fig. 3.21). Dark colored mudstones of Sha-3 and Sha-1 members in the Qikou Depression are the source rocks, and the strata above have no hydrocarbongenerating potential (Wang et al., 2006). Thus, faults are key conduits to vertical hydrocarbon migration into reservoirs of variable ages (Wang et al., 2006; Yu et al., 2006); and multiple oil-bearing reservoirs are vertically stacked and have frequently different hydrocarbon-water contacts (Fig. 3.21). Basin modeling studies of Yu et al. (2006) and Wang et al. (2006) indicate that hydrocarbons were expelled from source rocks during two major episodes. The first episode was at the end of Dongying time (~26 Ma) when source rocks in the Sha-3 Member became mature. Hydrocarbon expulsion ceased because of regional uplifting and erosion at the end of Paleogene. Therefore, this early episode of hydrocarbon expulsion is short and the amount of expelled hydrocarbon is limited. Major expulsion occurred during the second episode from late Miocene to Quaternary (Wang et al., 2006). The deposition of the Neogene Guantao and Minghuazhen formations caused maturation and hydrocarbon generation in the Sha-3 and Sha-1 source rocks. Hydrocarbon expulsion peaked at the end of Minghuazhen time (~2 Ma). And the

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Fig. 3.20 Diagram showing the tectonic location, oilfield distribution and strata in Chengbei Faultstep Zone. a Location of Bohai Bay Basin and the study area (red square) in NE China; b structural map of the Chengbei step-fault area showing faults on top of the Shahejie Formation, oil fields (green area), key wells used in this study (black dots), other wells (black circles), and location of cross sections discussed in the text (red lines); c stratigraphy of the study area, after Yuan et al. (2004)

Fig. 3.21 Structural cross section illustrating the stratigraphy, faults, and discovered hydrocarbon zones. See Fig. 3.20a for location

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181

studied area has kept subsiding from then without significant changes in tectonics (Wang et al., 2003). Thus, the CSFA is particularly suitable for using hydrocarbons as an indicator of fault opening and closing, since hydrocarbons in the CSFA may be considered to be accumulated during the period from Miocene to Pliocene (Yu et al., 2006). In addition, more than 140 exploratory wells have been drilled, providing a large quantity of well and production data. The entire CSFA area has been covered by 3D seismic survey, which has been interpreted at a grid resolution of 25 × 25 m for main structural layers. The abundant exploration data greatly facilitate acquisition of various geological parameters to aid in our study of fault connectivity. 2. Acquisition of Characterizing Parameters of Fault Opening and Sealing According to the fault opening and closing analysis method presented in the previous section, the analyzing work was carried out in the CSFA. The work flow is summarized as follows: (1) Establish the fault plane grid model. (1) The faults that play an obvious role in hydrocarbon migration and accumulation are determined. (2) Then drilling wells in the two walls of all faults, especially the fault intervals with more hydrocarbon bearing are selected. (3) The seismic data are interpretated and geological sections perpendicular to the fault strike are drawn. (4) The strata and fault framework of the designated migration period are restored by the balancedsection technique, and the fault plane grids composed of stratal interfaces—fault intersection lines and seismic section—fault intersection lines are established. Figure 3.22 shows the fault plane grid model of Zhangdong-H4 Well fault in Chengbei Fault-step area, at the end of Minghuazhen Formation sedimentation period. (2) Characterizing the fault opening and sealing. Sixteen reservoir sections perpendicularly passing through major hydrocarbon-controlling faults (Fig. 3.20) were

Fig. 3.22 Structural block diagram of the footwall of fault ZDH4. The positions of stratigraphic intervals are marked as solid lines on the footwall and as dashed lines on the hanging wall

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3 Hydrocarbon Conduit System and Its Quantitative Characterization

selected, and the opening and sealing of fault grid nodes are identified by using the previously proposed opening and sealing characterizing method according to the hydrocarbon distribution in the two walls of the faults and the genesis types of crude oil. The binary value is used to represent the indicator N i of fault connectivity, 0 represents sealing and 1 represents opening. (3) Obtaining the geological parameters: Along a cross section, a stratigraphic surface has two intersection points with the fault plane, one on the upthrown side, the other on downthrown side. Eight formations with eight surfaces are taken into account: bases of Upper and Lower Minghuazhen Formation (Nm1 , Nm2 ), Guantao Formation (Ng), Dongying Formation (Ed), Sha-1 Member (Es1 ), Upper Sha-2 Member (Es1 2 ), Lower Sha-2 Member (Es2 2 ), and Sha3 Member (Es3 ) (Fig. 3.20c, d). Thus, the total number of grid nodes along an intersection line is usually 16 on most sections (e.g., Fig. 3.22). Values of many parameters (burial depth, fault dip angle, fault throw, fault strike, sandstone content) at a node are obtained relatively easily from interpreted seismic sections and wireline logs, structural contour maps, and sandstone content maps. Fluid pressure in mudstone adjacent to the fault plane may be estimated by using 3D basin modeling (Zhang et al., 2010) (4) Data screening. In order to avoid many uncertain factors in the identification process, the observation data that cannot determine connectivity are abandoned, and a total of valid data on 117 nodes are obtained in the study area. The data set of each node contains a wide range of values of geological parameters and their corresponding fault connectivity characterizing values. 3. Effectiveness of Selected Parameters in Characterizing Fault Connectivity In order to evaluate the effectiveness of various parameters in evaluating fault connectivity, it is necessary to test different parameters, according to the above-mentioned connectivity probability statistics method, to find the best representative parameter(s). Zhang et al. (2011) investigated many parameters, statistically analyzed the relationship between them and fault connectivity probability (N p ) (Fig. 3.23). Their analyses indicate that single geological parameters are not effective in assessing fault connectivity during migration. Composite parameters, that combine two or more single geological parameters associated with hydraulic properties of fault zones (e.g., Bouvier et al., 1989; Lindsay et al., 1993; Yielding & Freeman, 1997), may be more effective in characterizing complex and heterogeneous hydraulic connectivity within fault zones. The effectiveness of some composite parameters is investigated as below. (1) Shale Smear Parameters The shale content in the faulted strata and fault displacement have been speculated as important factors controlling shale smearing (e.g., Lindsay et al., 1993; Weber et al., 1978; Yielding & Freeman, 1997). Several parameters considering the two factors have been proposed: Clay Smear Potential (CSP; Bouvier et al., 1989; Fulljames et al., 1997), Shale Smear Factor (SSF; Lindsay et al., 1993), Shale Gouge Ratio (SGR;

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183

Fig. 3.23 Relationships between fault-connectivity probability N p and fault dip angle (FDA) (a), sandstone content (SC) (b), fault throw (FT) (c), fault strike (FS) (d), and fluid pressure in mudstone (FP) (e). These weak and moderately strong relationships indicate that single geologic parameters may not be effective in characterizing fault connectivity for hydrocarbon migration. See text for discussions

Fristad et al., 1997; Yielding & Freeman, 1997), and Smear Gouge Ratio (SMGR). Among them, the SGR of Yielding and Freeman (1997) can be used to quickly and accurately estimate the content of shaly material within the fault zone, especially for heterogeneous siliciclastic strata, and is widely used (Sorkhabi & Tsuji, 2005). SGR is defined as the ratio between total thickness of shale in the faulted segment and the fault throw (Fig. 3.24a):

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3 Hydrocarbon Conduit System and Its Quantitative Characterization

Fig. 3.24 a Concept of shale gouge ratio (SGR); b relationships between fault-connectivity probability N p and SGR n 

SGR =

hi

i=1

L

× 100%

(3.3)

where SGR is Shale Gouge Ratio, %; L is the fault throw, m; hi is the thickness of mudstone stratum i dislocated by fault, m; n is the number of mudstone strata dislocated by the fault. Therefore, SGR involves not only the lithology of faulted strata, but also the fault displacement, which is an ideal parameter reflecting the opening and sealing capability of the fault. Mudstone thickness data can be obtained by interpreting gamma logging curves of wells near the faults. 117 SGR values are divided into 10 intervals with a 0.1 spacing. The relation between SGR and Np is similar to the curve D in Fig. 3.19. A statistically significant negative quadratic relationship between SGR and Np with a correlation coefficient of 0.89 is obvious within the interval of SGR value from 0.15 to 0.9 (Fig. 3.24a). The relationship indicates that a shale smeared fault segment is likely to be closed. The probability of a fault opening as migration pathway is high when SGR value is smaller than 0.45, but is extremely low when SGR value is greater than 0.70. (2) Stress Normal to the Fault Plane Several parameters, such as burial depth, dip angle and strike of a fault plane, direction and magnitude of tectonic stress are related to mechanical stresses arising from the overlying strata and tectonic stress field on a fault plane. With respect to fault connectivity, the essential factor represented by these parameters is the effective component stress normal to the fault plane. Large normal stress tends to cause plastic rock deformation during faulting to close fractures (Harding & Tuminas, 1989); otherwise, fractures may stay open and serve as conduits to fluid flow (Zhou et al., 2000). For any stress field, the normal component of the stress acting on a fault plane is (Jaeger & Cook, 1979):

3.2 Quantitative Characterization of Fault Carriers

σN = (sin α · sin θ )2 σH + (cos α · sin θ )2 σh + cos θ 2 σv

185

(3.4)

where σN is the stress normal to the fault plane in MPa; θ is fault dip angle; α is the angle between fault strike and the direction of maximum horizontal principal stress; σH , σh and σv are maximum horizontal principal stress, minimum horizontal principal stress, and vertical stress, respectively. The current stress field in the study area has been studied by Xu et al. (1996) and Wan (1993) using wellbore breakout and hydro-fracturing data from about 50 wells. The maximum stress is vertical and the direction of maximum horizontal principal stress is 55°–80°. The values of the horizontal principal stresses, σH and σh , following the burial depth, were estimated from the measurements of Xu et al. (1996). The fault strike, dip angle and burial depth can be obtained as described above. The values of d at 117 nodes are divided into eight intervals with an increment of 5 MPa. A dominantly negative quadratic relationship between d and N p is evident as indicated by a correlation coefficient of 0.85 (Fig. 3.25b). Faults are more likely closed when the stress normal to the fault plane is larger. The robustness of this relationship using composite fault-related parameters is much better than any of those using single parameters (including burial depth, fault dip angle, and fault strike) (Fig. 3.23). 1. Fault Opening Coefficient and Its Use in Characterizing Fault Opening and Closing (1)

Definition of Fault Opening Index

The aforementioned discussions of correlation between various geological parameters and connectivity probability show that a single geological parameter has many

Fig. 3.25 a Schematic diagram of obtaining normal stress parameters of section; b relationship between fault-connectivity probability N p and stress normal to the fault plane

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3 Hydrocarbon Conduit System and Its Quantitative Characterization

limitations in characterizing fault opening and sealing, while comprehensive parameters such as fault plane normal stress and SGR contain multiple factors, so the effectiveness of characterizing fault opening and closing is greatly improved. Therefore, the quantitative study of fault opening and sealing should adopt the strategy of integrating effective geological factors, and the influence of many key factors should be comprehensively considered for selecting characterizing parameters. The strong relationships between N p and fluid pressure in mudstone, normal stress perpendicular to fault plane, and shale gouge ratio indicate that the three parameters are effective in assessing fault connectivity during migration. They are incorporated into yet another composite parameter, the fault opening index (FOI). It is defined as a dimensionless coefficient (Zhang et al., 2010): FOI =

P δ · SGR

(3.5)

FOI is the ratio between factors favoring fault opening, represented by the fluid pressure in mudstone (P), and factors promoting fault closure, represented by shale gouge ratio (SGR) and stress normal to the fault plane (δ). Generally, the higher the FOI value, the higher the fault opening possibility. (2) The Relationship Between Fault Opening Index (FOI) and Connectivity Probability Based on the data of Chengbei Fault-step Zone in Dagang Oilfield, the values of FOI are calculated from the values of the three parameters. They range from 0.5 to 5 and are subdivided into nine intervals with an increment of 0.5. As the relation between FOI and N p is also similar to the curve D in Fig. 3.26, the relationship may be expressed as following: ⎧ 0 FOI ≤ 0.75 ⎪ ⎪ ⎨ NP = −0.1197 · FOI2 + 0.8931 · FOI − 0.6564 0.75 < FOI < 3.25 ⎪ ⎪ ⎩ 1 FOI ≥ 3.25

(3.6)

Np is zero when FOI is smaller than 0.75; N p increases from zero to 1 following a quadratic relationship when FOI changes from 0.75 to 3.25; and Np is 1 when FOI is larger than 3.25. It is apparent that the integrated parameter FOI composed of normal stress of fault plane, mudstone fluid pressure on both sides of fault and Shale Gouge Ratio (SGR) can better characterize the fault opening and closing. The fault connectivity probability characterized by FOI can be continuously and completely distributed between 0 and 1, which can be used to identify the possibility of fault opening readily. As shown in Fig. 3.16, this relationship is equivalent to model D, which is the most ideal result that can be obtained to characterizing fault opening and sealing on the current geologic data.

3.2 Quantitative Characterization of Fault Carriers

187

Fig. 3.26 Relationship between FOI and N p in the Chengbei Step Fault zone, Bohaiwan Basin

Zhou et al. (2010) studied the opening and sealing characteristics of tensile faults in the Jurassic in the Mosuowan area in the hinterland of Junggar Basin, NW China. The relationship between Fault Opening Index (FOI) and fault connectivity probability can be fitted; and a statistical relationship model consistent with that in Chengbei Fault-step Zone (Fig. 3.27) is obtained. However, due to the differences in geological conditions and fault activity characteristics between the two areas, the FOI threshold range and the regression function of fault connectivity probability are slightly different. As to be shown in the following chapter, in Dongying Sag, Jiyang Depression, and Bohai Bay Basin, the same relationship between Fault Opening Index (FOI) and fault connectivity probability is present. This indicates that the integrated-parameter model to characterize fault connectivity probability has certain universality based on oilfield observations and empirical fault opening-sealing identifications. (3) Effectiveness Test of Parameter Model

Fig. 3.27 Relationship between FOI and N P in Mosuowan area in the hinterland of the Junggar Basin (Zhou et al., 2010)

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3 Hydrocarbon Conduit System and Its Quantitative Characterization

In order to test whether the fault opening and sealing characterization model established by FOI parameter is effective, the fault reservoir section in Chengbei Fault-step Zone is used for testing. The data on the two sections passing through of Zhaobei Fault and Yangerzhuang Fault are not included in the statistical analysis in Fig. 3.27. The data on these two sections were used to test the fault opening and sealing characterization model. Figure 3.28 shows the test results. As shown in Fig. 3.28a, the connectivity probability of the section of Well Z36-Well Z42 is greater than 0.40 at the nodes corresponding strata underlying the Dongying Formation on fault plane, while the connectivity probability at the node corresponding to the bottom boundary of Dongying Formation on fault plane is only 0.08. Thus, the latter should be a largely sealing interval. Although the connectivity probability at the node corresponding the bottom boundary of Guantao Formation on fault plane is also high, the hydrocarbons from the Shahejie Formation cannot continue to migrate upward, and can only enter the Shahejie Formation in upthrown wall. At present, hydrocarbons have been found only in the First Member and Third Member of Shahejie Formation in this area, not in Dongying Formation and Minghuazhen Formation. On the section of Well Z5-Well Z4 × 1 passing through Yangerzhuang Fault (Fig. 3.28b), the connectivity probabilities at nodes on the fault corresponding to the bottom surfaces of the Third Member of Shahejie Formation, the First Member of Shahejie Formation and Dongying Formation are 0.93, 0.91 and 0.55, respectively. The connectivity probabilities corresponding at nodes on the fault corresponding to the First Member of Shahejie Formation and Dongying Formation are 0.83 and 1.0, while the connectivity probabilities corresponding to Guantao Formation and Minghuazhen Formation are only 0.37 and 0.02. Therefore, it can be concluded that the fault should be sealed at the intervals corresponding to the Guantao Formation. So hydrocarbons from the footwall migrated and accumulated into the First Member of Shahejie Formation on the hanging wall, since the content of sandstone in Dongying Formation is very low. Up to date, hydrocarbon has been recovered in

Fig. 3.28 Testing results of the characterization model of fault opening and sealing in reservoir cross sections. The faults in the figure are shown as black thick curves. The probability values of fault opening at different positions on an important source fault plane in the middle of the section are marked. The oil-bearing reservoirs in the formation are represented by red thin layers

3.2 Quantitative Characterization of Fault Carriers

189

the First Member of Shahejie Formation in this area, but not in Guantao Formation and Minghuazhen Formation. The test results of these two section demonstrate that the accuracy of the fault connectivity probability model is 100%.

3.2.5 Quantitative Characterization of Fault Carrier After determining the multi-parameter quantitative characterization model of fault opening and sealing, the FOI parameter and fault connectivity probability of each node on the whole fault plane grids are calculated from the geological data of hydrocarbon exploration. On the fault connectivity probability distribution map on the fault plane, the opening and sealing characteristics at different positions on the fault plane can be quantitatively characterized. This will be useful to quantitatively assess the conductivity of fault carriers in poorly-drilled areas. 1. Quantitative Characterization of Fault Opening and Sealing by Connectivity Probability Various composite parameters and fault connectivity probabilities are calculated on the fault plane and various contour maps can be constructed to quantitatively characterize the opening and closing characteristics at different positions on the fault plane. Using the Zhangdong-H4 Well fault in Chengbei Fault-step Zone as an example. Based on the reservoir characteristics and seismic data near the fault, we first divide the fault plane into 30 × 20 grids, and use 30 seismic sections passing through the fault, mud log, acoustic log and in-situ stress test data in wells near the fault to calculate the mudstone fluid pressure, compressive stress on fault plane, Shale Gouge Ratio (SGR) and Fault Opening Index (FOI) on the grid nodes. The values are then used to construct isoline maps on the fault plane. In Fig. 3.29, various contour maps on the fault plane are projected respectively on a vertical section to facilitate further analysis. The proven locations of hydrocarbon-bearing beds in adjacent wells are marked on Fig. 3.29d. It appears that the Shale Gouge Ratio (SGR) and Fault Opening Index (FOI) are effective to characterize the fault conductivity (Fig. 3.29c, d). Figure 3.30 is the characterization of the conductivity of the Zhangdong-H4 Well fault with the fault connectivity probability value. The connectivity probability at different positions of the fault plane is quite different. In the western part near Well H12, the connectivity probability of the Shahejie Formation at footwall is high, generally above 60%, whereas in the part between XH1 Well and XH6 Well the probability is small. The probability of the First Member Shahejie Formation-Dongying Formation at the footwall is extremely low, which suggests fault sealing, so that the hydrocarbons from the carrier bed of the Shahejie Formation at the footwall cannot continue to migrate upward, but tend to migrate toward the lower carrier beds, such as the Second Member and the Third Member of Shahejie Formation. Up to date, no

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3 Hydrocarbon Conduit System and Its Quantitative Characterization

Fig. 3.29 Topological maps of fluid pressure of mudstone (a), Shale Gouge Ratio (b), normal stress (c) and FOI (d) on the plane of Fault ZDH4. The solid curves are the intersection lines with the strata the footwall, and the dotted curves are the intersection lines with the strata at the hanging wall

hydrocarbon has been discovered in the Neogene in this area. In the area east of Well H12, in addition to the high connectivity probability of the Shahejie Formation at footwall, the area of the Third Member of the Shahejie Formation-Guantao Formation at hanging wall also has a high connectivity probability (>50%). The upper part of the fault plane exposing the Lower Member of Minghuazhen Formation may be sealed. Hydrocarbon reservoirs have been found in Guantao Formation and the Lower Member of Minghuazhen Formation in this area. The fault connectivity probability method can be used to intuitively evaluate the extremely heterogeneous opening and sealing characteristics of different parts of a fault. This is confirmed by the discovered hydrocarbons near the fault. The research results provide guidance for the potential evaluation of traps to be drilled near the fault. At the same time, this method provides a basis for quantitative study of the relationship between fault and fluid flow, and for quantitative simulation of hydrocarbon migration process. This method can also be used for reference in quantitative dynamic study of fault activity. 2. Quantitative Characterization of Fault Opening and Sealing Using Permeability Data To unify the characterization of the conductivity of fault carriers with that of other types of carriers, such as carrier beds and unconformities, it is necessary to convert the fault connectivity probability into equivalent permeability.

3.2 Quantitative Characterization of Fault Carriers

191

Fig. 3.30 Topological maps of fault connectivity probability on the plane of Fault ZDH4 to characterize fault conductivity. The solid curves are the intersection lines with the strata at the footwall, and the dotted curves are the intersection lines with the strata at the hanging wall

Hydrocarbon migration along faults is related to process of faulting and rock rupture that forms fluid flow conduits (McLaskey et al., 2012). A fault zone changes from a static sealing state to an active opening state, which can be regarded as a process of fracture forming and growing in the fault zone, and finally becomes a suitable conduit for fluid flow. The fault zone may be divided into faulted rock units corresponding to the aforementioned grid. For each unit, the description of fault opening process is not directly by giving a fracture or specifying the permeability, but by considering it as the result of microfracture forming and interconnecting (Gueguen & Palciouskas, 1994; Luo & Vasseur, 2002). Figure 3.31 shows the process of fracture forming in such a unit. When the fault is sealed, there are few microfractures in the unit, which are difficult to connect with each other (Fig. 3.31a). The permeability depends on the lithology of the unit. With increasing tectonic stress or formation fluid pressure, the effective stress acting on the unit decreases, and more microfractures will appear in the rock. The greater the stress increase or fluid pressure increase, the more microfractures in the unit. With the increasing number of microfractures, some microfractures are inevitably connected with each other. In the process of fault activity, once a sufficient number of microfractures are produced in a unit, and they will connect with each other to form a percolation conduit through the unit, and the range in the fault zone represented by this unit is opened (Fig. 3.31b). Then the equivalent permeability of the unit suddenly increases, and fluid flow may occur (Guéguen & Dienes, 1989). Guéguen and Palciouskas (1994) proposed that the equivalent permeability and porosity of the conductive unit can be estimated by connecting microfractures when

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3 Hydrocarbon Conduit System and Its Quantitative Characterization

A

B

Fig. 3.31 The forming process of fractures in rocks and the percolation connections of the microfractures. a When the number of microfractures is small, the whole rock is impermeable. b When the number of microfractures increases to a critical value, some microfractures connect with each other to form a fluid flow conduit. Blue thick lines indicate equivalent fracture or a small fault interval

microfracturing intensifies. Assuming that these conduits are composed of interconnected dish-shaped microfractures with a thickness 2w and a diameter 2c (Fig. 3.32), the average flow velocity within such a microfracture is calculated as (Landau & Lifshitz, 1987):

v=−

w2 d p φ 3η dx

(3.7)

where v is Darcy flow velocity; w fracture gap; η hydrodynamic viscosity; p pressure; and φ porosity. Among them, the permeability k is: Fig. 3.32 Concept and measurement of dish-shaped microfractures (Guéguen & Palciouskas, 1994)

Z C

2w

l Y X

3.2 Quantitative Characterization of Fault Carriers

k=

193

w2 φ 3

(3.8)

If the average distance between these dish-shaped fractures is l (Fig. 3.32), the porosity of fractures in the rock is: φ = 2π

c2 w l

3

(3.9)

By substituting Eqs. 3.9 into 3.8, the conversion equation of permeability k can be obtained as follows: k=

2π c2 w 3 3 l3

(3.10)

Therefore, in such a microfracture system, the permeability is actually determined by three microscopic variables c, w and l. When faulting continues, the tectonic stress or formation pressure increases continuously, and the microfractures in the rock units on the fault plane increase continuously. If we assume that values of c and w of the newly formed microfractures are the same as those of the original ones, the increase of microfractures means the decrease of the spacing l between them, and the equivalent permeability of the unit will also increase (Eq. 3.10). These connected microfractures constituting percolation conduits can be regarded as an equivalent fracture (Fig. 3.28b). Further increase in the number of microfractures results in an increase in the width of the equivalent fracture, and the corresponding permeability will inevitably increase (Luo & Vasseur, 2002). Numerical simulation of mudrocks, under the condition to keep the balance of fracture opening, tectonic stress and fault fluid pressure, indicates that the increase in equivalent permeability corresponding to the opening fracture is less than one order of magnitude of formation permeability (Luo & Vasseur, 2002). In the fault opening and sealing model, fault opening probability corresponds to the intensity of faulting. Therefore, through the fracture forming model of rock unit Fig. 3.31, we can relate the permeability of fault plane with the fault opening probability. When the fault is sealed, N p = 0, the equivalent permeability in the rock unit on the fault plane is very small, and the permeability can be the lowest as k 0 of mudrocks in the strata in the fault zone in the study area. When faulting increases continuously and reaches the fault opening threshold, N p > 0, the stress acting on the rock unit reaches the microfracture connecting threshold, and the equivalent permeability suddenly increases (Guéguen & Dienes, 1989). According Luo and Vasseur (2002), the value can be 10 × k 0 . After that, with the increase of fault opening probability, the equivalent permeability of rock unit increases continuously. When N p = 1, the permeability value is the maximum k m , which is far greater than 10 × k 0 .

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3 Hydrocarbon Conduit System and Its Quantitative Characterization

The change of fault opening and sealing related to hydrocarbon migration is significantly influenced by regional geological conditions. The permeability in the above models will vary in different study areas, even in different faulting episodes of the same fault. The relationship between fault opening probability N p and rock unit permeability varies from area to area. So, the researchers need to decide the values of the parameters according to the data and geological conditions of the study area. In our previous study, we assumed that the relationship between N p and k is linear:  k=

Np = 0 k0 10 × k0 + P × km Np > 0

(3.11)

Hence, the fault connectivity probability plays two roles in the characterization of the conductivity of fault carriers. On the one hand, the fault connectivity probability determines the connectivity relationship between each transport unit on the fault plane. On the other hand, the fault connectivity probability determines the equivalent permeability of the transport units on the fault plane during the fault opening period. Figure 3.33 shows the conversion result of the fault plane characterized by connectivity probability in Fig. 3.30 into that by equivalent permeability according to Eq. (3.11). The parts with equivalent permeability less than or equal to k 0 on the fault plane are shown in black, and the other parts are shown in different colors to indicate different values of equivalent permeability. The latter result shows more directly the opening characteristics of the fault plane during hydrocarbon migration. And such characterization parameter is consistent with the quantitative characterization of the conductivity of carrier beds, and can be easily used together to characterize the conduit framework composed of carrier beds and faults.

Fig. 3.33 Fault opening and sealing characteristics of Zhangdong-H4 Well Fault, characterized by equivalent permeability

3.3 Carriers Associated to Unconformity

195

3.3 Carriers Associated to Unconformity Unconformities are common in sedimentary basins, formed by erosion due to tectonic uplift or sea (lake) level fall, which results in uncoordinated contact relationship between new and old strata (Bates & Jackson, 1984). The migration conduits in the overlying strata above the unconformity are commonly conglomerates and sandstones in transgressive system (Hunt, 1996), while the migration conduits in the underlying strata of the unconformity are mostly weathered crusts or paleokarsts with high porosity and permeability formed by weathering and leaching (Jiang & Zha, 2016). For qualitative migration analysis, the occurrence of such strata with good physical properties indicates that migration can occur, so unconformities is considered as long-distance lateral hydrocarbon migration conduits (Hunt, 1996; Pan, 1986). However, there have been debates on how unconformity “surfaces” constitute the migration conduit and how to quantitatively characterize it (He, 2007; Song et al., 2010; Sui & Zhao, 2006; Wu et al., 2002, 2003).

3.3.1 Characteristics of Unconformities as Migration Conduits Unconformity is just a surface. If the rocks above and below the unconformity are defined by the “lithologic bodies developed due to the unconformity” (He, 2007), an unconformity associated migration conduit is an interval composed of these lithologic bodies (Sui & Zhao, 2006). Outcrop and core studies indicate that the unconformity assemblage has significant stratified structural properties (Chen et al., 2008; Qu et al., 2003; Song et al., 2008; Wu et al., 2002, 2003). The structural characteristics are the key factors to determine whether unconformity controls hydrocarbon migration and accumulation. Therefore, describing the internal material composition, structural relationship and spatial change of unconformity has become the main focus to understand whether unconformity can be considered as effective hydrocarbon migration conduit (He, 2007; Qu et al., 2003; Song et al., 2010; Sui et al., 2010; Zhang & Ai, 1996). 1. Lithological Characteristics of Unconformity-Related Strata The spatial relationship of unconformity-related strata change greatly (He, 2007; Luo et al., 2014; Song et al., 2010). The erosional processes and amount of underlying strata by various geological processes are heterogeneous. The sedimentary processes of overlying strata are also very different. And various diagenesis occurs during the burial process. The unconformity related strata have a general three-layer structure in the vertical direction, namely, the rocks overlying the unconformity, the weathered rocks beneath the unconformity and the underlying partially-weathered rocks. Influenced by duration, climate, topography, tectonic activities and many other factors, the weathered

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crust or even the partially-weathered rocks may be missing, thus forming various types of unconformity-related structures (Sui et al., 2010). According to the weathering degree of the bedrock underlying the unconformity, the layered unconformity structure can be divided into three types (Song et al., 2008; Sui et al., 2010): Type I, a complete three-layer structure, including the underlying weathered and partiallyweathered rocks, and the formation overlying the unconformity. Type II, the unconformity structure has no weathered crust, and only partially-weathered rocks are present; Type III, the unconformity structure has no weathered crust, and the rocks under the unconformity are basically not weathered. The rocks overlying the unconformity are commonly diachronous formed by reworked sediments being deposited in situ or transported from other sources (Tang et al., 2000). Their type and distribution are influenced by tectonic intensity, stress mode, paleoclimate and paleotopography. Conglomerate, sandstone and mudstone are the most common, and limestone and coal seams occur in some areas. Conglomerate and sandstone varies from tens of centimeters to more than ten meters in thickness, with a great variation and good porosity and permeability in general (Gao & Zha, 2008; Sui et al., 2010). Such sand-rich strata can form conduits for hydrocarbon migration. Mudstone is generally fluvial and lacustrine deposits, with a wide distribution, but its petrophysical properties are poor. So it can be barriers or caprocks overlying the unconformity. Paleosols on the top of weathered crust consist of fine-grained deposits formed by weathering of rocks underlying unconformity. Core observation shows that this layer is mostly purplish red or variegated massive mudstone without obvious sedimentary textures and structures. The thickness of the paleosols varies greatly and the position is mainly limited to the gentle slope of the unconformity (Sui et al., 2010). The sediments are mainly composed of stable minerals, such as quartz and clay minerals, mainly kaolinite; and feldspar and calcite are rare (Chen et al., 2008). The rocks are rich in Al, Fe and Ti, but poor in mobile elements such as Ca and Mg (Song et al., 2008). The permeability of paleosols is generally low due to compaction. So, they have good sealing ability and act as barrier for hydrocarbon migration (Wu et al., 2002). Partially-weathered rocks have been weathered, leached and disintegrated to a certain extent, and the weathering is incomplete (Wu et al., 2003). No matter whether the host rocks are clastic, carbonate, volcanic or metamorphic rocks, there commonly exist fractures, secondary dissolution pore-care system and some fracture-filled sediments. The weathering degree becomes gradually insignificant from shallow to deep, with a thickness ranging from several meters to hundreds of meters (Chen et al., 2008; Fu et al., 2005). Because the types and natures of underlying host rocks are different, the characteristics of partially-weathered rocks are quite different. If the mineral composition of host rocks are mainly quartz, feldspar and clay minerals, some feldspars and mica will be altered to kaolinite; and unstable components will be leached, resulting in many dissolution pores. Most partially-weathered sandstones have good porosity and permeability, mostly fracture and pore space, while mudstones have low permeability and strong plasticity, and is generally less than 3 m thick (Chen et al., 2008; Sui

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et al., 2010). Partially-weathered carbonate rocks are significantly altered to develop a large amount of fractures, dissolved pores and caves. Vertical seepage zones and horizontal zones are often developed in multiple periods to form a highly porous and permeable karst system with a thickness of several hundred meters (He, 2002; Lin et al., 2008; Liu et al., 2006; Zhang & Ai, 1996). The thickness of weathered volcanic and metamorphic rocks is smaller than that of carbonate rocks (Zhang et al., 2000), and fracture-dissolution pores are developed, but the degree of weathering is determined by rock brittleness and mineral composition. Rocks with a high brittleness and a high content of unstable minerals seem more easily to form fractures, dissolution pores and karst caves. 2. Relationship Between Unconformity Structure and Migration Conduit Widespread transgressive conglomerates or sandstones overlying an unconformity can serve as a hydrocarbon migration conduit with good physical properties. Hydrocarbons can migrate laterally along this conduit for a long distance (Cao et al., 2006; He, 2007; Hunt, 1996; Song et al., 2008). However, for other lithologies, the formation of conduit depends on the number, distribution and connectivity of permeable rocks in these transgressive deposits (He, 2007; Luo et al., 2014; Song et al., 2010). Fractures and dissolution pores and karst caves in partially-weathered sandstone, carbonates, volcanic and metamorphic rock are commonly spatially cross-cutting into each other, forming reticular zones with a high porosity and permeability, which can act as the main conduit for hydrocarbon migration (Gao & Zha, 2008; Zhang et al., 1996). Wu et al. (2002) studied the characteristics of migration conduit associated with the Permian unconformity in Junggar Basin. Based On log analysis and core observation, they divided the unconformity structure into three parts overlying and underlying the unconformity surface: basal conglomerate, weathered clay layer and weathered leaching zone. They concluded that the basal conglomerate and weathered leaching zone formed double conduits overlying and underlying the unconformity, that facilitate hydrocarbon migration for a long distance. The characteristics of unconformity structure determine its dual effects on hydrocarbon migration or sealing. To identify whether an unconformity can form migration conduits requires careful analysis of lithologic changes and stratal relationships (Song et al., 2008). Due to the influence of many factors, such as structures, paleotopography, climate, duration, denudation, the spatial unconformity structure are quite complex. He (2007) proposed that the identification of whether an unconformity surface may act as migration conduit needs to consider the contact relationship between the strata overlying and underlying the unconformity. The possible contact relationships are multiple. He (2007) further simplified the rocks overlying the unconformity into sandstone and mudstone, and the rocks underlying the unconformity into sandstone, mudstone, limestone and volcanic rock, and identified 64 types of contact relationships. If the formation and preservation of weathered crust are taken into account, the types would be far more. In addition, the strata underlying the unconformity may be interbeds of various rock types, so the types of stratigraphic structure assemblages are highly variable.

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Song et al. (2008) proposed that the vertical structure can be regarded as a spatial assemblage of permeable and impermeable layers. Overlying the unconformity, the permeable layers consist of conglomerates and sandstones; and impermeable ones are often argillaceous rocks and limestone. Underlying the unconformity, partiallyweathered conglomerate, carbonate, volcanic and metamorphic rocks act generally as permeable layers, while argillaceous rocks act as impermeable layers. The assemblages of these layers constituting the unconformity layered structure are simplified into four types: permeable layer/permeable layer, permeable layer/impermeable layer, impermeable layer/permeable layer, and impermeable layer/impermeable layer. Considering the weathered clay layer in the three-layer structure, the assemblages have four types: permeable layer/impermeable layer/permeable layer, permeable layer/impermeable layer/impermeable layer, impermeable layer/impermeable layer/permeable layer, impermeable layer/impermeable layer/impermeable layer, impermeable layer/impermeable layer/impermeable layer. Cao et al. (2006) made a comparative analysis of the Permian–Triassic unconformity in the northwestern margin of Junggar Basin and the Jurassic-Cretaceous unconformity in the hinterland of Junggar Basin. They concluded that when the strata underlying unconformity are Carboniferous metamorphic rocks, the weathered crust is preserved and may be widespread. The weathered clay layer constitutes a caprock with good sealing properties, and the underlying leaching layer has a good porosity and permeability, which can act as migration conduits. However, when the strata underlying the unconformity are clastic rock, the physical properties of the leached crust are mainly determined by the physical properties of the strata and internal structure, so that these strata often cannot act as long-distance migration conduits. Through numerous case studies in continental basins in eastern China, Song et al. (2010) think it is too difficult for unconformities to act as effective conduits for longdistance lateral migration. If the strata underlying unconformity surface are clastic deposits, because the rapid change of sedimentary facies leads to frequent changes of lithology, sandstone and mudstone are often laterally alternating, forming complex lithologic structures. Therefore, the numerous barriers in unconformity structure make it only suitable for short-distance conduits. In summary, the unconformity itself is only a surface with no volume. In fact, it is the carriers in the strata overlying and/or underlying the unconformity that play a role in subsurface fluid flow and hydrocarbon migration. When these carriers are composed of some special lithologies, such as the basal conglomerate or partiallyweathered rocks with a good continuity, these rocks can act as migration conduits. Other cases should be identified according to the connectivity between the carriers in the strata overlying and underlying unconformity. Understanding the structural relationship of unconformity-related strata is important for qualitative assessment of hydrocarbon migration.

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3.3.2 Spatial Distribution of Unconformity-Related Carrier Beds We studied the Permo-Triassic deposits exposed at the foothills of Bogda Mountains at the southern margin of Junggar Basin, NW China, to observe the characteristics of unconformities (Fig. 3.34). The rocks are fluvial-lacustrine deposits and are grouped into sedimentary cycles bounded by stratigraphic surfaces. The focus of the study is to identify the lateral extent of unconformities, the lithologies of adjacent strata and their lateral continuity. The observations are used to establish a cyclostratigraphic framework of fluvial-lacustrine sedimentary rocks, within which different types of unconformities are documented and interpreted with respect to the spatial distribution of unconformities and adjacent strata as potential hydrocarbon migration carrier beds. Three sections were measured. Two Permian–Triassic sections are ~1800 m and 1200 m long in Taodonggou and Tarlong, respectively; they are about 8 km apart. They are measured and described at a centimeter-decimeter scale, including lithology, stratal geometry, and paleosols underneath the unconformities. Depositional environments and cyclo- and sequence-stratigraphy are interpreted (Yang et al., 2010, 2021). 1. Identification of Multi-order Unconformities The thick Jurassic and Permian–Triassic fluvial-lacustrine deposits are divided into depositional cycles. The cycles are delineated on the basis of systematic depositional environmental changes. The depositional environment of rock units are interpreted mainly from sedimentary texture, sedimentary structure, paleosol structure, fossil fauna and flora, stratal geometry and boundary relationship, and stacking patterns. The depositional cycles are stratigraphic entities and can be regarded as timestratigraphic units. Furthermore, the depositional cycles are hierarchical, forming

Fig. 3.34 Geological map showing the Permo-Triassic outcrops at the foothills of Bogda Mountains at the southern margin of Junggar Basin, NW China

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three orders of cyclicity, termed high, intermediate, and low-order cycles. The boundaries of high-order cycles are at the turning point of abrupt or gradual changes of interpreted depositional environments. These boundaries can be conformable surfaces or unconformable surfaces, such as diastem, paraconformity, disconformity, and angular unconformity. High-order cycles are grouped into intermediateorder cycles (or sequences) on the basis of systematic changes of attributes (such as thickness, magnitude of facies shift, thickness of diagnostic facies) of high-order cycles. Intermediate-order cycles, in turn, are grouped into low-order cycles on the basis of regional, significant changes of long-term depositional environments and interpreted climatic and tectonic conditions. The boundaries of intermediate and low-order cycles are unconformable surfaces, including diastem, paraconformity, disconformity, and angular unconformity. We speculate that the cycle boundaries can also be grouped into three orders, high, intermediate, and low, assuming that the time span of high-order cycle boundaries is, in general, shorter than those of intermediate-order cycles, which, in turn, are shorter than those of low-order cycles (Fig. 3.35). This implies that the durations of the three orders of cycle boundaries are grossly different, correlating to the thickness and duration of the three-order depositional cycles. Finally, a litho-, cyclo-, and chronostratigraphy was established for the Permo-Triassic fluvial-lacustrine deposits in the Tarlong and Taodonggou sections in Bogda Mountains (Yang et al., 2010, 2021).

Fig. 3.35 Stratigraphic chart showing the cyclo- and chrono-stratigraphy Permian–Triassic deposits in the Bogda Mountains, southern Junggar Basin

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2. Lithofacies Sub- and Supra-Jacent to Unconformities and Their Juxtaposition Patterns Figures 3.36 and 3.37 are representive field phots and descriptions demonstrating the lithofacies and nature of contacts across unconformities. The lithofacies and depositional environments across the unconformities are significantly different, showing a great variety. Many unconformities are erosional unconformities. The paleosols and weathered rocks underlying the unconformities are highly heterogeneous. In addition, chemical dissolution and calcite cements occur sporadically. Regardless the type and order of unconformities, the overlying lithofacies are mainly conglomerate, sandstone, and mudrocks. Conglomerates and gravelly sandstones dominate and range from 0.1 s to 1 s of meters thick. They are generally fining upward, moderately sorted, moderately rounded, and commonly imbricated. They are mainly channel lag deposits of braided and meandering streams. Mudrocks are relatively rare above unconformities, mainly as lakeplain deposits. Paleosols occur

Fig. 3.36 Field photos showing the characteristics of unconformities at various scales, Bogda Mountains, southern Junggar Basin, NW China. a Regional disconformity separating the Middle Permian Hongyanchi and Quanzijie Low-Order cycles. Tarlong; b field photo and measured section showing the regional disconformity (green line) separating the Middle Permian Quanzijie (right) and Upper Permian Wutonggou (left) Low-Order cycles and the channel-base disconformity (red line) of a high-order cycle. Taodonggou; c regional disconformity separating Lower Permian Upper Daheyan and Middle Permian Lucaogou Low-Order cycles. Tarlong; d a braided stream channelbase disconformity separating underlying Calcisols from overlying braided stream deposit in Lower Permian Upper Daheyan Low-Order Cycle, Tarlong

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A. Types SS and SV unconformities for low-order cycles. Fig. 3.37 Characteristics of unconformities in Permo-Triassic terrestrial deposits, Bogda Mountains, southern Junggar Basin, NW China. a Types SV and SS disconformities in Lower Permian Middle-Daheyan Low-Order Cycle, Tarlong. The long wavy line is an intermediate-order cycle boundary; short wavy lines are high-order cycle boundaries. b Types SS and SP disconformities, Lower Permian Upper Daheyan Low-Order Cycle, Taodonggou. The long wavy line is an intermediate-order cycle boundary; short wavy lines are high-order cycle boundaries. c Two examples of types SS, SSh, and SP disconformities, Middle Permian Lucaogou Low-Order Cycle, Taodonggou. The long wavy line is an intermediate-order cycle boundary; short wavy lines are high-order cycle boundaries. d Stacked SS2 and SSh1 disconformities, Middle Permian Hongyanchi Low-Order Cycle, Taodonggou. The long wavy line is an intermediate-order cycle boundary; short wavy lines are high-order cycle boundaries

underneath unconformities, mainly in highly erosional unconformities at the base of some low- and intermediate-order cycles. Paleosols are red, brown to maroon, and in some cases, contain iron-oxide nodules. They develop in lacustrine mudrock and carbonate, and volcanic parent rocks. However, in many other high-, intermediate, and low-order cycles, paleosols do not occur. Nevertheless, the lithofacies subjacent to the unconformities are complex, commonly conglomerate, sandstone,

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B. Types SS and SP unconformities of low-order cycles.

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C. Types SSh and SP unconformities of low-order cycles. Fig. 3.37 (continued)

shale, carbonate, and rhyolite. Channel sandstones are commonly calcite cemented, probably related to leaching and precipitation during subaerial exposure. Primary leaching and weathering are, in some cases, difficulty to be differentiated from modern weathering.

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D. Types SS2 and SSh1 unconformities associated with intermediate-order cycles. Fig. 3.37 (continued)

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3. Lithofacies Juxtaposition Patterns Across Unconformities The role of unconformity in hydrocarbon migration and accumulation is, to a large degree, determined by the juxtaposition of lithofacies sub- and supra-jacent to the unconformities (Wu et al., 1998, 2003). In this effort, we classify unconformities into two types on the basis of lithofacies juxtaposition patterns (Fig. 3.38). Rocks formed before the formation of an unconformity may not be genetically related to those formed after the formation of unconformity. In sedimentary rock packages, different types of sedimentary facies and depositional systems will juxtapose across an unconformity because sedimentary, biological, tectonic, climatic, and topographic conditions in and adjacent to the depositional site that control sedimentation may have changed significantly over the significant time span represented by the unconformity. In addition, a significant thickness of the sedimentary rocks underlying the unconformity may have been eroded. The degree of disruption of sedimentary regime and the amount of erosion depend on the processes that form an unconformity and the duration of the unconformity. As a result, an unconformity can be classified on the basis of lithofacies (rock types) across the unconformity, for the purpose of the study of hydrocarbon carrier beds related to unconformity. The rocks underlying an unconformity may be any types of igneous, metamorphic, and sedimentary rocks. However, rocks overlying an unconformity have to be rocks that form on the earth surface, namely, sedimentary and volcanic rocks. This classification results in numerous scenarios of lithologic juxtapositions across an unconformity (Fig. 3.1). Hydrocarbon carrier beds are rocks that are porous and permeable to serve as efficient fluid flow conduits in the subsurface. The porosity and permeability of rocks underlying an unconformity are determined by the primary fabrics of rocks during their formation, intensity of structural fracturing, diagenetic alteration during burial, and subaqueous and subaerial chemical and physical alterations during the formation of the unconformity. The degree and type of surficial alteration depends on the environmental conditions (depositional environment, climate, topography) during the period of unconformity formation and the characteristics of rocks themselves. The porosity and permeability of rocks overlying an unconformity are also determined by primary and diagenetic factors similar to those underlying an unconformity, but they are not subject to subaqueous and subaerial alternations during the period of unconformity formation, because they form after the formation of unconformity. However, the topography of the unconformity surface and the substrate sediments and rock types underneath an unconformity will affect to a certain degree the thickness and type of sedimentary rocks deposited on the unconformity surface. The two types of juxtaposition patterns presented here only consider siliciclastic sedimentary rocks for the suprajacent lithofacies. The first type has a permeable lithofacies, such as sandstone or conglomerate, overlying the unconformity. The lithofacies subjacent to the unconformity ranges from conglomerate, sandstone, mudrock, paleosol, carbonate, evaporite, and igneous or metamorphic rocks (Fig. 3.38a). Whether the suprajacent rocks can serve as a carrier is determined by their porosity and permeability during the migration. If the subjacent rocks are conglomerate or

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A – Type 1 lithofacies juxtaposition pattern across an unconformity. Fig. 3.38 Types of unconformities as classified by the juxtaposing lithologies underlying and overlying an unconformity. a Overlying lithology is a permeable sandstone or conglomerate. The porosity and permeability of paleosols depend on the type of parent rock and the maturity of paleosols. If the parent rocks are, for example, limestone, evaporite, and marble, pedogenesis may generate dissolution cavities to increase the porosity and permeability of these rocks, which can serve as hydrocarbon carriers. b Overlying lithology is a non-permeable shale or coal. A shale can also be a carrier bed if it is highly fractured

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B. Type 2 lithofacies juxtaposition pattern across an unconformity. Fig. 3.38 (continued)

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sandstone, they commonly can form a carrier bed with the suprajacent rocks. If the subjacent rocks are mudrocks, the suprajacent rocks will likely not act as a carrier. If the paleosols underneath the unconformity have a high sealability, whether the rocks below the paleosol can be a carrier bed is determined by their type and degree of weathering. If the rocks below the paleosols are limestone, evaporite, igneous or metamorphic rocks, the fracturing caused by intense leaching and physical breakage can form fractures, dissolution pores and vugs and, thus, these rocks can serve as a carrier. On the other hand, if the weathering is weak, the rocks underneath the paleosol will not likely be a carrier. Finally, if the rocks below the paleosols are mudrocks, they will not likely be a carrier. In general, for this type of unconformity, potential carrier beds are likely the rocks both sub- and supra-jacent to the unconformity as double-deck carriers, or rocks suprajacent to the unconformity as single carriers. The second type of unconformity has a suprajacent mudrock with a variety of subjacent rocks, such as conglomerate, sandstone, mudrock, paleosol, limestone, evaporite, igneous or metamorphic rocks (Fig. 3.38b). The suprajacent mudrocks have low permeability due to compaction and commonly do not have a high conductivity. They are likely good caprocks. Whether the subjacent rocks can serve as a carrier depends on the type of rocks and their degree of weathering and alteration, similar to that in the first type of unconformity. In general, for the second type of unconformity, the potential carrier bed is likely the subjacent rocks as single carriers. It is also possible that no carriers may be present either underneath or above the unconformity. 4. Lateral Changes of Lithofacies Sub- and Supra-Jacent to Unconformities We documented the lateral changes of lithofacies in the sub- and supra-jacent strata of unconformities in stratigraphic cross sections to assess the effectiveness of unconformity-associated carrier beds. The correlation of stratigraphic sections indicates that, even in a distance of 20–100 m, the lithofacies change significantly and become difficult to trace laterally (Figs. 3.39 and 3.40). In the Taodonggou cross section (Fig. 3.39), there two unconformities in the vicinity of the boundary between the Hongyanchi and lower Quanzijie low-order cycles. The upper unconformity separates a 30-cm thick paleosol from overlying 1-m thick gravelly sandstone at the left. However, 100 m to the right along the same unconformity, the subjacent rock changes to a shale, while the suprajacent rock changes to a 2-m thick conglomerate. Furthermore, the lower unconformity, at the left most section, separates a limestone from overlying paleosol. 20 m to the right, the interval changes to a 50 cm thick calcareous sandstone overlying shale intercalated with limestone. This unconformity merges with the upper one 100 m to the right. Similarly, the two erosional unconformities in the vicinity of the boundary between the upper Quanzijie and Wutonggou low-order cycles also have significant lateral lithofacies changes across the unconformities in a distance of 100 m and 2 km, respectively (Fig. 3.40). The lithofacies changes across an unconformity are produced by the spatial and temporal variations of geological processes before, during, and after the formation of the unconformity. Erosion and alteration of the underlying strata are common

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Fig. 3.39 Lateral variations in type of disconformities. The upper disconformity is a regional loworder cycle boundary separating the Middle Permian Hongyanchi and Quanzijie Low-Order Cycles, Taodonggou, Bogda Mountains, NW China

Fig. 3.40 Lateral variations in type of unconformities. The lower disconformity is the low-order cycle boundary between Middle Permian Quanzijie and Upper Permian Wutonggou Low-Order Cycles, Taodonggou, Bogda Mountains, NW China

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to form a stratal hiatus, which affects the deposition of overlying sediments. The differences in processes and products laterally along the unconformity will result in lateral variations in lithofacies across the unconformity. This is exacerbated by the rapid lateral facies changes of terrestrial strata. In summary, the lateral facies changes greatly complicates the interpretation of carrier and non-carrier beds.

3.3.3 Model of Unconformity-Related Carriers Unconformity, in essence, just represents a surface of stratal hiatus. Its role in longdistance hydrocarbon migration and accumulation is determined by the porosity and permeability and lateral continuity of sub- and supra-jacent lithofacies. The sealability and continuity of caprock overlying an unconformity-related carrier is also important (Chen et al., 2011; Song et al., 2010). The assessment of such carrier beds needs to be done in a three-dimensional view. In two and three dimensions, rocks above and below unconformity will change laterally because of lateral changes of sedimentary, igneous, and metamorphic facies, tilting of rock strata, and lateral variations in erosional depth. This will significantly increase the complexity of the unconformity classification based on the juxtaposing rock types across the unconformity (Fig. 3.41). The variety of lithological juxtapositions significantly complicates the evaluation of the connectivity and effectiveness of unconformity-related carriers. Above all, regardless of the juxtaposition pattern, lateral lithofacies changes will always reduce the potential of such carriers for long-distance hydrocarbon migration. Therefore, the role of unconformity in hydrocarbon migration needs to be considered as whether the weathered crust and the stratigraphic assemblages across the unconformity possess lateral transport capability. And the spatial superposition relationship (more precisely, fluid connectivity) among permeable strata related to the unconformity is the key. The relationship is actually what has been discussed in the first section of this chapter. In the definition of the carrier bed, the caprock above the bed may be diachronous. Therefore, we suggest that unconformity-related strata should be treated as special carrier beds as hydrocarbon migration conduits. The main problem is how to determine the regional caprock near the unconformity in the study area. The distribution of carrier beds in the strata above the unconformity can be determined by the geological conditions and data conditions without considering the unconformity per se. If the strata suprajacent to the unconformity are permeable, such as basal conglomerates and sandstones, only the position of overlying caprock needs to be considered in a carrier bed model. If the strata are of a low permeability, such as mudrocks or coal seams, the strata would act in fact as a caprock for the strata underlying the unconformity. Then the carrier bed model needs to incorporate the strata subjacent to the unconformity. Thus, no matter whether there is weathering crust, the stratum of a certain thickness range can be used as a carrier bed, regardless of the type, distribution, and connectivity of the carriers.

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Fig. 3.41 Conceptualized model showing possible cases of heterolithic lithologies and carrier beds underlying and overlying an unconformity

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Once the three-dimensional range of a carrier bed is determined, subsequent steps can follow the procedures outline in the first section of this chapter, to establish a carrier bed model and to characterize quantitatively the carrier conductivity.

3.4 Development and Quantitative Characterization of a Composite Hydrocarbon Conduit Framework Conduit units, such as faults (fractures), carrier beds and unconformities, often do not occur in isolation in a hydrocarbon MAU. During the main hydrocarbon migration period, different types of carriers are commonly connected to each other spatially, forming a composite conduit system. In the study of dynamics of hydrocarbon migration-accumulation, the establishment of a composite conduit framework in a key period is the core issue for objectively and quantitatively evaluating the hydrocarbon migration efficiency of the conduit system (Luo et al., 2012). This includes the description of the spatial geometric relationship of various types and shapes of carriers, the determination of their linking relationships, as well as the selection of parameters suitable for characterizing quantitatively the transport capabilities of various carriers in the composite conduit framework three-dimensionally. Based on the previous methods of modeling and quantitative characterizing a single conduit unit, such as a carrier bed, fault and unconformity, this section discusses the development of a composite hydrocarbon conduit framework and the quantitative characterization method of its conductivity.

3.4.1 Principles of Construction of a Composite Conduit System Composite hydrocarbon conduit framework is a three-dimensional system, which is composed of two or more types of carriers connected, crossed and superimposed with each other in a hydrocarbon MAU limited by time and space. The formation of composite conduit system is time-sensitive and spatially structured. In different periods of basin evolution, due to geological processes such as tectonic deformation, fault activity and diagenesis, the transport capabilities, spatial structure and matching relationship of different types of carriers in the composite conduit framework change constantly, and their role as hydrocarbon migration conduits and transport styles also change. The construction of the composite conduit framework should also be limited to specific hydrocarbon migration periods within the spatial range of the hydrocarbon MAU. A composite conduit framework should have a clear relationship of effective carriers, so that the process of hydrocarbon migration from the source to trap can be traced.

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The establishment of the composite conduit framework should be based on the quantitative study of the conductability of various types of carriers during the main hydrocarbon reservoir forming stage. The analysis of the assemblage pattern and three-dimensional geological modeling of the composite conduit framework in the key period should be the focus. Effective parameters to quantitatively characterize the composite conduit framework in the three-dimensional space need to be formulated. Subsequently, the hydrocarbon transport efficiency of the conduit system can be objectively and accurately evaluated. The most common composite conduit system is the carrier bed-fault framework, formed by faulting of the carrier beds. In addition, although unconformity-related carriers can be treated as one type of the carrier bed, strata of different ages juxtapose with each other with different tectonic relationships (He, 2007). Hence, these carriers with different sedimentary environments and facies distributions can also form different forms a carrier bed-(unconformity) carrier bed framework. Three-dimensional modeling technology with various graphic algorithms can be used to establish a framework consisting of strata and fault planes, and to describe the spatial relationship of composite carrier units. However, a reasonable acquisition of quantitative parameters of composite conduit framework and accurate interpolation in a limited three-dimensional space are still challenging to meet the data requirements for simulating oil and gas migration. Limited by the current level of subsurface data availability and basin model methods, the model establishment and quantitative characterization of the carrier bed and fault carrier has to be simplified to two-dimensional based on by upscaling. According to our understanding of the assemblage pattern of a composite conduit framework, the quantitative description of multiple plane topological models is adopted for quantitative characterization of conductability of the composite conduit system in a three-dimensional space. That is, the three-dimensional conduit system composed of different carrier assemblages is the three-dimensional conduit framework formed by several crossing twodimensional carrier surfaces. The description of hydrocarbon migration can only be carried out along these surfaces. The description of composite conduit framework also depends on the spatial scale. Faults and fractures are common in a composite conduit framework in small-scale migration conduit analysis. However, in large-scale conduit system, the small-scale frameworks must be simplified as a unit of the large system, where the details are replaced by equivalent units, or even an equivalent parameter. In the construction of the composite conduit framework in actual basins, it is necessary to integrate the understanding of tectonics, sedimentary system, characteristics and evolution of hydrocarbon geological elements. The distribution of discovered hydrocarbon reservoirs, the range of hydrocarbon sources, and the characteristics of various carriers at different reservoir forming stages should be obtained from basic data in exploration and development and be fully used in this effort. The discovered hydrocarbon reservoirs, hydrocarbon shows and hydrocarbon fluid inclusions as clues should be used to interpret the hydrocarbon migration process, analyze the spatial relationship among hydrocarbon source, carriers and hydrocarbon reservoirs in the main reservoir forming stages, identify the effective carrier assemblages in a

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composite conduit framework and hydrocarbon migration modes in various carriers. All the above data and analyses will permit us to reconstruct a model that can quantitatively characterize the connectivity of different types of carriers in the composite conduit framework in an area. In frontier exploration areas, the lack of complete geological and geochemical data makes it difficult to directly determine the lithologic assemblage of a composite conduit framework. The conditions and characteristics of carriers in adjacent wellexplored areas can be used as analogs. Seismic data and limited exploratory well data can be used to infer hydrocarbon distribution, carrier morphology and spatial distribution. With those data constraints, the possible hydrocarbon migration process can be inferred, and various carrier assemblages can be reasonably interpreted to establish a composite conduit framework model.

3.4.2 Method to Establish a Composite Conduit Framework The construction of composite conduit framework is a systematic and comprehensive study and requires the comprehensive application of multidisciplinary information of geology, geophysics and geochemistry and related methods. Applying the aforementioned principles with our experiences in the Bohai Bay, Ordos, and Qaidam basins, the methods of constructing a composite hydrocarbon conduit framework are summarized below. 1. Identification of the Source-Conduit-Reservoir Assemblage The interpretation of basic geological, seismic, log, and engineering testing data in an area can be used to identify the spatial assemblage of hydrocarbon source rocks-types of carriers (conduits)-hydrocarbon reservoirs. The key maps to be constructed are a series of regional well cross sections and fence diagrams covering the source areas and drilled reservoirs or traps. The sections should include stratigraphic framework, effective source rocks, carrier beds, fault geometry, reservoirs, oil–water contact. Although a large number of reservoir cross sections may be available, most of them are used for target evaluation at a local scale, and commonly lack of geological information that indicates independent MAUs. Therefore, it is necessary to systematically compile representative regional hydrocarbon reservoir sections in order to develop a composite conduit framework. For a specific study area, it is very important to properly select representative sections that reflect the possible hydrocarbon migration and accumulation process to facilitate the identification of composite carriers. The location and number of sections can be determined on the basis of the distribution of MAUs and the integrity of seismic-well data. Maps of hydrocarbon distribution of different intervals, hydrocarbon expulsion intensity, tectonics, sedimentary facies or sandstone ratio, and hydrocarbon migration-accumulation units can be superimposed and analyzed. The

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hydrocarbon migration direction can be determined by correlation of hydraulic potential and carriers in the migration-accumulation units. The relationship between effective source rocks and reservoirs can be used to ensure that well and seismic data on and near the cross sections are as complete as possible. In addition, the selection of representative sections should not be limited to those covering the discovered reservoirs and should include those related to non-producing wells to better understand oil and gas migration. The analysis of such sections can provide important clues on hydrocarbon migration history. The stratigraphic contacts and fault planes in the representative sections define the basic framework of the composite conduits. In fault-rich areas, faults disrupt the lateral continuity of carrier beds to form a complex assemblage. Accurate identification of various contacts is critical to determining the geometry, mutual connectivity of different types of carriers and their spatial relationship with sources and reservoirs. In areas where 3D seismic data is available, it is relatively easy to identify the stratigraphic boundaries and faults in the sections. Generally, seismic sections can be calibrated by synthetic seismograms linked with well data. In areas lacking 3D seismic data, 3D basin models can be constructed using commercial software using the structural maps of individual stratigraphic units. Then, geological sections can be extracted from the 3D model. The accuracy of the sections may be poor. Thus, it is necessary to use the well data near the sections to carefully improve the sections. Using the Chengbei Fault-step Zone on the south slope of Qikou Sag in the Bohai Bay Basin as an example, we made four well cross sections covering hydrocarbon generating sags and traps. Figure 3.42 shows the sections passing through Qikou Sag and Zhangdong-Zhaodong-Guanjiabao hydrocarbon-bearing structures near the eastern part of the study area. The dark gray mudstones of the Third Member of Shahejie Formation (Es3 ) and the First Member of Formation (Es1 ) of Paleogene on the footwall of Well Hai-4 are mature source rocks. The relationship among the source rocks, types of carriers and reservoir distribution indicates that the composite conduit framework is composed of: (1) Three fault carriers of Well H4, Yangerzhuang, and Well Z8 North; (2) sandstone carrier beds of the Second and Third members of Shahejie Formation in the north of Well H4 fault, Jurassic sandstone carrier beds, the Second Member of Shahejie Formation, Guantao Formation and Minghuazhen Formation between Well H4 fault and Yangerzhuang fault, sandstone carrier beds of the First Member of Shahejie Formation, Guantao Formation and Minghuazhen Formation in the south of the Yangerzhuang fault; and (3) basal conglomerate carrier bed above the unconformity at the base of Shahejie Formation. Finally, the four sections are assembled into a fence diagram to construct the composite conduit framework of the study area (Fig. 3.43). 2. Construction of the Model of Composite Conduit Framework During the Key Hydrocarbon Reservoir Forming Stage Detailed oil-source correlation confirms (Wang et al., 2006; Zhang et al., 2007) that the hydrocarbons found in Chengbei Step Fault Zone mainly come from the source rocks of the Third Member of Shahejie Formation in Qikou Sag and Qinan Sag, mixed with a small amount from the source of the First Member of Shahejie Formation.

3.4 Development and Quantitative Characterization of a Composite …

217

Fig. 3.42 Section showing the composite conduit framework crossing wells H4 and XH8, Chengbei Step Fault Zone in Qikou Sag of the Bohai Bay Basin

Fig. 3.43 Fence diagram of a composite conduit framework in Chengbei Step Fault Zone in Qikou Sag of the Bohai Bay Basin

Based on the tracing results using geochemical indicators and physical parameters of crude oil in representative sections in the study area, it is speculated that the hydrocarbon migration and accumulation processes may have occurred during the Minghuazhen-Quaternary time (Fig. 3.44): The hydrocarbons generated in the source

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rocks of the Third Member of Shahejie Formation in the northern Qikou Sag were expelled into the carrier bed of the Third and Second members of Shahejie Formation and migrated laterally toward south, then vertically through the Well Zhangdong-H4 fault and Zhaobei fault. Oil migrating upward along the Zhaobei fault mainly charged into the sandstone carrier beds of the Third and First members of Shahejie Formation. During the continued migration southward to the Yangerzhuang fault-Yangerzhuang South fault, hydrocarbons mainly migrated laterally into the sandstone carrier beds of the First Member of Shahejie Formation on the hanging wall. Subsequently, in Zhaodong, Guanjiapu and Liuguanzhuang areas, hydrocarbons migrated vertically along the faults and migrated into the carrier beds of Guantao and Minghuazhen formations and accumulated there. Therefore, the most important reservoir forming period in the study area is from the time when the upper Minghuazhen Formation was deposited to the present. There are mainly four possible assemblages of carrier framework: (1) The Third Member of Shahejie Formation carrier bed-Zhangdong fault-the Third Member of Shahejie Formation carrier bed-Zhaobei fault—the Third Member of Shahejie Formation carrier bed-Yangerzhuang fault—the First Member of Shahejie Formation carrier bed; (2) the Third Member of Shahejie Formation carrier bed-Zhangdong/Well H4 fault—the Second Member of Shahejie Formation carrier bed-Zhaobei fault— the Third Member of Shahejie Formation carrier bed-Yangerzhuang/Yangerzhuang South fault—the First Member of Shahejie Formation carrier bed; (3) the Second Member of Shahejie Formation carrier bed-Zhangdong/Well H4 fault—the Third Member of Shahejie Formation carrier bed-Zhaobei fault—the Third Member of Shahejie Formation carrier bed-Yangerzhuang/Yangerzhuang South fault—the First Member of Shahejie Formation carrier bed; and (4) the Second Member of Shahejie Formation carrier bed-Zhangdong/Well H4 fault—the Second Member of Shahejie Formation carrier bed-Zhaobei fault—the Third Member of Shahejie Formation carrier bed-Yangerzhuang/Yangerzhuang South fault—the First Member of Shahejie

Fig. 3.44 Delineation of conduit framework assemblages and the geochemical tracer of the migration processes in Chengbei Step Fault Zone of the Bohai Bay Basin

3.4 Development and Quantitative Characterization of a Composite …

219

Formation. Furthermore, three models of hydrocarbon migration at the end of deposition of Dongying Formation can be developed: (1) The Third Member of Shahejie Formation carrier bed-Zhangdong/Well Hai 4 fault—the Third Member of Shahejie Formation carrier bed-Zhaobei fault—the Third Member of Shahejie Formation carrier bed; (2) the Third Member of Shahejie Formation carrier bed-Zhangdong/Well H4 fault—the Second Member of Shahejie Formation carrier bed-Zhaobei fault—the Third Member of Shahejie Formation carrier bed; (3) the Second Member of Shahejie Formation carrier bed-Zhangdong/Well H4 fault—the Second Member of Shahejie Formation carrier bed - Zhaobei fault - the Third Member of Shahejie Formation carrier bed. These assemblages of conduit framework for the main reservoir forming stage can be used as a composite framework model for further simulation and analysis of oil and gas migration and accumulation, or be combined to construct a more complex composite conduit framework. In the absence of large-scale tectonic activities after the main reservoir forming stage, the present-day data may be directly used to establish the carrier framework to represent the hydrocarbon conduit framework during the main reservoir forming stage. For superimposed basins with multi-periods of reservoir formation and tectonic activities, the geometry and assemblage relationship of different types of carriers in the main reservoir forming stage may be quite different from those at present. Therefore, it is necessary to use 3D basin modeling or balanced-section restoration to accurately reconstruct the paleomorphologies of fault planes, carrier beds and unconformities, and reconstruct the paleocomposite conduit framework. Figure 3.45 shows the effective conduit framework model for the northern part of Dongying Sag in the Bohai Bay Basin in the key reservoir forming stage. It demonstrates the spatial geometry of the top surfaces of two sandstone carrier beds Es4 sChunshang and Es4 sChunxia , and the Shengbei-T94 fault plane, as well as the spatial relationship among these carriers.

3.4.3 Quantitative Characterization of Composite Conduit Framework The composite conduit framework is a three-dimensional geological body composed of sandstone carrier beds, unconformities, and faults. The quantitative characterization of composite conduit framework needs to select unified parameters that can reflect the conductability of various carriers by using attribute interpolation methods. At present, although the morphology of a composite conduit framework can be established using 3D geological modeling, there are still problems in constructing a 3D attribute model to describe various geological properties. The required data for numerical simulation of hydrocarbon migration in composite conduit system are scarce. Generally, stochastic models directly based on seismic data are difficult to be geologically verified. More importantly, common heterogeneities make it too difficult

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Sh

eng

Fa bei

ult

Es4s c huns ha ng

T94

Fau

lt

Es4s

c hun x ia

Fig. 3.45 Paleomorphological model of the carrier framework in the north part of Dongying Sag of Bohai Bay Basin during the key reservoir forming stage (end of Ed)

to interpolate quantitative parameters of carrier beds and fault carriers in threedimensional space. Therefore, we propose a method of multiple plane topological modeling to characterize the complex conduit system in three-dimensional space (Luo et al., 2007b). In the Shahejie Formation of Chengbei step-fault belt in Qikou Sag, Bohai Bay Basin, the reservoir-carrier system is modelled as a three-dimensional framework composed of faults and carrier beds (Lei et al., 2014). Figure 3.46 shows schematically such a simplified framework model. The two sets of ES2 and Es3 carrier beds and F1 and F2 faults form a stepped composite carrier framework (Fig. 3.46a). When hydrocarbon is charged into the ES2 carrier bed from the southern end, there may be three possible migration patterns, illustrated in Fig. 3.46a with different color arrows. This geological process occurring in three-dimensional space can be investigated as three possible scenarios by using three map-based conduit models (Fig. 3.47b, c). Through such conversions, several possible two-dimensional composite conduit framework models for oil migration can be established in a study area during one key reservoir forming stage. These 2D models may be combined to represent the three-dimensional composite conduit system in the study area. In these 2D models, the connectivity and transport capacity of different carrier units can easily be characterized quantitatively.

3.4 Development and Quantitative Characterization of a Composite …

221

Fig. 3.46 a Diagram showing a conceptual 3D composite conduit system or framework that is composed of two carrier beds and two faults. The arrows with different colors show the possible migration patterns, when hydrocarbon is charged into the S2 carrier bed from the southern end. The 3D models may be simplified into 2D map-based conduit framework models corresponding to the first b second c and last d migration patterns

Fig. 3.47 A composite conduit framework model of Shahejie Formation in Chengbei Step Fault Zone during the key reservoir forming stage. a composite conduit framework model composed of sandstone carrier beds in different intervals and faults; b quantitative characterization of conductivity of different types of carriers

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Figure 3.47a shows a 2-D composite conduit framework model of Shahejie Formation in Chengbei Step Fault Zone of Qikou Sag of Bohai Bay Basin during the hydrocarbon migration period at the end of Minghuazhen deposition (Luo et al., 2007b). In the model, sandstone carrier beds in the Third Member of Shahejie Formation—the Second Member of Shahejie Formation—the First Member of Shahejie Formation were connected by several fault carriers from north to south, to form a complete composite conduit framework. In the north area of Zhangdong- Well H4 fault, the Third Member of Shahejie Formation are taken as the lateral migration carrier bed; from the north of Zhangdong- Well H4 fault to the pinchout of the Second Member of Shahejie Formation near the Zhaobei fault, the Second Member of Shahejie Formation are taken as the migration carrier bed. In Zhaobei-Yangerzhuang south area, the Third Member of Shahejie Formation are taken as the lateral migration carrier bed; and in the south of Yangerzhuang-Yangerzhuang South faults, the First Member of Shahejie Formation are taken as the carrier bed. In Fig. 3.47b, different colors are used to describe the corresponding conductivity of each carrier. That of a sandstone carrier bed is shown by green color gradation: from light to dark, the permeability of carrier bed is reduced from 500 × 10–3 um2 to 0.5 × 10–3 um2 (corresponding to the average throat radius from 0.25 mm to 0.05 mm). The fault conductivity is characterized by equivalent permeability. It is calculated by Eq. (13) and is divided into 10 intervals. The equivalent permeability of the fault zone is about 0.1 × 10–3 um2 –5000 × 10–3 um2 . Figure 3.47b shows a spectrum of colors from light green to dark orange, and the darker the orange color, the higher the conductivity.

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Chapter 4

Quantitative Evaluation Method of Hydrocarbon Migration and Accumulation Efficiency and Resource Distribution

Objective evaluation of hydrocarbon resource potential is very important for exploration decision-making (Pang, 2003). The quantitative methods to analyze the distribution rule of commercial-scale hydrocarbon resources and accurately predict the spatial location of hydrocarbons to be discovered are of great significance for reducing exploration risks, optimizing exploration targets and improving exploration benefits (Hu et al., 2007). Especially in the grim situation of increasingly complex oil and gas exploration objects, we urgently need accurate information of hydrocarbon migration and accumulation efficiency and quantitative prediction of hydrocarbon resource distribution. The research idea and quantitative method of hydrocarbon reservoir formation dynamics provide practical research tools for solving the problem. Based on the method of dynamics of migration and accumulation and the principle of material balance, from the perspective of the whole process of primary migration, secondary migration, accumulation for reservoir formation and preservation after hydrocarbon generation, considering the dynamic equilibrium relationship among hydrocarbon supply, loss and accumulation, according to the stages of hydrocarbon migration and the loss mechanism in the corresponding stages, we have proposed a calculation model of hydrocarbon loss during the secondary migration. By referring to the previous estimation methods of hydrocarbon expulsion amount of source rocks, non-commercial-scale accumulation and loss duo to tectonic damage, we have also established a new material balance model with migration-accumulation unit as resource evaluation unit, and proposed a prediction method of hydrocarbon resource distribution based on quantitative evaluation of migration and accumulation efficiency of carrier system.

© Science Press and Springer Nature Singapore Pte Ltd. 2023 X. Luo et al., Dynamics of Hydrocarbon Migration, https://doi.org/10.1007/978-981-19-5534-1_4

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4.1 Primary Hydrocarbon Migration and Estimation of Hydrocarbon Expulsion After the primary migration of hydrocarbons generated in source rocks, the amount of hydrocarbons expelled to the carrier bed (or reservoir) fundamentally determines the resource potential and migration and accumulation efficiency of conventional hydrocarbons in basins. As the primary migration mechanism is very complex (Magara, 1978; Mann et al., 1997), The awareness about this geological phenomenon is still fuzzy, although scholars have put forward various inferences and hypotheses based on microscopic observation and laboratory simulation of source rocks (Chen, 1982; Li, 2013; Magara, 1978; Tissot & Pelet, 1971). There remain many unanswered questions about the primary migration of hydrocarbons, and there is still a lack of effective means for macro analysis in actual basins (Mann et al., 1997; Luo, 2001), it is still difficult to accurately estimate the amount of hydrocarbon expulsion from source rocks.

4.1.1 Dynamic Condition Analysis of Hydrocarbon Primary Migration On the geological time scale, the primary migration of hydrocarbons and the subsequent secondary migration almost occur synchronously, so the time of the primary migration basically corresponds to the time of the entire hydrocarbon migration and accumulation for reservoir formation. The correct understanding of the dynamic conditions of the primary migration directly affects the grasp of the entire migration and accumulation for reservoir formation process. Using the numerical basin model, the distribution and evolution characteristics of fluid pressure in the hydrocarbon primary migration system composed of sandstone beds and source rocks are simulated to discuss the dynamic conditions for primary migration of hydrocarbons in free state through pore throats, microfractures and kerogen networks in source rocks. 1. Basic Understanding of Hydrocarbon Primary Migration The hydrocarbon primary migration mechanism has been studied and discussed for a long time (Chen, 1982; Tissot & Espitalié, 1975). How are the generated hydrocarbons expel out of source rocks by overcoming the resistance is the main content of the primary migration mechanism research (Palciauskas, 1991). Under the conditions of porous media environment in source rocks when hydrocarbon generation occurs and later, the dynamic conditions of primary migration often depend on the phase state of flowable hydrocarbons (Price, 1976), and different phase states inevitably correspond to different migration conduits and migration driving force. Previous researchers have proposed a variety of phase states of primary migration, which can be mainly categorized as three types: water-dissolving phase, free phase and diffusion phase (Li, 2013). The migration mode of hydrocarbons in

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water-dissolving phase is assumed as follows: Hydrocarbons are carried by water, and the migration occurs more easily in hydrophilic medium without overcoming huge capillary resistance. However, experimental and theoretical calculation results show that the solubility of petroleum in water is very limited (Jones & Roszelle, 1978; McAuliffe, 1969), and even considering the more favorable solubilization mechanisms (Bray & Foster, 1980; Price, 1976), it is difficult to meet the requirements for the formation of commercial-scale accumulations (Chen, 1982). Therefore, the water-dissolving phase is not the main state for the hydrocarbon primary migration. The primary migration of hydrocarbons in diffusion phase is also mainly limited to natural gas (Leythaeuser et al., 1983). If the precondition is whether hydrocarbon reservoirs with commercial value can be formed, the primary migration of hydrocarbons should be mainly in free state (Li, 2013; Magara, 1978; Mann et al., 1997; Ungerer et al., 1990). In recent years, with the exploration and development of unconventional shale hydrocarbons, conditions have been available to conduct microscopic observation and research on the internal structure and fluid occurrence state of shale, revealing that there exists indeed a large amount of free hydrocarbons in source rocks (Jarvie, 2012; Lei et al., 2015, 2016). According to the hydrocarbon generation theory of the thermal maturation of organic matters, the primary migration of hydrocarbons mainly occurred in the late stage of diagenesis, and the fine pore throat in the source rocks may restrict the migration of hydrocarbons in this stage (Tissot & Welte, 1984), so it is necessary to have a good match between the seepage conditions and the migration dynamic conditions in the source rocks (Magara, 1978). In addition, episodic opening-sealing microfractures in argillaceous source rocks under the combined action of tectonic stress and abnormally high pressure (Luo & Vasseur, 2002), possible kerogen networks in source rocks (Ungerer et al., 1990), and oil wetting pathways formed by previous hydrocarbon migration (Leythaeuser et al., 1983; Ungerer et al., 1990) may all become conduits for the primary migration of hydrocarbons in free state (Li, 2013; McAuliffe, 1979). The source rocks in the late diagenesis stage are dominated by fine pore throats, so that the resistance of conduits determines for hydrocarbon migration in free state is very large, and the buoyancy is negligible compared with it (Tissot & Welte, 1984). Overcoming such resistance requires strong migration driving force. If molecular diffusion is not considered, abnormal fluid pressure is the most important driving force for hydrocarbon primary migration among various possible forces (Magara, 1978; Mann et al., 1997). When the abnormal pressure in the argillaceous rock is high, a pressure transition zone can be generated near its boundary (Magara, 1978). The higher the excess pressure, the narrower the pressure transition zone range (Leythaeuser et al., 1988; Luo, 2001; Magara, 1978). This narrow abnormal pressure transition zone seems to correspond to the change of easily migrated organic components found in the boundary part of thick source rocks in some hydrocarbonbearing basins (Tissot & Pelet, 1971). However, due to the widespread existence of silty laminae with relatively good porosity and permeability in source rocks (Lei et al., 2015) and the communicative effect of micro-fractures continuously opening due to high pressure (Fan et al., 2012; Jin et al., 2010; Lash & Engelder, 2005; Panahi

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et al., 2014), hydrocarbons in the center of source rocks may also migrate, but the hydrocarbon retention amount should be more. To make kerogen form complete networks as primary migration conduits that can pass through source rocks, source rocks should have a fairly high content of organic matter (Pepper, 1991). Stainforth and Reinders (1990) considered the Total Organic Carbon (TOC) content of 1.0% to be the minimum requirement, while Thomas and Clouse (1990) suggested that 2–2.5% is necessary. The analysis results of rock structural characteristics and hydrocarbon-bearing potential in source rocks show (Lei et al., 2015): the silty laminae and micro-fracture network together constitute the primary migration conduits in source rocks. The kerogen networks are more likely to form conduits with oil-wetting properties in the organic-rich argillaceous matrix rocks, so that the generated hydrocarbons can migrate to the silty laminae or micro-fractures under more general dynamic conditions. In this way, the kerogen content may be less and the corresponding kerogen networks can be limited to local areas. Of Cause, if the kerogen content is rich, the corresponding kerogen networks can be connected in larger area to form direct dominate conduits, and the primary migration should occur naturally and easily. Even for natural gas migration in source rocks by diffusion, its migration conduits and phase states may not be consistent. Referring to the diffusion-percolation hydrocarbon expulsion model proposed by Mann (1994), it seems that the primary migration of natural gas in source rocks occurs in stages: Hydrocarbons generated in the organic-rich argillaceous matrix in source rocks can occur in a diffusion mode; when encountering silty laminae and micro-fractures with better physical properties in source rocks, the hydrocarbons condense into a free state and migrate to the outside of source rocks along these conduits with better physical properties (Stainforth & Reinders, 1990; Thomas & Clouse, 1990). Therefore, for the normal process of hydrocarbon migration and accumulation for reservoir formation, no matter in what phase state and conduit the hydrocarbons migrate for the first time, it requires a certain excess pressure difference between source rocks and the outside to form sufficient hydrodynamic driving conditions (Magara, 1978; Ungerer et al., 1990). However, geological factors and effects, such as tectonic stress, temperature, thermal expansion of pore fluid and thermal cracking of organic matter, actually only change the value of fluid pressure, or only change the distribution state of pressure in source rocks. Therefore, the analysis of the dynamic conditions of hydrocarbon primary migration seems to be attributed to the study of the generation, evolution and distribution of overpressure in source rocks (Luo, 2001). 2. Simulation Analysis of Pressure Coupling Relationship Between Source Rock and Adjacent the imbalance Sandstones There are many possible mechanisms for abnormal fluid pressure in sedimentary basins (Osborne & Swarbrick, 1997; Smith, 1971). Quantitative analysis results show the following findings (Luo & Vasseur, 1992; Osborne & Swarbrick, 1997). Under the geological conditions of most sedimentary basins, some previously considered

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important pressurization mechanisms, such as hydrothermal pressurization, dehydration conversion of clay minerals and recrystallization of other water-bearing minerals, have little pressurization efficiency and are mostly negligible. For source rocks with poor seepage conditions, the main mechanisms of abnormal pressure formation are (1) the imbalance between increase of pore fluid volume caused by sediment compaction and drainage conditions and (2) hydrocarbon generation (Luo & Vasseur, 1992). (1) Establishment of Geological Model In actual basins, the pressures in high-pressure argillaceous rocks are often different from those of their interbedded permeable formations. The formation and distribution of overpressure in argillaceous rocks are not only affected by geological factors such as rock physical properties, fabric, rock thickness, diagenesis, basin subsidence rate and surface deposition rate, but also significantly influenced by the pressure state in the interbedded sandy stratum (Luo, 2001). To discuss the dynamic conditions for the primary migration of hydrocarbons in free state, the self-developed numerical basin model (Luo, 1998; Luo & Vasseur, 1992) was used to quantitatively simulate the pressure distribution characteristics in argillaceous rocks and sandstone under three conditions. The model can couple the change of pressure field with the temperature field, compaction and fluid flow during basin evolution, and has been rigorously tested (Luo & Vasseur, 1995). For details of model establishment, testing, boundary conditions and parameter selection, please refer to Luo and Vasseur (1992) and Luo (1998). The geological model is shown in Fig. 4.1, which consists of 4 layers of mudstone interbedded with 5 layers of sandstone, forming the hydrocarbon primary migration system of source rock—adjacent reservoir assemblage. In the simulation, the conditions and parameters of each shale layer, as well as that of each sandstone layer, are completely consistent, which not only reflect the pressure distribution characteristics in each formation at the final moment, but also show the history of pressure evolution in the bottom formation and the trend of pressure evolution in the upper formation. In Fig. 4.1a, the sandstone layers are completely enclosed by argillaceous rock layers, in which the fluid pressure is consistent at the boundaries of sandstone and argillaceous layers due to the propagation of pressure from the argillaceous layers. In Fig. 4.1b, the sandstone layers are always connected with the surface pressure-bearing aqueous formation, in which the fluid pressure is always maintained as hydrostatic pressure. In Fig. 4.1c, the sandstone layers are sealed by argillaceous rock layers for most time, and at a later time, an open fault made the upper and lower strata connected with each other and with the surface, and the pressure in the sandstone layers are changed into hydrostatic pressure in a very short time. (2) Simulation Results in Different Cases Under the condition that the sandstone is completely enclosed by argillaceous rocks, the overpressure in the sandstone mainly comes from the transfer of adjacent argillaceous rocks and remains similar to that of the latter (Magara, 1978; Smith, 1971). Case 1 in Fig. 4.2 shows the pressure distribution and corresponding porosity distribution

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b

c

Fig. 4.1 Geological model established for simulating pressure distribution in sandstone-mudstone interbedded section: a sandstone layers are sealed by interbedded mudstone layers from beginning to end; b sandstone layers remain connected to each other from beginning to end, and the pressure remains hydrostatic; c sandstone layers are initially completely sealed by the argillaceous rock layers, and at a certain moment a fault is opened, so that the sandstone layers are connected hydrodynamically

in sandstone-mudstone interbedded system. According to the figure, the excess pressure in sandstone is consistent everywhere, showing that the pressure increases with depth following hydrostatic pressure gradient; the pressures in mudstones are equal to that in sandstones at the boundary and increases toward the interior of mudstones (Fig. 4.2a). Because the compaction process of mudstone is quite different from that of sandstone, the changes of porosity between them are very different. However, within the argillaceous rock, the porosity changes gently from the boundaries to the centers (Fig. 4.2b). Under the condition that the pressure in sandstone is hydrostatic pressure, the pressure distribution in argillaceous rock is quite different from the above situation. Case 3 in Fig. 4.2a shows the simulation calculation results of pressure distribution in this system. When the fluid pressure in argillaceous stratum is relatively low (mudstone layer 1), the pressure gradually increases from the boundary to the center of the layer. When the given conditions make the pressure higher (mudstone layers 2 and 3), the pressure in mudstone layers changes slowly in the middle of the layer, and the closer to the formation boundary, the greater the pressure gradient. When the pressure is very high (mudstone layer 4), the transition zone near the formation boundary is very narrow, and the formation pressure in the layer increases with depth in gradient close to the lithostatic pressure. Corresponding to the pressure evolution, the compaction on the mudstone interface all proceeds normally, with small porosity, but slightly to the interior of the layer, the higher pore pressure hinders the compaction, with relatively large porosity (Fig. 4.2b).

4.1 Primary Hydrocarbon Migration and Estimation of Hydrocarbon … Pressures (MPa) 0

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Fig. 4.2 Pressure distribution and corresponding porosity distribution in sandstone-mudstone interbedded system under different sandstone connection conditions. Case 1—the sandstone layers are surrounded and sealed by mudstone layers; Case 2—the sandstone layers are just connected by the opening fault; Case 3—the sandstone layers keep always hydrostatic pressure

Under the condition that the pressure in sandstone first increases and then decreases, the open fracture passes through high-pressure formation, and the response of pressure in formations with different permeability is quite different. Curves 1 and 2 in Fig. 4.2a show the simulation results of the pressure distribution before and after the fracture opening on the section shown in Fig. 4.1c. Before the fracture opening, the sandstone was completely sealed by argillaceous rock. When the fracture is opened, the upper and lower strata are connected with each other and connected with the surface, the pressure in the sandstone layer becomes hydrostatic pressure in a very short time, and the distribution of the pressure in the mudstone layer changes suddenly near the boundary, but basically remains the same in the interior of the formation (Curve 2). Compared with curve 3 showing that the sandstone pressure always keeps hydrostatic pressure, curve 2 shows the process of pressure in sandstone first increases and then decreases, making the pressure in argillaceous rock layer relatively high, and the excess pressure gradient near its boundary is much larger. 3. Geological Conditions Conducive to Primary Migration The analysis of dynamic conditions of hydrocarbon primary migration can provide us with a macroscopic method to identify the characteristics of hydrocarbon primary migration. According to the previous simulation results, we will discuss which geological conditions are favorable for the existence of high excess pressure gradient in the expelling ranges in source rocks. (1) Possibility of Maintaining Hydrostatic Pressure in Sandstone When the source rocks mature and expel hydrocarbons, the depth is often larger, especially for the source rocks in the gas production stage. For such buried depth,

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it is not easy to keep low pressure in adjacent sandstone. In one possible case, the sandstone extends far laterally, spanning different structure units of the basin; one end buried in the hydrocarbon-generating area together with source rocks, while the other end is located on the slope or uplift of the basin, directly or indirectly connecting with the confined groundwater aqueous (Luo et al., 2000). In the other possible case, an early-developed and long-term opening fracture passes through the system composed of source rocks and sandstones. The open fracture continuously releases the fluid in sandstone, and the pressure in source rocks is basically unaffected by the fracture due to low permeability (Luo & Vasseur, 2016). (2) Conditions that the Pressure in Sandstone First Increases and Then Decreases In many cases, the source rocks and their interbedded sandstone are in the abnormal pressure zone, and their pressures are almost the same. Some sudden geological processes may cause the rapid decrease of pressure in sandstone. If an open fault passes through the source rocks and the interbedded sandstone, and connects with the low-pressure formation or pressure-bearing aqueous formation, the pressure of the sandstone will soon decrease, while the pressure of the source rocks will remain basically unchanged. The wide pressure difference between the two forms a high excess pressure gradient (Fig. 4.2), which can exist in a certain geological period after the fault opening (Luo, 1999). The preservation of fluid pressure in high-permeability sandstone often requires the enclosure of argillaceous rocks. Under suitable conditions, chemical diagenesis in sedimentary strata can also form a cemented crust (overpressure compartment) with good hydrodynamic sealing (Ortoleva, 1995). If the sandstone bed extends far laterally and its hydraulic connectivity is good, the fault connection may occur far away from the observation point. Therefore, the favorable primary migration range formed by fault activities can be very wide. Episodic fault opening-sealing is conducive to form and maintain high pressure gradient between source rocks and sandstones (Luo & Vasseur, 2016). (3) Geological Factors Affecting the Pressure Distribution Characteristics in Source Rocks The distribution of pressure and porosity in low-permeability formation often complement each other: The reason why the pressure in formation is higher is that the higher compaction degree at the formation boundary makes the permeability at the formation boundary lower, which is not conducive to the outward expulsion of pore fluid; however, the higher under-compacted degree in the formation keeps more pore fluid in the formation, and more fluid needs to be expelled in the later compaction process to achieve equilibrium, which is conducive to the increase of formation pressure. Therefore, the distribution of abnormal pressure in argillaceous rock is more special under the condition of low pressure of carrier bed. The shape and range of pressure distribution in formation are different due to the influence of different geological factors and the effect and process of different pressurization mechanisms. Figure 4.3 shows the pressure distribution in a mudstone layer enclosed by upper

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and lower sandstone layers with hydrostatic pressure, which is simulated under the conditions of changes in several geological factors. (4) Microfracture Generation During the maturation and evolution of source rocks, the formation fluid pressure increases rapidly under the two pressuring mechanisms of compaction and thermal degradation/cracking of organic matter. If the formation fluid pressure exceeds the rock fracturing threshold, it will lead to natural hydraulic fracturing, creating better conditions for the primary migration of hydrocarbon. To consider the generation of hydraulic fractures in the overpressured shales, Luo and Vasseur (2002) proposed a dynamic hydraulic fracturing model, which couples pressure evolution and microfracture opening. In this model, the formation of fracture is regarded as

Fig. 4.3 Pressure distribution in mudstone in sandstone-mudstone-sandstone interbedding system simulated under the change conditions of different geological factors: the dotted line in the figure is hydrostatic pressure, and the dot-and-dash line is hydrostatic pressure. a Compaction coefficient of argillaceous rock, curves 1, 2, 3, 4 correspond to 0.0004, 0.0008, 0.0012, 0.0016 m−1 respectively, and the deposition rate of overlying sediments is 1000 m·Ma−1 ; b Deposition rate of overlying sediments, curves 1, 2, 3, and 4 correspond to 10, 100, 1000, and 10,000 m·Ma−1 respectively, and the compaction coefficient of argillaceous rocks is 0.0008 m−1 ; c Argillaceous rock thickness, curves 1, 2, 3, 4, 5 correspond to 100, 200, 500, 1000, 2000 m respectively, the argillaceous rock compaction coefficient is 0.0008 m−1 , and the deposition rate of overlying sediments is 1000 m·Ma−1 ; d Porosity–permeability relationship coefficient of argillaceous rocks, curves 1, 2, 3, and 4 correspond to 10–3 , 10–5 , 10–7 , and 10–15 respectively, the compaction coefficient of argillaceous rocks is 0.0008 m−1 , and the deposition rate of overlying sediments is 100 m·Ma−1

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the result of interconnecting of microfractures. Microscopically, each microfracture opens or seals independently, controlled by the relationship between effective stress and strain of rocks. When the fluid pressure in the source rocks increases, the number of microfractures increases and tend to connect to each other to form a fracture. When the pressure continues to increase, the fracture continues to expand. Once the pressure reaches the fracture pressure threshold, the interconnected microfractures penetrate the rock, forming an open fracture. Hydrocarbons and formation water are expelled along the open fracture faster, and the formation pressure decreases accordingly. If the pressuring effect is strong, the fracture remains open, the formation pressure remains near the threshold, and its opening degree (measured by the corresponding permeability) depends on the relationship between the pressuring and the pressure losing due to expulsion of formation fluid. When the pressuring becomes weak, the open fracture gradually closes, and the corresponding permeability decreases until it is completely sealed. Figure 4.4 illustrates the modeled results on a vertical profile that is situated in the two-dimensional section illustrated in Fig. 4.4a. In Fig. 4.4, diagram a presents the pressure distribution at a given time, where four pressure curves are included: lithostatic, hydrostatic, fracturing threshold as well as pore pressure. Diagram b illustrates the distribution of permeability corresponding to the pressures in diagram a. In this diagram, the intrinsic permeability and the fracture permeability are presented. Diagram c shows the effective stresses state at five points vertically distributed in the shale. Their positions are marked in diagram a. In each scheme, the fracturing envelope and the Mohr circles are illustrated. The solid circle represents the actual stress state and the dotted one the stress state when fracturing would take place. During the hydraulic fracturing, in the portions of the mudstone formation near the boundaries, the pressure is not affected by hydraulic fracturing (Fig. 4.4a, c1, c2). The portion affected by hydraulic fracturing is mainly in the middle of the formation, where the fractures are formed that results in permeability increase (Fig. 4.4b, c3, c4, c5), making the fluid in the middle of the formation may be expelled outward. This is actually a dynamic process (Luo & Vasseur, 2002), during which the formation fluid is still continuously expelled and balanced with pressuring, resulting the fluid pressure keep stabile near the fracture pressuring threshold. In this process, values of fracture permeability do not exceed that of the intrinsic permeability of sediments by more than one order of magnitude. But it does efficiently diminish the potential high pressure and keep the pressure at the fracturing threshold. The porosity increase associated with fracturing seems to be negligible. The dynamic equilibrium process of microfracture opening—fluid expulsion—pressure decrease—microfracture sealing—permeability reduction—pressure increase—microfracture opening in source rocks is often the same process of hydrocarbon primary migration.

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4.1 Primary Hydrocarbon Migration and Estimation of Hydrocarbon …

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Fig. 4.4 Modeled results of fracturing happening in the shale. Left: the profiles of the pore pressure (upper) and the permeability in the shale at a specific time (lower, where kintr is the intrinsic permeability and kf the fracture permeability). Right: the effective stress states at different points marked in the upper left scheme. In diagrams c1 and c2, the dashed circle represents the state where the fracturing happens

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4.1.2 Estimation of Hydrocarbon Expulsion from Source Rocks The amount of hydrocarbons involved in hydrocarbon migration and accumulation is the sum of hydrocarbon expulsion from source rocks. Accurate estimation of hydrocarbon expulsion is very important for dynamic analysis of hydrocarbon migration and accumulation process and distribution rule, and it is also the premise for accurate evaluation of hydrocarbon resources (Pang et al., 2002). Previous have conducted a large number of in-depth explorations on hydrocarbon expulsion from source rocks, and put forward many calculation methods for hydrocarbon expulsion from different perspectives, including: organic matter thermal pressure simulation experiment method (Lewan et al., 1979; Saxby et al., 1986; Sweeney et al., 1995; Tissot, 1987), compaction hydrocarbon expulsion model method (Magara, 1978; Hao et al., 1994; Shi, 1994; Shi & Zhang, 2004), pressure difference-percolation model method (Shi, 1994), diffusion hydrocarbon expulsion method (Chen et al., 2002), saturation threshold method (Mi et al., 1994), material balance method (Xiao & Gao, 1998; Pang et al., 2002) and others. We have combined these methods with basin simulation technology for quantitative study of hydrocarbon expulsion history. However, the research degree of hydrocarbon expulsion history of source rocks is still far less than that of hydrocarbon generation history, and whether the existing methods are applicable requires to be tested through practical application. Because the hydrocarbon expulsion mechanism has not been completely understood so far, some calculation models of hydrocarbon expulsion based on hydrocarbon expulsion mechanism are controversial, and some key parameters involved in calculation are often not directly accessible. 1. Ideas and Existing Problems of Hydrocarbon Expulsion Estimation Previous estimation of hydrocarbon expulsion amount from source rocks is based on hydrocarbon generation amount multiplied by hydrocarbon expulsion ratio to obtain hydrocarbon expulsion amount (Tissot & Welte, 1984). The residual hydrocarbon amount in source rocks is naturally the difference between hydrocarbon generation amount and hydrocarbon expulsion amount. Hydrocarbon expulsion efficiency was later defined as the ratio of the amount of hydrocarbon expelled to the amount of hydrocarbon generated during the hydrocarbon migration and accumulation for reservoir formation (Chen et al., 2014; Leythaeuser et al., 1984a, 1984b). Generally, the expulsion ratio is obtained by using residual organic matter components in the source rocks to infer the expelled hydrocarbon amount and the total hydrocarbon generation amount (Cooler et al., 1986; Pepper, 1991; Leythaeuser et al., 1984a, 1984b; Zhang et al., 2006), or by estimating the proportion of hydrocarbons in the total amount of fluids expelled from source rocks (Li et al., 1992; Tian, 1990). These methods have certain applicability in the evaluation of hydrocarbon resources, but for the study of hydrocarbon migration and accumulation for reservoir formation, especially the study of multi-stage migration and accumulation, it is necessary to

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obtain the change process of hydrocarbon expulsion from source rocks, and it is not suitable to adopt the hydrocarbon expulsion efficiency. An important reason for proposing hydrocarbon expulsion efficiency is the hydrocarbon expulsion range and mechanism of thick source rock beds. According to the analysis of geochemical sections of source rocks (Tissot & Esbitalié, 1975; Mackenzie, 1983), previous researchers held that light components such as n-alkanes, isoprenes and others regularly change between the source bed boundaries and the interior, indicating that the hydrocarbon expulsion in thick source rock beds occurs within a certain range of the upper and lower boundaries, so its hydrocarbon expulsion efficiency is greatly affected by the thickness of the source rock beds (Leythaeuser et al., 1984a, 1984b, 1988). In recent years, the research on shale hydrocarbons has led to the understanding of the internal rock structure of source rocks, and the understanding of the hydrocarbon expulsion range of source rocks is also different from the past (Chen et al., 2014). Because silty laminae with relatively good porosity and permeability properties are common in argillaceous source rocks, hydrocarbons generated in argillaceous source rocks often preferentially enter these silty laminates. Microfractures generated in source rocks caused by abnormal high pressure connect these silty laminae to form dominant conduits for hydrocarbon expulsion outward source rock (Lei et al., 2015). In this way, the hydrocarbon primary migration in source rocks is actually a cyclic process: hydrocarbon generation, micro-distance migration, fracturing and outward primary migration. This mechanism enables hydrocarbons in the middle of thick source rock beds to migrate outward. For thick source rock beds, the hydrocarbons in the interior can only migrate through internal connected conduits, while the hydrocarbons at the edge of the source rock beds may have more conduit choices, and the hydrocarbon expulsion efficiency should be relatively high (Mackenzie & Quigley, 1988; Pepper, 1991). The distribution of high excess pressure in the source rock beds shown in Fig. 4.4a also shows that there is indeed a rapid change segment of excess pressure gradient in the boundary portions of the thick source rock beds, but at the same time, the corresponding increase in fracture permeability in the source rocks is conducive to hydrocarbon migration. The migration conduits and dynamic conditions in thick source rock beds are completely different from those near the upper and lower boundaries, and the fractionation of organic matter components in the process of hydrocarbon migration is naturally different. The limited thickness of organic component variation zones at the boundaries of source rock beds does not mean that primary migration does not occur in the center of source rock beds. Hydrocarbons of different components in source rocks can fill possible pore spaces in source rocks in various states such as free phase, adsorbed phase and dissolved phase (Lei et al., 2015). From the point of view of material balance, hydrocarbons generated in the thermal evolution of source rocks can only be expelled out of source rocks after satisfying the retention amount required in source rocks in various ways (Tissot & Welte, 1984; Pepper, 1991; Pepper & Dodd, 1995; Pang et al., 2012). Therefore, as long as the total amount of hydrocarbons generated in source rocks and the amount of hydrocarbons retained in source rocks are known, the difference is the amount expelled from source rocks. Therefore, to study the hydrocarbon migration

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and accumulation process in different stages, it is also necessary to determine the changes of hydrocarbon generation amount and hydrocarbon retention amount in the burial process of source rocks. The research on hydrocarbon generation amount has been conducted for a long time, and the method is relatively mature and reliable (Pepper & Corvi, 1995a, 1995b). The most direct method is to select representative samples from source rocks, obtain the relationship between hydrocarbon generation and pyrolysis temperature through pyrolysis simulation experiments (Behar et al., 1991a, 1991b), and calculate the hydrocarbon generation of source rocks (Wei et al., 2012). On the premise of the determined kerogen type in the source rocks in the study area, using the appropriate kerogen pyrolysis dynamic model and related parameters proposed by previous researchers, the hydrocarbon generation process of the source rocks during burial process can also be conveniently simulated, and the hydrocarbon generation amount under different burial depths can be obtained (Tissot & Espitalié, 1975; Wei et al., 2012). On the premise of the known hydrocarbon generation evolution process, if the hydrocarbon retention amount at different stages in the thermal evolution process of source rocks can be known, the hydrocarbon expulsion amount can be estimated (Fig. 4.5). The red curve in Fig. 4.5 shows the accumulated hydrocarbon generation by kerogen pyrolysis in source rocks, the blue line shows the hydrocarbon retention amount in source rocks at different evolution stages, and the brown dotted line indicates the hydrocarbon retention capacity of source rocks. Before kerogen in source rocks reaches the maturity threshold, there are only a few soluble hydrocarbons in rocks, which cannot meet the hydrocarbon retention in source rocks, and the hydrocarbon expulsion amount is always zero. When the depth of source rock increases, the kerogen maturity reaches the hydrocarbon generation threshold, and the amount of hydrocarbons generated in the source rocks increases rapidly, exceeding the requirements of the rocks for the hydrocarbon retention amount. After part of the hydrocarbons are expelled from the source rocks, the hydrocarbon amount generated in the source rocks continues to increase. As the rocks become denser and the hydrocarbon retention amount gradually decreases, the hydrocarbon expulsion amount continues to increase until the source rocks lose their hydrocarbon generation capacity. Therefore, the main problem in the estimation of hydrocarbon expulsion is how to accurately measure or estimate the hydrocarbon retention amount in source rocks under different maturity conditions. Chloroform bitumen “A” content and pyrolysis parameter S1 content can be used to estimate the retained hydrocarbon content in source rocks (Lu et al., 2012; Song et al., 2013). However, both Soxhlet extractor method and Rock–eval pyrolysis require a certain amount of source rock samples. During the pretreatment, heating and measurement of core barrel, sample preservation, drying, sample crushing, etc., there is serious light hydrocarbon loss in the sample (Chen et al., 2015; Cooler et al., 1986; Song et al., 2013), the higher the maturity of source rocks, the higher the proportion of light hydrocarbon components, and the larger the loss in the obtained samples. It is difficult to estimate the loss of light hydrocarbons (Cooler et al., 1986; Song et al., 2013).

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Fig. 4.5 Calculation model of hydrocarbon generation and expulsion amount of source rocks considering only residual hydrocarbons (Yan et al., 2015)

In other words, the residual hydrocarbons with higher molecular weight in source rocks are basically measured by various geochemical methods, while the light hydrocarbons that can be more important under subsurface in-situ conditions are difficult to recover. Such test data may be credible for the evaluation of hydrocarbon retention in low-maturity source rocks, but the evaluation of higher-maturity source rocks has undoubtedly low credibility. Secondly, under the current laboratory technical conditions, it is also difficult to ensure accurate measurement of retained hydrocarbons in source rocks. Organic solvents are often difficult to extract all soluble hydrocarbons in source rocks (Yan et al., 2015). The high temperature conditions of pyrolysis can cause cracking, composition differentiation and escape of residual hydrocarbons (Zou et al., 2012). Some researchers use the “solvent swelling phenomenon” in polymer physics to study the retention capability of kerogen in source rocks for hydrocarbons of different components, and further discuss the hydrocarbon expulsion efficiency of source rocks (Cai et al., 2007; Zou et al., 2012). The method is characterized by being carried out at a lower temperature (30 °C), and the measured hydrocarbon composition covers a wide range, which is helpful to analyze the retention capacity of kerogen for hydrocarbons of different components and study the effect of hydrocarbon component differences caused by kerogen. According to the analysis of lacustrine facies source rocks in the Fourth Member of Shahejie Formation of Bohai Bay Basin by Wei et al. (2012) and Yan et al. (2015), they concluded that the hydrocarbon retention amount in source rocks obtained by solvent swelling method is about twice as high as that obtained by hydrocarbon index method obtained by differential thermal analysis (Yan et al., 2015). In essence, this method only considers the capacity of kerogen to retain hydrocarbons. This method does not represent the actual amount of hydrocarbons retained by kerogen, nor does it consider the retention of hydrocarbons in other pore spaces, surface and interior of granular minerals in source rocks. The observation results of

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microscopic characteristics of hydrocarbon occurrence in shale hydrocarbon reservoirs show that the amount of hydrocarbons retained in source rocks in other ways is significant and may even exceed the hydrocarbon retention capacity of kerogen (Lei et al., 2015). In recent years, people have tried to directly measure the amount of hydrocarbons expelled during the maturation of source rocks through thermal simulation experiments of source rocks (Qin et al., 2013; Chen et al., 2015), which provides an alternative idea and method for the estimation of hydrocarbon expulsion. However, pyrolysis experiments have difficult similarity problems in simulating the hydrocarbon generation process of organic matter under actual geological conditions (Zou et al., 2012). Firstly, for the hydrocarbon expulsion conditions during the experiment, although the subsurface temperature and pressure conditions have been considered as much as possible in these experiments, and even the amount and composition of hydrocarbons expelled under the pyrolysis reaction conditions can be measured in real time (Chen et al., 2015), such hydrocarbon expulsion conditions are still quite different from the actual subsurface source rock formation. Secondly, for the simulation of hydrocarbon expulsion in the gas generation stage with higher maturity, during the process of formation burial heating, kerogen is thermally degraded to generate liquid oil and a part of natural gas. When the temperature rises to the temperature required for thermal cracking, the remaining solid kerogen and the generated oil will be cracked into natural gas. In this complex pyrolysis process, the hydrocarbons generated in the source rocks may supplement the retained hydrocarbon amount in the rocks, or be expelled at any time. Therefore, the conditions of sealing, semi-sealing and opening in the experimental process may be far from the actual situation. Thirdly, the preservation and determination of the original residual hydrocarbon amount in the sample are important factors affecting the experimental results (Chen et al., 2015). The process of drilling and coring in shale gas reservoirs shows that even the source rocks in the oil generation stage have a wide distribution range of internal hydrocarbon components. Light hydrocarbons including methane will continuously escape during drilling, coring and later preservation. The total loss amount is difficult to estimate, but it obviously accounts for a considerable proportion of the total hydrocarbon amount in the source rocks (Cooler et al., 1986; Hunt et al., 1980; Jiang et al., 2016). These hydrocarbons should be an important portion of the in-situ retained hydrocarbons in source rocks (Xue et al., 2015; Zhu et al., 2015). In addition, to compensate for the effect of time in the thermal evolution of organic matter in the laboratory, the heating temperature must be raised. However, although such a temperature range and heating rate meet the needs of organic matter pyrolysis to a great extent, it inevitably causes transformation, disintegration and phase change of rock minerals, which completely changes the rock structure, pore throat characteristics and hydrocarbon adsorption capacity. The retention effect of source rocks on hydrocarbons of various phase seems quite different from the actual situation. Regardless of the accuracy of the measurement of residual hydrocarbon amount in source rocks, only the value under the current buried depth can be measured in each sample. To recover and establish the change relationship of residual hydrocarbon

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amount in the geological history of source rocks, it is necessary to collect and measure enough similar source rock samples with different maturity. Such requirement is difficult to meet in the actual study area: In addition to similar source rocks (including source rocks with similar lithology and kerogen type), a series of requirements such as similar thermal evolution processes, similar post-sampling processing and similar preservation conditions are also required. Therefore, it is an important method to establish a residual hydrocarbon model for source rocks of different parent materials by using a large amount of data on the residual hydrocarbon amount in similar source rocks in different regions (Chen et al., 2014; Pang, 2003; Pepper, 1991). Based on previous test results, the hydrocarbon generation potential of the same type of kerogen varies with maturity almost consistently in both marine and continental source rocks, but the distribution of residual hydrocarbon amount varies very differently (Chen et al., 2014), which is not only related to the amount and maturity of organic matter, but also related to the rock type and mineral composition characteristics of source rocks (Chalmers & Bustin, 2012; Chen et al., 2015; Curtis, 2002; Daniel et al., 2008; Pepper & Corvi, 1995a, 1995b). 2. Hydrocarbon Generation Potential Modeling Method Hydrocarbon generation and expulsion potential method is an estimation method of hydrocarbon expulsion based on the hydrocarbon expulsion threshold theory by combining the hydrocarbon generation and expulsion mechanism of source rocks with the principle of material balance (Pang, 1995; Pang, 2003; Zhou & Pang, 2002). This method establishes hydrocarbon generation and expulsion model based on a large amount of rock pyrolysis data. It avoids the low reliability deficiency of hydrocarbon expulsion calculation results caused by incomplete consideration of influencing factors and inaccurate selection of parameters in the current calculation models. It features easy acquisition of required data, simple and feasible method, high reliability and the like, and can be applied to areas with any exploration degree (Guan et al., 2005; Jiang et al., 2007; Zhou et al., 2006; Zhu et al., 2008). Most of the hydrocarbon-bearing basins in China are the superimposed basins which have experienced multi-stage tectonic evolution, multi-stage hydrocarbon generation and expulsion, and multi-stage hydrocarbon migration and accumulation (Jin & Wang, 2004). As mentioned earlier, the study of hydrocarbon migration and accumulation in such a basin should divide the hydrocarbon migration and accumulation units according to different hydrocarbon reservoir forming stages, and the total hydrocarbon expulsion amount in different hydrocarbon generation and expulsion stages should be estimated. However, directly dividing different stages in the model shown in Fig. 4.5 will cause some problems in calculating the cumulative hydrocarbon generation and expulsion amount in each stage, and it is necessary to improve the hydrocarbon generation and expulsion potential model method. Generally, in basins with continuous increase in formation temperature, the hydrocarbon generation process of source rocks is continuous, and the hydrocarbon expulsion process should also be continuous. In this way, the hydrocarbon expulsion model shown in Fig. 4.5 is appropriate. However, when the hydrocarbon generation process is interrupted by basin uplift, denudation and temperature decrease, the migration

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and accumulation process with source rocks as hydrocarbon source may be divided into several stages. If there is no obvious change in the basin temperature field during the evolution of the basin, the cumulative hydrocarbon generation amount and hydrocarbon expulsion model shown in Fig. 4.5 will basically not change. As long as the appropriate boundary of hydrocarbon reservoir forming stage can be determined, such model can still be appropriate. If the basin temperature field changes obviously in different tectonic evolution periods, the burial depth of the source rocks that have stopped hydrocarbon generation will probably change greatly when they enter the hydrocarbon generation stage again. To avoid the problems caused by this situation, we can set the ordinate as the maturity of source rocks. As long as source rocks return to the maturity condition before uplift and denudation, the hydrocarbon generation and expulsion process will continue. In this way, the hydrocarbon generation process is intermittent, but the curve in the hydrocarbon expulsion model is continuous, easy to use. Based on the evolution model of hydrocarbon generation amount—hydrocarbon retention amount shown in Fig. 4.5, an estimation model of hydrocarbon expulsion amount based on pyrolysis analysis data can be obtained (Fig. 4.6). In the model, the ordinate is maturity, and the abscissa is the relative content of normalized organic components, which are the absolute amount measured in the samples divided by the original organic carbon content TOC0 in source rocks. S 1 0 and S 2 0 are the relative contents of the initial soluble hydrocarbon and pyrolytic hydrocarbon before the hydrocarbon generation threshold. S1 and S2 are the relative contents of soluble hydrocarbon and pyrolytic hydrocarbon measured with in situ samples. The value ranges of S 1 0 and S 2 0 are obviously different in source rocks with different kerogen types, that corresponds to different thresholds of hydrocarbon generation and expulsion (Chen et al., 2014; Zhong et al., 2004). Therefore, separate modeling is required according to the type of organic matter in source rocks. In the model shown in Fig. 4.6, the sum of S 1 0 and S 2 0 is considered as the hydrocarbon generation capacity of source rock. When a source rock is buried to the mature threshold, kerogen pyrolysis occurs to generate hydrocarbons, S 2 gradually decreases, while S 1 gradually increases. However, the increase of S 1 does not correspond to the expulsion of hydrocarbons, because the generated hydrocarbons must first meet the hydrocarbon retention in source rocks. Only when the amount of hydrocarbons generated in source rocks exceeds its retention capacity, the source rocks reach the hydrocarbon expulsion threshold, and the hydrocarbons begin to be expelled. In the model, the hydrocarbon expulsion threshold lags behind the hydrocarbon generation threshold, and the difference in burial depth or maturity between them is affected by many factors. The two curves in the model are the cumulative changes of various organic matter contents with maturity. When calculating the amount of hydrocarbon retained and expelled from the volume unit in source rocks, it needs to be multiplied by the original organic carbon content TOC0 in the actual source rock: qz = TOC0 ∗ S1

(4.1)

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Fig. 4.6 Calculation model of hydrocarbon expulsion amount in source rocks undergoing multistages of hydrocarbon generation and hydrocarbon expulsion processes

qp =

[(

) ] S10 + S20 − (S1 + S2 ) ∗ TOC0

(4.2)

where qz and qp are respectively the amount of retained hydrocarbons and the amount of expelled hydrocarbons in the source rock unit. Regarding the recovery of original organic carbon, previous have proposed different methods (Chen et al., 2003; Xiao & Gao, 1998; Zhong et al., 2004). All methods have certain theoretical basis, but they are not accurate and rigorous enough. The thermal evolution process of source rocks occurs with the burial process, and the loss of organic carbon caused by the expulsion of hydrocarbons generated during thermal evolution may be considerable. The source rocks undergoes a diagenetic

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process dominated by compaction, which reduces the porosity of the source rocks. The mass of solid matters in a unit of rock volume increases much comparing to that in the original rock unit. In source rocks containing type III kerogen, the content of organic carbon is basically unchanged before the pyrolysis of a large amount of natural gas, but the recovery coefficient of organic carbon in mature source rocks containing type I kerogen may be as high as 2.5 (Zhong et al., 2004). With the hydrocarbon generation and expulsion model shown in Fig. 4.6 the total hydrocarbon expulsion Qp of the source rock unit during a certain basin evolution period can be obtained by integral calculation of the corresponding maturity stage: ∮

Ro,

j+1

10−3 qp (Ro ) · ρb (Ro ) · dRo

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(4.3)

j

where j is a certain stage of migration and accumulation happened in basins, Ro,j and Ro,j+1 are the maturity degrees corresponding to the beginning and end of the stage, and ρb (Ro ) is the rock density of the source rock unit at Ro . The obtained formula (4.4) cannot be directly used in practice, because the thickness, organic matter abundance, organic matter type and burial depth of source rocks in different stages may vary greatly in actual basins and cannot be calculated in a simple way. To unify the calculation methods of hydrocarbon expulsion and retention of source rocks, we suggest to adopt the cumulate method of source rock units. For the source rocks in a certain horizon in a region, firstly, according to the basin structure characteristics and the distribution intensity of source rocks in the study area, the study area is divided into grids with a certain density and the grids are numbered. The basic principle is that the thickness, organic matter abundance and organic matter type of source rocks in each grid range can be essentially considered to be uniform. The area S of each grid unit can be set to be consistent, but the effective thickness hi , organic matter abundance TOCi , organic matter type and thermal maturity Ro of source rocks in each grid may be different in the same stage. Therefore, for each grid unit, in a certain basin evolution period j, the total hydrocarbon expulsion j Q ip of source rocks in the unit can be calculated by the following formula: j+1

R∮o, i j

Qi p =

10−3 qpi (Ro ) · ρbi (Ro ) · S · h i (Ro ) dRo

(4.4)

j Ro, i

where qp i (Ro ) is the relationship between the hydrocarbon expulsion amount and the maturity Ro as indicated in Fig. 4.6; ρbi (Ro ) and hi (Ro ) are the average density and average thickness of source rocks in grid i, respectively, and considering the change of diagenesis with burial depth, they are both defined as functions of maturity. In this way, for a certain period, the hydrocarbon expulsion of the whole study area is the sum of the hydrocarbon expulsion of each grid unit:

4.1 Primary Hydrocarbon Migration and Estimation of Hydrocarbon …

251

j+1

j Q qp =

R∮o, i n ∑ i=1

10−3 qpi (Ro ) · ρbi (Ro ) · S · h i (Ro ) dRo

(4.5)

j Ro, i

where n is the total number of grid units. The total hydrocarbon expulsion in the basin evolution process in the study area is the sum of hydrocarbon expulsion in different stages: j+1

Q qp =

R∮o, i n m ∑ ∑ j=1 i=1

10−3 qpi (Ro ) · ρbi (Ro ) · S · h i (Ro ) dRo

(4.6)

j Ro, i

where m is the total number of migration—accumulation stages during whole basin evolution. Admittedly, for areas where universal models of hydrocarbon generation and retention have not yet been established, it is often difficult to establish a definite organic matter generation and expulsion relationship qi(Ro ) in practice. Then the average values of the parameters within the limited range of temporal and spatial scales may be used. If the density and thickness of source rocks are also replaced by the average values of different stages, the above formulas (4.6) can be simplified as: j Q qp =

n ∑

10−3 q i,p j · ρ i,b j · S · h i, j

(4.7)

i=1

where the mark ‘–’ upon the variables indicate the average value of the relevant variable of source rocks in the grid unit. Formula (4.7) can be simplified as: Q qp =

m ∑ n ∑

10−3 q i,p j · ρ i,b j · S · h i, j

(4.8)

j=1 i=1

To make the estimated hydrocarbon expulsion quantity more reliable, the division of basin evolution periods can be as detailed as possible according to the abundance of data. For different hydrocarbon reservoir forming stages, no matter whether the hydrocarbon migration and accumulation process occur in a quasi-continuous or episodic way in each stage, only the total amount of hydrocarbons expelled from source rocks in this stage can be considered in the study. After the main reservoir forming stages in the study area are determined, the hydrocarbon expulsion in several time stages can be cumulatively calculated. Therefore, the model shown in Fig. 4.6 provides the schematic relationship of the obtained expelled amount in each stage. If the stages of migration and accumulation process can be determined as three in the study area, the total hydrocarbon expulsion in each stage is QI , QII and QIII respectively.

252

4 Quantitative Evaluation Method of Hydrocarbon Migration …

Fig. 4.7 Hydrocarbon expulsion model of source rocks of the third member of Shahejie formation in Chengbei fault-step zone of Qikou Sag, Bohai Bay Basin

The above model is used to estimate the hydrocarbon expulsion of source rocks of the Third Member of Shahejie Formation in Chengbei Fault-step Zone of Qikou Sag in Bohai Bay Basin. Through rock pyrolysis tests on a large number of representative source rock samples, the hydrocarbon expulsion model diagram (Fig. 4.7) of source rocks of the Third Member of Shahejie Formation is established by using the measured S 1 , S 2 and their corresponding organic carbon content data. On the basis of the determined hydrocarbon expulsion threshold of source rocks, the hydrocarbon expulsion ratio, hydrocarbon expulsion efficiency and hydrocarbon expulsion rate in different thermal evolution stages are obtained. As can be seen from Fig. 4.7, on the buried depth section, the hydrocarbon expulsion threshold depth of the Third Member of Shahejie Formation is about 3450 m, where the hydrocarbon generation potential index of source rocks starts to change from increasing to decreasing, and its corresponding vitrinite reflectance Ro is about 0.62%. After the hydrocarbon expulsion ratio and efficiency of source rocks reach the hydrocarbon expulsion threshold, they increase with the thermal evolution degree, and the increasing trend changes from rapid increase to slow increase, finally approaching a certain value. The maximum hydrocarbon expulsion ratio of source rock is about 420 mg/g, and the maximum hydrocarbon expulsion efficiency is about 69%. The maximum hydrocarbon expulsion rate of source rocks corresponds to a buried depth of about 3820 m, and the corresponding vitrinite reflectance Ro value is 1.2%. According to the above hydrocarbon expulsion model and hydrocarbon expulsion ratio—Ro model, combined with the distribution of geological and geochemical parameters of source rocks (organic carbon content, density, source rock thickness, etc.) and the recovery results of thermal evolution history, the hydrocarbon expulsion intensity of source rocks in different geological stages is calculated. Figure 4.8 is the

4.1 Primary Hydrocarbon Migration and Estimation of Hydrocarbon …

253

cumulative hydrocarbon expulsion intensity diagram of source rocks of the Third Member of Shahejie Formation in the study area in different stages. It can be seen from the figure that Qikou Sag and Qibei Sag are the two most important hydrocarbon expulsion centers in the study area, with hydrocarbon expulsion intensity reaching 1000 × 104 –1600 × 104 t/km2 , and the hydrocarbon expulsion intensity of Qinan Sag extending southward from Qibei Sag is also relatively high, generally 400 × 104 – 800 × 104 t/km2 . From these three hydrocarbon expulsion centers to the surrounding uplift area, the hydrocarbon expulsion intensity decreases, and the source rocks in most areas south of Yangerzhuang-Zhangdong-H4 Well area have not reached the hydrocarbon expulsion threshold in geological history. Based on the calculation of hydrocarbon expulsion intensity and cumulative hydrocarbon expulsion intensity in each stage, the hydrocarbon expulsion amount of source rocks of the Third Member of Shahejie Formation in the main migration and accumulation units are calculated by using formula (4.9). The results (Table 4.1) show that the hydrocarbon expulsion peaks of the three migration and accumulation

Fig. 4.8 Cumulative hydrocarbon expulsion intensity diagram of source rocks of the third member of Shahejie formation in Chengbei fault-step zone

254

4 Quantitative Evaluation Method of Hydrocarbon Migration …

Table 4.1 Calculation results of hydrocarbon expulsion amount of source rocks of the third member of Shahejie formation in different migration and accumulation units at different stages in Chengbei fault-step zone Geological age

Time (Ma)

Hydrocarbon expulsion (× 108 t) Western unit

Present

0

0.1257

Central unit 3.8554

Eastern unit 0.9798

Total hydrocarbon expulsion (× 108 t) 4.9609

Nmupper end

2

0.1294

5.3228

1.3756

6.8278

Nmlower end

5.1

0.053

4.3989

1.1339

5.5858

Ng end

12

0.0042

4.5357

1.1256

5.6655

Ed1+2 end

24.6

0.0003

3.8382

0.7599

4.5984

Ed3 end

27.8

0

0.1575

0.0225

0.1800

Es1 end

29.3

Cumulative

0 0.3126

0.0227

0.0023

0.025

22.1312

5.3996

27.8434

units in the study area mainly occurred at the end of Minghuazhen formation deposition period, but the times of large-scale hydrocarbon expulsion of the three units are quite different. The hydrocarbon expulsion of the central migration and accumulation unit reached from 3.8382 × 108 t at the end of Dongying formation deposition period to the peak of hydrocarbon expulsion 5.3228 × 108 t, while the eastern and western migration and accumulation units only have 1.3756 × 108 t and 0.1294 × 108 t at the peak of hydrocarbon expulsion. The cumulative hydrocarbon expulsion of source rocks of the Third Member of Shahejie Formation is 27.8434 × 108 t, of which the western migration and accumulation unit has the lowest hydrocarbon expulsion, only 0.3126 × 108 t, and the central migration and accumulation unit has the highest hydrocarbon expulsion, reaching 22.1312 × 108 t. From the estimation results of hydrocarbon expulsion, it is inferred that the central migration and accumulation unit in the study area has great potential for hydrocarbon exploration and can be used as the key area for further exploration.

4.2 Estimation Method of Hydrocarbon Losses During Secondary Hydrocarbon Migration During the secondary migration of hydrocarbons from source rocks into the carrier bed, a considerable amount of hydrocarbon is lost due to rock adsorption, pore water dissolution and other residuals in the migration conduits (Hirsch & Thompson, 1995; Luo et al., 2007a, 2007b, 2007c), these hydrocarbon losses often affect hydrocarbon accumulation in traps, and may also affect the direction of hydrocarbon migration and the distribution of hydrocarbon migration amount in different directions (Lei et al., 2016; Luo, 2011). This is a part that must be considered when evaluating the potential of hydrocarbon resources by the material balance method (Lewan et al., 2002; Pang, 1995). Therefore, the estimation of hydrocarbon losses during secondary migration is

4.2 Estimation Method of Hydrocarbon Losses During Secondary …

255

very important for hydrocarbon resource evaluation, exploration direction selection and remaining hydrocarbon resource development (Lewan et al., 2002; Pang, 1995). At the basin scale, besides gas phase diffusion being an important factor affecting natural gas loss, the amount of residual hydrocarbons in the secondary hydrocarbon migration pathway mainly depends on the spatial geometric characteristics of the migration pathways and the saturation of residual hydrocarbons within the pathways (Luo et al., 2004). However, because the hydrocarbon migration pathways passing through carrier beds is generally extremely heterogeneous (Luo et al., 2004, 2008; Schowalter, 1979), it is almost impossible to directly observe the dynamic process of hydrocarbon migration (Garden et al., 2001), and the scale of the pathways can only be demonstrated through various indirect means. Therefore, little is known about the spatial dimensions of migration pathway and residual hydrocarbon saturation. For a long time, the limitation of understanding makes vague conclusions on the amount of residual hydrocarbons in the secondary migration conduits, and the calculation methods of secondary migration losses proposed by previous look very rough (Fu et al., 1999; Jiang et al., 2002; Shi et al., 2000). In recent years, we have made great progress in the study of physical simulation and numerical simulation methods of the hydrocarbon secondary migration mechanism. The numerical simulation method can quantitatively describe the formation process, quantity, geometrical morphology and distribution range of the secondary migration pathway (Luo et al., 2004), and obtain the residual oil saturation on the migration pathway through experimental methods (Luo et al., 2004; Zhang et al., 2003). Here, mainly based on the understanding of the pathway characteristics in different stages of secondary migration and the changes of residual oil saturation in the pathway obtained from physical experiments and numerical simulation experiments, we propose a mathematical model (Luo et al., 2007a, 2007b, 2007c, 2008) for reasonably estimating the hydrocarbon losses in the secondary migration pathway at the basin scale. And the First Member of Yaojia Formation in the northwest of Songliao Basin is taklen as an example for practical application.

4.2.1 Model of Hydrocarbon Secondary Migration Process If hydrocarbon accumulation with commercial significance is formed in hydrocarbon-bearing basins or in migration- accumulation units, the secondary migration must first occur in carrier beds lying within in source bed range with considerable oil collecting area. In this range, the size of oil catchment area depends on the spatial distribution of effective source rocks on the one hand, and the interbedding permeable carrier beds on the other hand. Generally, the source rock series with large total thickness but small thicknesses of single layers has better hydrocarbon expulsion conditions and oil catching area. Within the source rocks, the hydrocarbon expulsion conditions are extremely harsh, and the hydrocarbon generation times at different positions are not completely consistent with others. And whichever place reaches the hydrocarbon expulsion conditions first will expel hydrocarbons outward.

256

4 Quantitative Evaluation Method of Hydrocarbon Migration …

In addition, due to the episodic characteristics of hydrodynamic evolution in source rocks, hydrocarbon expulsion from source rocks generally occurs in an episodic manner, with a limited amount of hydrocarbon expulsion each time, but a long total hydrocarbon expulsion duration (Luo, 2001; Mann et al., 1997; Xie et al., 2001). In the lateral direction, hydrocarbon expulsion from different locations may not occur simultaneously. Therefore, the hydrocarbon expulsion process should be a process in which hydrocarbon expulsion occurs one after another on the surface of source rock beds in a certain geological period. The amount of hydrocarbons expelled from source rocks into the proximate carrier bed at a certain point is extremely limited, and the height of continuous hydrocarbon column is very small, so its buoyancy is difficult to overcome the capillary force of the carrier bed at that position to make hydrocarbons float upward. After the gradual accumulation of hydrocarbons expelled many times and from multiple points, the hydrocarbons staying at the bottom of the carrier bed converge with each other, and the volume of continuous hydrocarbons and the height of hydrocarbon column gradually increase. At a certain moment, when the buoyancy corresponding to the height of hydrocarbon column is large enough to overcome the minimum capillary force, secondary migration can occur (Chapman, 1982). In this process, after a migration pathway is formed, hydrocarbons in adjacent contiguous areas within a certain range at the bottom of the carrier bed may migrate upward along the pathway (Fig. 4.9). When the hydrocarbons migrate vertically and reach the top of the carrier bed, the upper caprock acts as a check on upper direction, and hydrocarbons can only migrate in the updip direction, along a relatively small thickness range of the top of the carrier

Fig. 4.9 Model of gradual accumulation and local breakthrough of hydrocarbons expelled from source rocks at the bottom of carrier bed (modified according to Chapman, 1982)

4.2 Estimation Method of Hydrocarbon Losses During Secondary …

a

257

b

Fig. 4.10 Characteristics of hydrocarbon migration pathways and its stage division scheme in classical hydrocarbon secondary migration—accumulation model. a Generalized cross-section and b bird view from above

bed (Thomas & Clouse, 1995). If the dip angle of the carrier bed is small, the buoyancy of hydrocarbon vertical upward migration in the carrier bed generally may not make hydrocarbons continue to migrate laterally, and hydrocarbons must accumulate under the caprock for some time, until the more hydrocarbons subsequently migrate and join the accumulate to generate enough buoyancy for hydrocarbon lateral migration (Lerche & Thomsen, 1994). Therefore, in the assemblage of a set of source rock and carrier bed in the actual basin, the secondary migration of hydrocarbons within the hydrocarbon expulsion range of the source rocks can be divided into two stages (Fig. 4.10): One is the vertical migration of hydrocarbons in the carrier bed within the hydrocarbon expulsion range of the source rocks; the other is the lateral migration of hydrocarbons at the top of the carrier bed under the caprock. If the hydrocarbon lateral migration in source rock range does not encounter a suitable trap, the hydrocarbons will continue to migrate laterally outside the source rock range (Hindle, 1997; Thomas & Clouse, 1995). Because there is no hydrocarbon supplied from the underlying formation, the spatial distribution characteristics of hydrocarbon migration pathway outside the hydrocarbon expulsion range of source rocks are quite different from those within the range.

4.2.2 Proportion of Hydrocarbon Migration Pathway to Migration Conduit 1. Vertical Migration within Hydrocarbon Expulsion Range of Source Rocks In the hydrocarbon expulsion range of source rocks, hydrocarbons first form contiguous accumulation at the bottom of the carrier bed, and then migrate upward almost vertically. The results of laboratory simulation and numerical simulation analysis (Hirsch & Thmpson, 1995) show the follow findings: When entering from the bottom of a square porous medium unit, hydrocarbons first accumulate gradually on the bottom and then migrates upward along several pathways; due to the competitive

258

4 Quantitative Evaluation Method of Hydrocarbon Migration …

Fig. 4.11 Concept and characteristics of independent vertical migration unit. a Characteristics of migration pathways in the condition of 40 × 40 × 40 grids; b characteristics of migration pathways in the condition of 100 × 100 × 100 grids

relationship between pathways, the development of one pathway limits the development of others. On the one hand, the total hydrocarbon supply at the bottom is not enough to satisfy the simultaneous growth of all the formed pathways (Tokunaga et al., 2000); on the other hand, the pathway with the highest rise forms the highest hydrocarbon column height, and its top buoyancy is the largest (Hirsch & Thmpson, 1995). As a result, the total number of pathways decreases with the height of migration, and finally only one pathway can reach the top boundary of the system (Fig. 4.11). When vertical migration occurs, several petroleum clusters may move upwards within a relatively small domain. Gradually, the clusters stop moving one after the other and ultimately only one continues to grow (Fig. 4.11; see Hirsch & Thompson, 1995). This is due to competition among the clusters; the development of some clusters inhibits the others—those that grow more quickly usually possess a larger buoyancy force (Hirsch & Thompson, 1995). Under actual migration conditions, the domain in which the growing clusters are in competition with one another must be finite. Such a domain may be considered as a prism with square top and bottom faces, and is referred to here as an independent (vertical) migration unit (IMU). Due to the microscopic heterogeneity of migration conduits, the pathway-toconduit ratio of secondary migration decreases rapidly with the height of migration pathway in the system of complete hydrocarbon continuum at the bottom. The research of Hirsch and Thompson (1995) can be used to estimate the ratio of migration pathways in all conduits in an independent vertical migration unit. Their research found that at the bottom of the cubic carrying space, the ratio of pathways in conduits is very high, basically reaching more than 50%. With the increase of the height from the bottom, the ratio decreases rapidly. Under the condition that the bottom surface

4.2 Estimation Method of Hydrocarbon Losses During Secondary …

259

of the unit is large enough, the ratio decreases to the minimum value within a certain height, and then the value remains basically unchanged with the increase of height. Based on the experiments of Hirsch and Thompson (1995), the pathway saturation, Sc (or the cluster saturation) in such a prism may be defined as: Sc = a L −b m

(4.9)

where L m is the specific length of the base of the IMU, a is a constant depending on the migration pattern (Luo et al., 2004), and b = 0.5 (Hirsch & Thompson, 1995). The volume of migration pathways within an IMU may be estimated by: Q u1 = φ × h × L 2m × Sc

(4.10)

where φ is the porosity, h is the height of the IMU, and h × L 2m is the volume of an IMU. At the basin scale, the content of vertical migration clusters within the source area, Q 1 , should be: Q 1 = n Q u1 = φ · St · Sc · h

(4.11)

where n is the number of IMUs within the source area, St = n × L 2m is the surface area of the source area, and h is the vertical thickness of the carrier. 2. Lateral migration within an effective source area Migrating petroleum arriving at the top of the carrier will continue to migrate laterally. The point at which the vertical migration pathway reaches the overlying seal and lateral migration begins may be defined as the “turning point”. Within an IMU, the position of the turning point at the top of the reservoir is chosen at random. For lateral migration, the growth of clusters is limited at the top of the carrier; hence, pathways may occupy only a relatively thin layer in the upper part of the IMU (Thomas & Clouse, 1995). In actual situations, this generally represents a homogeneous sandstone or an upward coarsening sequence, while in a fining upward sequence, greater losses result due to the “trapping” of clusters (Karlsen & Skeie, 2006). This pathway is formed in a three-dimensional space with a limited upper boundary, which has been studied by previous researchers (Yan et al., 2012). If the thickness of the lateral migration pathway under the caprock is approximately equal to the width of the pathway in the vertical migration stage, then the pathwayto-conduit ratio of a single migration pathway can be considered to be essentially S c . For a single independent migration unit, the top area of the migration pathway is about: S2 = Sc L e

(4.12)

where L e is the distance from the migration turning point to the boundary of migration unit along the upward dip direction of the top surface of the carrier bed when

260

4 Quantitative Evaluation Method of Hydrocarbon Migration …

the hydrocarbons migrate vertically to the top boundary of the carrier bed. L e is a randomly varying value, and the expected value is 0.5 L m . When multiple IMUs join together, the migration pathways in each IMU grow in an upslope direction and may grow into adjacent IMUs where they may travel through the top surface and/or join existing pathways (Luo et al., 2004). Therefore, on the basin scale, the pathway-to-conduit ratio of lateral migration pathway within source rock range is: n ∑

L ie Sc > nS2 > nSc L m

(4.13)

i=1

where L ie is the distance from the migration turning point in the number i independent migration unit to the boundary of the source rock range in the upward dip direction. Here, we give two endmember values. The value on the left indicates that the hydrocarbons migrating from one migration unit never encounter other pathways when passing through other units, and reach the edge of the source rock range, while the value on the right indicates that the hydrocarbons migrating from one migration unit encounter the pathway of the adjacent units with the shortest distance when passing through. Previous physical simulation experiments show (Luo et al., 2004) that the hydrocarbons migrate in the later stage along the formed pathway when encountering the formed pathways, and under the condition that the migration dynamics do not vary significantly, the morphology of the pathways basically does not change. When the hydrocarbons migrating in different migration units encounter the independent pathways formed by other units, the pathways will be used for migration, but the morphology of the pathways will not be changed, and the range of the pathways will not be expanded. Therefore, the value of S2 in Eq. (4.12) should be somewhere in between, closer to the value on the right. The characteristics of lateral migration pathways within an effective source area can be analyzed using a simulator (Fig. 4.12a). The size range of the IMU is assumed to be 100 × 100 bonds, and migration processes are simulated from 1 × 1 to 5 × 5 IMUs. The simulation result in Fig. 4.12b show that under these conditions, the average value of Sl in a single IMU is about 8%; this value increases as the numbers of IMUs, Nm , increase (Fig. 4.12b). The increase in Sl ends at the value Sl' while the number of IMUs is sufficiently large. At a basin scale, the hydrocarbon content in lateral migration clusters within an effective source area Q 2 should be: Q 2 = n Q u2 = nϕ Sl' L 2m dl = ϕ St Sl' dl

(4.14)

3. Lateral migration outside the effective source area Hydrocarbons arriving at the boundaries of a source area will continue to migrate laterally as long as a seal is not encountered. In contrast to lateral migration within the source area, there is no migration of hydrocarbons from below, and therefore

4.2 Estimation Method of Hydrocarbon Losses During Secondary …

261

Fig. 4.12 The characteristics of lateral migration pathways within the effective source area. a shows the pattern of pathways on the top of a carrier bed consisting of 8 × 8 IMUs, where the arrow shows the direction of the driving force; b graph of IMU number (N m ) versus S l

migration behavior at this stage may also be considered as 2D migration from the boundary of the source area to the trap. In this case, each migration pathway grows independently of all the others. Therefore, the development of any one pathway will not influence the movement of others unless it meets another pathway. Here, we discuss losses from lateral migration pathways outside the effective source area using two models: a sloping rectangular plate model (Fig. 4.13a) and a circular synclinal model (convex down) (Fig. 4.13b). (1) Rectangular incline model Figure 4.13a shows the rectangular plate model, where pathways grow upwards independently from the boundary of a source area at the bottom. Pathways will unite gradually in the direction of migration, but the shape and width of the existing pathways will not vary. A 2D network of N IMUs × 24 IMUs, comprising 50 × 50 bonds, was constructed to simulate lateral migration outside the source area. N varies from 2 to 40 IMUs with a step of 2 IMUs. The simulation results are shown in Fig. 4.14 which shows the relationship between the pathway saturation (Sd ) and the migration distance (L d ), where Sd = Sl /Sl' and L d is the migration distance normalized to the boundary length W. By analyzing statistically the data (presented as dots) in Fig. 4.14, the relationship between the pathway saturation Sd and the migration distance L d from the boundary can be obtained as follows: ln(Sd ) = 0.3853 ln(L d ) − 0.8093

(4.15)

where L d is the characteristic distance from the boundary of the source rock range, and Sd is the plane pathway-to-conduit ratio at the distance L d from the boundary of the source rock range.

262

4 Quantitative Evaluation Method of Hydrocarbon Migration …

a

b

Fig. 4.13 Two models to simulate lateral migration pathways outside the source area. a A sloping rectangular plate model, where the shaded line at the bottom of the network represents the boundary with the source area; b a circular synclinal model; the shaded circle represents the boundary with the source area

Fig. 4.14 Graph of lateral migration distance versus variation of( relative pathway ) saturation Sd = Scl /Sl' , for the two models shown in Fig. 4.13. Scl is the saturation of the pathway at L d , and Sl is the initial saturation of the pathway at the boundary of the source area

The results shown in Figs. 4.13a and 4.14 illustrate the migrating pathways gradually combining or “condensing” into fewer larger pathways with increasing migration distance. This happens principally in the vicinity of the source area. At L d ≈0.6, one-half of the pathways will disappear as a result of combination. At L d ≈2.0, the combination of pathways is practically complete. However, combination does not occur (Sd = 1.0) within a distance of ~ 0.125 from the boundary.

4.2 Estimation Method of Hydrocarbon Losses During Secondary …

263

As mentioned above, the initial value of the pathway saturation at the boundary for each IMU should be Sl ', as shown in Fig. 4.13. The hydrocarbon loss within the area outside the source area can therefore be estimated by: ⎧ Ld ⎪ ⎨ Q 3 = φ Sr h m S2 W ( ⎪ ⎩ Q 3 = φ Sr h m S2 W 0.125 + 0.445

) if L d ≤ 0.125W

∮L d

L −0.3853

if L d > 0.125W

(4.16)

0.125

where W is the width of the hydrocarbon supply range of source rocks, and h m is the average diameter of the migration pathways. (2) Circular syncline model Figure 4.13b shows the circular synclinal model, in which pathways grow independently upwards in all directions from the boundary of the source area at the centre marked by a shaded circle. To make the simulation easier, only one-quarter of the circular syncline was taken into account. A 2D network of 16 IMUs × 16 IMUs, with 50 × 50 bonds, was constructed to simulate lateral migration outside the source area. The simulated results are shown in Fig. 4.14 in terms of the relationship between the relative pathway saturation (Sd ) on the carrier surface outside the source area and the migration distance (L d ). As before, Sd is derived from the surface pathway saturation (Sl ) normalized by the Sl ', and L d is the migration distance normalized by the boundary length W (here, the girth of the source area). By statistically analyzing the data (presented as triangles) in Fig. 4.14, the relationship between the pathway saturation (Sd ) and the migration distance (L d ) from the boundary may obtained as follows: ln(Sd ) = 0.3021 ln(L d ) − 0.4850

(4.17)

The results shown in Figs. 4.13b and 4.14 illustrate that the migrating pathways gradually combine with increasing migration distance. However, in this model, the lateral space increase toward the migration direction and pathway combination principally occurs in the vicinity of the source area. At L d ≈0.35, one-half of the pathways will disappear as a result of combination. At L d ≈2.0, the combination of pathways is practically complete. With reference to Eq. (4.8), the hydrocarbon loss within the area outside the source area may be estimated by: ⎧ ' Ld ⎪ ⎨ Q 3 = φ · dl · Sl · W · ( '

⎪ ⎩ Q 3 = φ · dl · Sl · W · 0.1 + 0.485

∮L d 0.1

) if L d ≤ 0.1W L −0.3021

if L d > 0.1W

(4.18)

264

4 Quantitative Evaluation Method of Hydrocarbon Migration …

4.2.3 Estimation Model of Losses During Secondary Migration Luo et al. (2004) noted that when the source supply stops, oil continues to migrate causing the hydrocarbon cluster to shrink and the initially continuous pathway to disconnect into isolated segments. The residual saturation within the pathway decreases to around 30–40%. The migration loss ratio can be estimated as the product of the relative pathway volume and the hydrocarbon saturation within the pathway. By synthesizing these results, the hydrocarbon loss (Q ms ) in migration pathways within a migration-accumulation unit may be estimated by the following formula: Q ms = Sr · (Q 1 + Q 2 + Q 3 ) ) ( ' ' = Sr · φ · St · Sc · h + dl · St · Sl + dl · Sl · W · L · L v

(4.19)

where S r is the residual hydrocarbon saturation in the pathway after hydrocarbon migration; φ is the porosity of the carrier bed; St is the top view area of the carrier bed within the source rock range; Sc is the pathway-to-conduit ratio of vertical migration of hydrocarbons within the source rock range; h is the thickness of the carrier bed within the source rock range; Sl ' is the lateral migration pathway-to-conduit ratio of hydrocarbons within the source rock range, and dl is the thickness of lateral migration pathway of hydrocarbons. where, L v = L d Sd , is the ratio of the distance (L) from the source rock boundary to the observation point to the width (W ) of source rocks, and the L v value depends on the morphology of the carrier bed and hydrocarbon expulsion area. In the incline area where the carrier bed is located on the side of the hydrocarbon expulsion area of source rocks, Sd can be estimated by the following formula (Luo et al., 2007a, 2007b, 2007c): { Sd =

1 L d < 0.125 L 0.445L −0.853 d > 0.125 d

(4.20)

As a result: Lv =

⎧ ⎨ Ld ⎩ 0.125 + 0.445

∮Ld

L d < 0.125 L −0.853 dL L d > 0.125 d

(4.21)

0.125

In the syncline area where the carrier bed surrounds the hydrocarbon expulsion area of the source rock: { 1 L d < 0.1 (4.22) Sd = −0.3021 L d > 0.1 0.485L d As a result:

4.2 Estimation Method of Hydrocarbon Losses During Secondary …

Lv =

⎧ ⎨ Ld ⎩ 0.1 + 0.485

∮L d

265

L d < 0.1 (4.23)

L −0.3021 dL L d > 0.1 d

0.1

4.2.4 Case Study The migration pathway model described above was applied to assess petroleum losses in two case studies (Figs. 4.15 and 4.16). The case illustrated in Fig. 4.15 is a sloping rectangular plate (shown as the small diagram at the top-left corner), in which the lower quarter of the slope lies within the source rock with a surface area of 24 × 5 IMUs, and the other three-quarters of the slope serve as a lateral migration space. The pathways corresponding to vertical migration in the IMUs within the source area were not simulated but were calculated directly from Eq. (4.11). Figure 4.15 shows that vertically migrating oil reaches the top surface of the reservoir randomly within each IMU and then migrates laterally. 1.0 α 0.75 20 0.5 16

0.25

Lm

0

12

8

4

0

0

4

8

12

Lm

16

20

24

Fig. 4.15 Migration pathways at the top of a sloping rectangular plate carrier (shown at the top-left corner), viewed from above. The source area lies at the bottom quarter of the sloping plate. Note the combination of pathways outside the source area

266

4 Quantitative Evaluation Method of Hydrocarbon Migration … 1.0 0.75

20

0.5 16 0.25

Lm 12

0

8

4

0 0

4

8

12

Lm

16

20

24

Fig. 4.16 Migration pathways on the top of a carrier in form of a circular syncline (shown at the top-left corner), viewed from above. The source area lies at the centre of the syncline. Note the combination of pathways outside the source area

Within the effective source area, the density of pathways tends to remain constant, and outside the area, the pathways gradually combine. To show the combination of pathways, different colors are used to illustrate the frequency with which the pathway was used by the oil. The scale is demonstrated in the upper right corner in Fig. 4.15. The area of carrier beds within the source area is 5 × 24 IMUs, and that of lateral migration between the boundary of the source area and the upper margin is 15 × 24 IMUs. It is assumed that the average thickness of the reservoir is 3 m, and the average porosity of the reservoir is 20%. Based on the previous discussion, the length of the IMU is assumed to be 1 m; and Sc , the pathway saturation in the IMU, is 7%; the value of Sl ' is assumed to be 20%; and the average thickness of the lateral pathways is assumed to be 0.14 × L m = 0.14 m. From Eqs. (4.11) and (4.14), the hydrocarbon losses from pathways in the rectangular reservoir may be estimated as follows: Q 1 = 0.2 × 120 × 0.07 × 3 = 5.04 m3 Q 2 = 0.2 × 120 × 0.14 × 0.14 = 0.47 m3

4.2 Estimation Method of Hydrocarbon Losses During Secondary …

267

The distance between the boundary of the source rock area and the top margin is L d = 18/24 = 0.75. Substituting this value in Eq. (4.16), the losses outside the source area are: Q 3 = 0.2 × 0.14 × 0.14 × 24 × [0.125 + 0.445 × (0.750.3853 − 0.1250.3853 )] ≈ 0.030 · m3 Thus, Q 1 is one order of magnitude larger than Q 2 , and Q 2 is one order of magnitude larger than Q 3 Similarly, with the circular synclinal migration model (top left-hand corner of Fig. 4.16) the same evaluation may be performed. The source area of carrier beds of the reservoir is about π × 42 ≈ 50 IMUs, and the area of lateral migration between the boundary of the source area and the upper margin is about π × (82 − 42 ) ≈ 151 IMUs. We assume similarly the average thickness of the reservoir is 3 m, and the average porosity of the reservoir is 20%. Based on the previous discussion, the length of the IMU is assumed to be 1 m; and Sc , the pathway saturation in the IMU, is 7%; the value of Sl ' is assumed to be 20%; and the average thickness of the lateral pathways is taken to be 1 m. From Eqs. (4.11) and (4.14), the hydrocarbon losses from pathways in the carrier beds may be estimated as follows: Q 1 = 0.2 × 50 × 0.07 × 3 = 2.1 m3 Q 2 = 0.2 × 50 × 0.14 × 0.14 = 0.196 m3 The distance between the boundary of the source rock area and the margin of migration is L d = 8/25 = 0.32. Substituting this value in Eq. (4.18), the loss outside the source area is: Q 3 = 0.2 × 0.14 × 0.14 × 25 × [0.1 + 0.485 × (0.320.3021 − 0.10.3021)] = 0.0196 m3 Similarly, with the circular synclinal migration model (top left-hand corner of Fig. 4.16) the same evaluation may be performed. The source area of carrier beds of the reservoir is about π × 42 ≈ 50 IMUs, and the area of lateral migration between the boundary of the source area and the upper margin is about π × (82 − 42 ) ≈ 151 IMUs. We assume similarly the average thickness of the reservoir is 3 m, and the average porosity of the reservoir is 20%. Based on the previous discussion, the length of the IMU is assumed to be 1 m; and Sc , the pathway saturation in the IMU, is 7%; the value of Sl' is assumed to be 20%; and the average thickness of the lateral pathways is taken to be 1 m. From Eqs. (4.11) and (4.14), the hydrocarbon losses from pathways in the carrier beds may be estimated as follows:

268

4 Quantitative Evaluation Method of Hydrocarbon Migration …

Q 1 = 0.2 × 50 × 0.07 × 3 = 2.1 m3 Q 2 = 0.2 × 50 × 0.14 × 0.14 = 0.196 m3 The distance between the boundary of the source rock area and the margin of migration is L d = 8/25 = 0.32. Substituting this value in Eq. (4.16), the loss outside the source area is: Q 3 = 0.2 × 0.14 × 0.14 × 25 × [0.1 + 0.485 × (0.320.3021 − 0.10.3021 )] = 0.0196 m3 The results in this case show similarly that Q1 is one order of magnitude larger than Q 2 , and Q 2 is one order of magnitude larger than Q 3 . From the results of these two case studies, we conclude that the loss outside the “source area” is two orders of magnitude less than that within the source area. This means that about 90% of the losses during petroleum migration occur in vertical pathways, and 99% of the losses occur within the range of the source area. Implications which can be derived from these results include the following: (1) long range lateral migration, for example in foreland basins (Karlsen & Skeie, 2006), is quantitatively better understood; (2) the possibility of finding commercial accumulations within the source area is much larger than outside this area; (3) the further the oil/gas fields lie from the source area, the larger the quantity of accumulated petroleum in the trap should be, but the likelihood of it being discovered is smaller.

4.3 Estimation of Non-Commercial Hydrocarbon Accumulation Loss When hydrocarbons encounter a trap in the process of secondary migration in the carrier system, they will accumulate. If the trap is small and the amount of hydrocarbons is sufficient, the migrated hydrocarbons can overflow the trap and continue to migrate. For hydrocarbon exploration and development, if the scale of a single reservoir is too small and lower than the critical economic hydrocarbon reservoir scale, it has no commercial exploitation value. These small-scale accumulated hydrocarbon reserves belong to invalid accumulation in hydrocarbon resource evaluation and cannot be included in hydrocarbon resources. The estimation of the loss resulted from Non-commercial hydrocarbon accumulations is also a problem that must be faced.

4.3 Estimation of Non-Commercial Hydrocarbon Accumulation Loss

269

4.3.1 Calculation Principle and Method The amount of hydrocarbon loss corresponding to Non-commercial accumulations can be estimated by reservoir scale sequence method (Jin & Zhang, 1999; Masters, 1993) (Fig. 4.17). The basis of this method is that the sequence of hydrocarbon reservoir scales in a certain petroleum system, namely the sequence of reserves from large to small, obeys Pareto’s law: ( n )K Qm = Qn m

(4.24)

where Qm is the probability reserve of the reservoir with serial number equal to m, and Qn is the probability reserve of the reservoir with serial number equal to n; K is the change factor of reservoir reserve scale; m, n is the reservoir serial number, and is any value in the integer sequence (1, 2, 3,…), m /= n. If the largest reservoir (No. 1) is discovered with a reserve of Qmax , then we have: Q max = n K Q n Qn =

Q max nK

(4.25) (4.26)

Although the law cannot be well explained theoretically at present, the statistics of a large number of hydrocarbon-bearing areas at home and abroad show that the reservoir scale sequence generally obeys Pareto’s law (Guo et al., 2005; Hu et al., 2007; Jin & Zhang, 2002), which can be used to infer the number and reserves of undiscovered reservoirs.

Fig. 4.17 Determination of Non-commercial hydrocarbon accumulation amount based on reservoir scale sequence method

270

4 Quantitative Evaluation Method of Hydrocarbon Migration …

In estimating the amount of hydrocarbon loss by Non-commercial accumulation, firstly, according to the reservoir scale sequence discovered in the study area, the Pareto law is used to calculate the maximum reservoir reserve scale Qmax and the scale sequence Qi of all possible reservoirs (i = 1, 2, … N, representing the serial number of hydrocarbon reservoirs). At the same time, the minimum economic reservoir scale evaluation method (Li, 1991) is used to determine the minimum lower limit standard Qmin of industrial hydrocarbon reservoirs, and the following formula is used to calculate the amount of Non-commercial hydrocarbon accumulation Qls : Q ls = Q a −

L ∑ Q max ik i=1

(4.27)

where L is the serial number of the hydrocarbon reservoir corresponding to the smallest industrial hydrocarbon reservoir (Qmin ); Qa is the amount of hydrocarbons accumulated in all reservoirs, which can be calculated by statistical summation of the determined reservoir scale sequence, namely: Qa =

N ∑ Q max ik i=1

(4.28)

where N is the total number of hydrocarbon reservoirs that should be large enough in the study area. This method is suitable for a complete and independent hydrocarbon geological system (such as petroleum system, MAU, even play). The generation, migration, accumulation and subsequent geological changes of hydrocarbons in such system all occurred under the same historical conditions of petroleum geological evolution, that is, the predicted hydrocarbon fields have a unified genetic linkage, and there are at least three discovered hydrocarbon reservoirs in the evaluation unit (Zhao, 1988).

4.3.2 Examples of Non-Commercial Accumulation Amount Estimation Taking Chengbei Fault-step Zone of Qikou Sag in Bohai Bay Basin as an example, we introduce how to use reservoir scale sequence method to estimate hydrocarbon loss of Non-commercial accumulation in actual basin. The Chengbei Fault-step Zone as a whole is in a relatively complete and independent petroleum system. Up to 2005, nine oilfields such as Zhangdong, Zhangbei, Qidong, Zhaodong, Zhangdong East, Yangerzhuang, Youyi, Liuguanzhuang, and Chenghai have been discovered. Six hydrocarbon-bearing structures, such as wells ZH8, ZH4 × 1, ZH2 × 1, ZH1 × 1, ZH5, and ZH69, have been proved, which meet

4.3 Estimation of Non-Commercial Hydrocarbon Accumulation Loss

271

Table 4.2 Geological reserves data and predicted reservoir sequence in Chengbei fault-step zone on the South slope of Qikou sag Serial number

Oilfield

Oil bearing horizon

Geological reserves (× 104 t)

Predicted reservoir scale serial number

1

Zhangdong oilfield

Es1−3

4645.9

2

2

Zhaodong oilfield

Nm

4376.27

3

3

Zhangdong east oilfield

Es2+3 , J

2293.35

4

4

Qidong oilfield

Nm, Es1+2

2249

5

5

Yangerzhuang oilfield

Nm, Ng, Es1 , Ed

1997.68

6

6

ZH8

Nmbottom + Ng

1555.24

8

7

Chenghai oilfield

Mz

1142

12

8

ZH4 × 1

Es1

819

17

9

Youyi oilfield

Es1−3

332.46

46

10

Z69

Ng

263

59

11

Liuguanzhuang oilfield

Nm, Ng

66

81

12

Zhangbei oilfield

Ed

198

166

13

ZH5

Ed

104

275

14

ZH1 × 1

Es1

29

686

15

ZH2 × 1

Es2+3

23

888

the applicable conditions of the above-mentioned oilfield scale sequence method. Table 4.2 lists the reserves data of discovered hydrocarbon fields in the study area. 1. Calculation of K Value Reservoir scale change factor K is the key parameter of the reservoir scale sequence method. According to the statistics of main hydrocarbon-bearing areas in China and abroad, the variation range of K value is between 0.5 and 2, which can be determined by actual geological conditions and exploration and development results, or by reference in exploration areas with similar geological conditions. However, due to the lack of relevant data in the study area and its adjacent areas, we calculate the K value according to Pareto’s law (Zhao, 1988). The study area is divided into 9 domains, and the Eq. (4.27) is used to calculate the reservoir scale sequence, and according to the standard deviation σ between the actual reserves and the predicted reserves of the discovered oilfields in each prediction sequence: √ σ =

)2 1 ∑l ( Q i − Q ji i=1 l

( j = 1, 2, . . . , s)

(4.29)

272

4 Quantitative Evaluation Method of Hydrocarbon Migration …

where Qi is the reserves of the discovered oilfield in the study area; Qji is the predicted reserves of the number i oilfield discovered in the number j prediction sequence. Using the reserves data listed in Table 4.2 for calculation, when K is 0.9004, the standard deviation σ is the smallest, equal to 0.00078, so K = 0.9004 is selected to predict the oilfield scale sequence in the study area. 2. Determination of Reservoir Scale Sequence Prediction Model At present, 15 oilfields and hydrocarbon-bearing structures have been discovered in the study area, which are arranged from large to small according to the reserve scale Qi (i = 1, 2, 3 …) (Table 4.2). Among them, the largest Zhangdong oilfield has a reserve scale of 4645 × 104 t, and the smallest Zhuanghai 2 × 1 structure has a reserve of 21 × 104 t. Taking the reserves of Zhangdong oilfield with the largest reserves as the calculation point, the sequence Ai is obtained by the following formula: √ Ai =

K

Qi Q1

(4.30)

The sequence Ai is multiplied by the positive integer n (n = 1, 2, …), and when the value of Ai n is close to the positive integer 1, 2, 3, …, they are counted in the following matrix: | | A11 n A12 n · · · | | A21 n A22 n · · · | | ··· ··· | | A n A n··· m1 m2

| | | A1L n || || 1 || A2L n || || 2 || ≈ · · · || || 3 || Aml n | | m |

Calculate the standard deviation σ of each row of the matrix: √ 1 ∑l σ = (Ai n − An )2 i=1 l where An =

1 l

l ∑

(4.31)

(4.32)

Ai n.

i=1

By calculation, the standard deviation of the second row of the matrix is less than 0.05. Because Ai n is close to positive integer m, it conforms to Pareto’s law within a given error range, so the second row can be used as the oilfield scale prediction sequence model in the study area. 3. Calculation of Maximum Oilfield Reserves Qmax and Prediction of Reservoir Scale Sequence The maximum oilfield reserve scale is also the key parameter that affects the prediction result of reservoir scale. Dividing each value of the previously determined prediction model sequence Ai n by Ai , we can obtain the serial number n of the discovered hydrocarbon reservoir in the prediction hydrocarbon scale sequence (Table 4.2).

4.3 Estimation of Non-Commercial Hydrocarbon Accumulation Loss

273

Multiplying the corresponding hydrocarbon reservoir reserves Qi (i = 1, 2, …, L) in Table 4.2 by the K power of the prediction sequence n, we can obtain the maximum hydrocarbon reservoir reserves (No.1 Oilfield) Qmax in the study area. Take the average value of the predicted maximum hydrocarbon reservoir reserves of all domains as the maximum hydrocarbon reservoir reserves in this area: 1∑ = Qi · n K l i=1 l

Q max

(4.33)

According to Eq. (4.33), the maximum reservoir reserves Qmax in Chengbei Faultstep Zone is calculated to be 10,346 × 104 t. According to Eq. (4.29), the predicted maximum reservoir reserves Qmax are divided by 1 K , 2 K , … respectively to obtain the predicted reservoir scale Q·n, thus establishing the reservoir scale prediction sequence of the study area (Fig. 4.18). 4. Calculation Results of Non-commercial Hydrocarbon Losses After determining the sequence of oilfield scale (Fig. 4.18), economic evaluation is carried out according to the oilfield development cost of the study area at that time, and the minimum economic reservoir reserves Qmin = 10 × 104 t is taken. Using Formula (4.29), the Non-commercial hydrocarbon accumulation in the study area is calculated to be 20.98 × 108 t, of which the Non-commercial hydrocarbon accumulation in the western hydrocarbon migration and accumulation unit is 0.02 × 108 t, the central migration and accumulation unit is 17.31 × 108 t, and the eastern migration and accumulation unit is 3.65 × 108 t.

Fig. 4.18 Sequence distribution of predicted oilfield scale in Chengbei Fault-step Zone

274

4 Quantitative Evaluation Method of Hydrocarbon Migration …

4.4 Quantitative Evaluation of Hydrocarbon Resources and Spatial Distribution The evaluation of hydrocarbon resources potential is an important basis for making hydrocarbon exploration decisions, and the details and reliability of the information provided by the evaluation greatly affect the effectiveness of hydrocarbon exploration (Xu et al., 2001). With the improvement of hydrocarbon exploration degree, the current hydrocarbon exploration objects tend to be complicated and refined. To minimize exploration risks, quantitative prediction information of hydrocarbon resources distribution is urgently needed. Accurate prediction of hydrocarbon resources and the spatial distribution to provide scientific basis for exploration decision-making has become an important task in the study of hydrocarbon accumulation dynamics. In this section, based on the estimation method of hydrocarbon loss in the process of hydrocarbon migration and accumulation, we present a material balance model suitable for resource evaluation of multi-source and multi-stage complex petroleum systems. Moreover, taking the dominant migration pathway obtained by simulation as the evaluation clue, the migration and accumulation amount of hydrocarbons in the migration pathway and the trap is evaluated, and the hydrocarbon migration and accumulation efficiency and the spatial position of hydrocarbon resources to be discovered are intuitively and quantitatively determined. A resource distribution prediction method based on quantitative evaluation of hydrocarbon carrying efficiency is preliminarily formed.

4.4.1 Material Balance Method for Hydrocarbon Resources Evaluation At present, there are dozens of hydrocarbon resources evaluation methods proposed at home and abroad. According to the different theoretical basis and application objects, these methods are mainly classified into four categories: genesis method, geological analogy method, statistical prediction method and comprehensive prediction method (Jin, 1995; Lee & Wang, 1983; Zhao et al., 2005). Superimposed basins in China are characterized by multiple sets of source rocks, multiple types of reservoir-caprock assemblages, multiple stages of hydrocarbon generation and expulsion, and multiple stages of reservoir formation. Usually, the exploration target formations are buried deep, undergo long-term evolution. The history of hydrocarbon reservoir charging is complex, and the hydrocarbon distribution rule is controlled by many factors. The existing resource evaluation methods and related migration and accumulation coefficients are generally not applicable (Pang et al., 2002). For the resource evaluation of such complex petroleum systems, it is necessary to consider the whole process of hydrocarbon generation, migration, accumulation and preservation. On the premise of accurately obtaining the amount of hydrocarbon supply and loss in different stages,

4.4 Quantitative Evaluation of Hydrocarbon Resources and Spatial …

275

it is a feasible method to use the material balance model to estimate the amount of hydrocarbon resources. According to the principle of mass conservation, hydrocarbons are lost in various ways in the process from hydrocarbon source to reservoir after generation. More importantly, the migration—accumulation processes keep a state of dynamic equilibrium of continuous loss and accumulation (Pang et al., 2002): Hydrocarbons generated by source rocks can enter the carrier bed through primary migration and undergo secondary migration only after satisfying the residual hydrocarbon amount of source rocks; during the secondary migration, some hydrocarbons are lost in the migration conduits or escape directly to the surface, and the remaining hydrocarbon amount is the available hydrocarbon accumulation amount in a certain migration—accumulation unit. Therefore, the commercial resources amount is the remain of the amount of hydrocarbons available for accumulation minuse the amount of accumulation without commercial value and the amount of destruction and loss. Therefore, in practice, as long as the hydrocarbon loss in each process can be accurately estimated, the final resource amount in the hydrocarbon migration-accumulation unit can be obtained. For the evaluation method of hydrocarbon resources based on the principle of material balance, the selection of evaluation unit is very important (Pang et al., 2002; Zhao et al., 2005). According to the division method of migration-accumulation unit with temporal and spatial limitation in the dynamical study of reservoir-forming, hydrocarbons in each migration-accumulation unit start from the source, migrate in the carrier bed, and accumulate in traps to form hydrocarbon reservoirs. If the formed hydrocarbon reservoir is destroyed, two results may occur: First, there is no caprock above the destroyed hydrocarbon reservoir that can prevent hydrocarbon loss, and the lost hydrocarbons migrate to the surface and are completely lost. Second, the hydrocarbons lost from the hydrocarbon reservoir is the source of another migrationaccumulation unit, and migrates and accumulates along the carrier system to form new hydrocarbon reservoirs. Obviously, this reservoir-forming system with temporal and spatial limitation is the most convenient unit for estimating and evaluating resources. Conventional hydrocarbon resources are the hydrocarbons generated by source rocks minus the loss in the process of hydrocarbon expulsion, secondary migration, non-commercial scale accumulation, etc., which finally accumulate in commercial scale hydrocarbon reservoirs. Therefore, the resource amount of a certain hydrocarbon migration-accumulation unit can be calculated by the following formula. Q z = Q e − Q ms − Q f

(4.34)

where Qz is the resources amount of the migration-accumulation unit; Qk is hydrocarbon supply from the source; Qy is the amount lost during the secondary migration; Qf is the non-commercial accumulations of hydrocarbons. Then, how to evaluate hydrocarbon resources of petroleum systems with different reservoir-forming times and different reservoir-forming ranges in specific practical research areas?

276

4 Quantitative Evaluation Method of Hydrocarbon Migration …

In fact, in any sedimentary basin, the process of hydrocarbon generation, migration, accumulation, reservoir forming and preservation is only a part of the basin evolution process, and its spatial range is limited. The range of petroleum system is exactly the range delineated by a set of source rocks and related hydrocarbons, having been and/or will potentially be discovered. That means, all hydrocarbons, including primary sources and secondary ones, within the system. Therefore, the petroleum system can be used as a higher-level hydrocarbon resource evaluation unit (Zhao et al., 2005) to unify different migration-accumulation units within its scope. In a petroleum system, different migration-accumulation units may be partially or fully superimposed on each other in space and time (Fig. 1.5). Therefore, the evaluation of hydrocarbon resources at the petroleum system level is not a simple sum of the resources in each migration-accumulation unit, but an analysis based on actual geological conditions to ensure that the hydrocarbon losses of the common parts are not calculated repeatedly when all migration-accumulation units are superimposed. For example, the source rocks in petroleum system may have experienced many hydrocarbon expulsion processes, and the temporal and spatial range and characteristics of each corresponding migration-accumulation unit are different. However, from the perspective of mass balance, the amount of hydrocarbons generated by source rocks can be expelled after meeting the amount of hydrocarbons required for adsorption and retention (Pang et al., 2005), so the occurrence of the first hydrocarbon expulsion has already met this requirement, and the hydrocarbons generated in the later stage basically do not need to be adsorbed and retained again when being expelled, unless the hydrocarbon expulsion mechanism has undergone fundamental changes. Therefore, the amount of residual hydrocarbons in source rocks can be calculated as a “general amount” and estimated according to the current organic matter characteristics (Pang et al., 2002). Similarly, the amount of residual hydrocarbons on the hydrocarbon migration pathway cannot be accumulated, because hydrocarbons tend to migrate along the pathway with the least resistance, so hydrocarbons migrating many times often reuse the existing pathways in the conduits (Luo et al., 2007a, 2007b, 2007c, 2008). The estimation of hydrocarbon loss during migration should be based on the estimation of each migration-accumulation unit, and those with repeated pathways should only be selected once. In some basins, the hydrocarbon migration-accumulation units in different stages may be inherited, i.e. the ranges of migration-accumulation units in all reservoir forming stages are almost the same, and the source rocks, carrier systems and traps are shared as one set, so the estimation of resources in this petroleum system is relatively simple (Lei et al., 2010). Therefore, when evaluating the hydrocarbon resources of a petroleum system, it is necessary to quantitatively estimate: (1) The amount of hydrocarbons expelled from source rocks and the amount of residual hydrocarbons in paleo-reservoirs during previous reservoir-forming processes (destruction of supply sources in paleoreservoir); (2) the amount of residual hydrocarbons in the migration pathways within the carrier system; (3) the amount of hydrocarbons accumulated in non-commercial reservoirs; (4) for some petroleum system systems, the amount of hydrocarbons destructed and escaped in a certain stage (Pang et al., 2005).

4.4 Quantitative Evaluation of Hydrocarbon Resources and Spatial …

277

4.4.2 Realization of Hydrocarbon Migration and Accumulation Evaluation in Migration Pathway Through the elaboration of the previous chapters, it can be found that the method of hydrocarbon reservoir-forming dynamics research is very beneficial to the prediction of the spatial distribution of hydrocarbon reservoirs to be discovered and the analysis of hydrocarbon exploration risks, because the method can quantitatively describe the characteristics of migration conduits and vividly display the pathways of hydrocarbon migration (Fig. 4.19a). Since conventional hydrocarbon reservoirs must be distributed near the migration pathway, when MigMOD numerical model method is used to simulate the migration pathway of hydrocarbons, it is assumed that the Non-commercial accumulation amount is randomly distributed on the pathway, and at the same time, the identification term of hydrocarbon supply amount is further added to the numerical model to obtain the migration amount of hydrocarbons in the migration pathway (Fig. 4.19b). The specific realization method is as follows: when simulating the formation of the migration pathway, starting from any hydrocarbon supply point in the effective source range, each step of hydrocarbon migration should not only deduct the loss amount on the migration pathway within the step range, but also deduct the Non-commercial accumulation amount related to the range, and the remaining hydrocarbon amount is called the migratable amount; if the migratable amount is positive, the hydrocarbons will continue to migrate and the pathway will increase; if the migration amount is zero or negative, the migration stops. When the hydrocarbons migrating from other hydrocarbon supply points migrate along the formed pathway, the hydrocarbons are no longer lost, and the migration flux in this pathway increased. When hydrocarbons continue to migrate and form a new pathway, the hydrocarbon loss and corresponding Non-commercial accumulation in the new pathway should be deducted. By analogy, the hydrocarbon migration pathway is continuously formed, and the hydrocarbon loss and corresponding Non-commercial accumulation on the new pathway are deducted. Hydrocarbons from different hydrocarbon supply points may pass through the same pathway due to the combination of migration pathways, which increases the migration flux in this pathway, but the hydrocarbon loss in the pathways and the corresponding Non-commercial hydrocarbon accumulations are no longer deducted. The amount of hydrocarbons that can migrate along the pathways is the commercial accumulation amount. In the simulation results considering the migration and accumulation amount of hydrocarbons, we express the migration flux in different colors, and the migration amount distribution in the migration pathways is clear at a glance (Fig. 4.19). According to the migration pathways and flux distributions in Fig. 4.19b, the distribution positions of hydrocarbon resources and the main areas of hydrocarbon accumulations can be directly identified: The denser the pathways, the greater the possibility of commercial hydrocarbon accumulations; the higher the flux, the larger the scale of hydrocarbon reservoirs formed near the pathways. The amount of hydrocarbon

278

4 Quantitative Evaluation Method of Hydrocarbon Migration …

Fig. 4.19 Simulation results of hydrocarbon migration pathways while the hydrocarbon loss is taken into account in the conceptual geological model. a Simulation results corresponding to a Bond number of 1.0 × 10−3 with a homogeneous carrier medium with an ellipse as the source area; b the total hydrocarbon losses are taken into account by randomly distributing on all migration pathways; c structural configuration of the carrier bed shown in a and b, adjoining syncline (lower left) and anticline (upper right) constructed using z = x · exp(−x 2 − y2 ). The color bar scale in a and b is the relative flux of migrated hydrocarbon in pathways

resources accumulated in different directions or even in different locations can be quantitatively presented.

4.4 Quantitative Evaluation of Hydrocarbon Resources and Spatial …

279

4.4.3 Method Process of Hydrocarbon Migration and Accumulation Eff Efficiency and Resource Distribution Evaluation The prediction method of hydrocarbon resource distribution based on quantitative evaluation of carrier system constitutes the final link in the study of hydrocarbon reservoir forming dynamics, which not only highlights the systematicness and advancement of the research method of hydrocarbon accumulation dynamics, but also reflects the practical guidance of the research method of hydrocarbon reservoir forming dynamics in actual hydrocarbon exploration. Therefore, under the guidance of quantitative dynamic ideas and methods, we have initially formed a series of methods and technologies suitable for the evaluation of conventional hydrocarbon migration and accumulation efficiency and resource distribution in complex basins by combining the theoretical methods of hydrocarbon reservoir forming dynamics with practical regional applications. In the research of actual areas, four key technologies are implemented, such as evolution and recovery of hydrocarbon migration dynamic field, quantitative description of composite carrier system, evaluation of loss during migration and accumulation, and migration and accumulation simulation of source-potential-conduit coupling, is mainly carried out (Fig. 4.20):

Fig. 4.20 Process of hydrocarbon migration and accumulation efficiency and resource distribution evaluation technologies

280

4 Quantitative Evaluation Method of Hydrocarbon Migration …

(1) Starting from establishing the fine geological model of the basin, by using the fluid dynamic field recovery technology based on basin modelling, we reconstruct the ground temperature and fluid pressure field during the basin burial process, thus obtaining the hydrocarbon expulsion history of each set of source rocks and the paleo-fluid potential of the main carrier bed, and determining the hydrocarbon supply range and migration-accumulation unit in the key reservoir-forming stage. (2) Using the modeling method of quantitative description of the composite carrier system, we quantitatively evaluate the geometric and fluid connectivity of sandstone carrier bed (including unconformity surface) and the opening and sealing of fault carriers, establish the hydrocarbon composite conduit framework and adopt appropriate geological parameters for characterizing uniformly and quantically the carrying capacity. (3) Taking the hydrocarbon migration and accumulation unit as the basic element, we carry out quantitative evaluation of the hydrocarbon migration and accumulation loss amount, estimate the hydrocarbon loss amount, non-industrial accumulation amount and structural destruction escape amount during the secondary migration, and obtain the hydrocarbon amount available for commercial accumulations in the migration and accumulation unit. (4) Based on the implementation of the above three supporting work, the migration and accumulation MigMOD simulation technology of source-potential-conduit coupling is used to quantitatively analyze the spatial distribution of dominant migration pathways and the amount of hydrocarbons available for accumulation on different pathways, and finally provide a scientific basis for exploration target evaluation and exploration decision-making.

4.5 Applicability Test of Research Methods At present, this set of research technical methods has achieved good practical application in many continental hydrocarbon-bearing basins in the east and west of China (Lei et al., 2010; Luo et al., 2007b; Zhang, 2007; Zhao et al., 2011). Taking the Paleogene in Chengbei Fault-step Zone of Qikou Sag in Bohai Bay Basin as an example, the actual working methods and application effects are introduced below. Firstly, based on the tectonic map, sedimentary facies map, paleo-geotemperature map, denudation thickness map and source rock characteristic correlation map (thickness, organic matter type, organic carbon content, etc.) of each stratum in the study area, after modelling and digitization, all data are converted into a three-dimensional data set acceptable for further numerical treatments. The burial history, thermal evolution history and fluid pressure evolution history are recovered by using Temis3D basin simulation software (Fig. 4.21). On this basis, the fluid potential field of different carrying intervals during key hydrocarbon reservoir-forming stages and the hydrocarbon expulsion history of main source rock intervals are simulated and calculated. Figure 4.22 shows the fluid potential characteristics of the top interface of the Third

4.5 Applicability Test of Research Methods

281

Member of Shahejie Formation in the study area at 2.0 Ma (corresponding to the end of Minghuazhen Formation deposition period). The fluid potential is generally characterized by high in the north and low in the south. Qikou Sag is taken as the hydrocarbon supply area. According to the distribution of the fluid potential field, the study area can be divided into three hydrocarbon migration and accumulation units in the east, middle and west, with the separation troughs as the boundaries. The hydrocarbon expulsion amount of source rocks in different geological periods is obtained by hydrocarbon generation potential method (Zhou & Pang, 2002). Figure 4.23 shows the spatial distribution of hydrocarbon expulsion intensity of source rocks in the Third Member of Shahejie Formation in the key reservoir-forming stage (2 Ma). Based on the study of hydrocarbon migration and accumulation dynamic conditions in the key reservoir-forming stage, according to the quantitative research method of carrier system described in Chap. 3, the quantitative analysis of carrying characteristics of sandstone carrier beds, faults and unconformity carrier beds is further carried out (Luo et al., 2007b; Zhang, 2007), in which unconformity carrier beds

a. Three-dimensional presentation of burial evolution history

b. Planar distribution of Ro evolution

c. Fluid pressure evolution section Fig. 4.21 Simulation results of paleotectonics, ground temperature and fluid pressure at 2 Ma in Chengbei fault-step zone

282

4 Quantitative Evaluation Method of Hydrocarbon Migration …

Fig. 4.22 Distribution of fluid potential and division of migration and accumulation units of the top interface of the third member of Shahejie formation in Chengbei fault terrace belt at 2.0 Ma

are treated as conglomerate or water transgression sandstone carrier beds overlying the unconformity surface. Based on the analysis of the carrying characteristics of different types of carriers, combined with the geological anatomy and geochemical tracing of the discovered hydrocarbon reservoirs, the optimal combination mode of various carriers is determined. The sandstone carrier beds and fault carriers are crossconfigured in time and space. The permeability values corresponding to throat radius in sandstone carrier bed and to fractures in fault carrier are used to uniformly quantitatively characterize the carrying capabilities of various carriers, and 10 conduit framework models of main reservoir forming stages are constructed to realize a complete description of the composite carrier system in the study area (Zhang et al., 2007). In the fourth section of the third chapter, we show the composite conduit framework model of the study area, which consists of sandstone carrier beds of the Third Member of Shahejie Formation (Es3 ), the Second Member of Shahejie Formation (Es2 ) and the First Member of Shahejie Formation (Es1 ) connected with the main controlling faults (Fig. 3.47).

4.5 Applicability Test of Research Methods

283

Fig. 4.23 Hydrocarbon expulsion intensity of source rocks of the third member of Shahejie formation in Chengbei fault-step zone at 2 Ma

Finally, using MigMOD hydrocarbon migration and accumulation simulation software (Luo, 2011; Luo et al., 2007a), the hydrocarbon source, migration driving force field and carrier system are dynamically coupled in the migration—accumulation units limited temporally and spatially, and the dynamic process of hydrocarbon migration and accumulation is quantitatively analyzed (Fig. 4.24). In the process of migration and accumulation simulation, the hydrocarbon loss estimation methods introduced in the second and third sections of this chapter are used to calculate the residual hydrocarbon amount in the migration pathway and the non-industrial accumulation amount near the migration pathway (Luo et al., 2007b). The calculation results of hydrocarbon expulsion amount, loss amount and resource amount during migration and accumulation in each migration and accumulation unit are listed in Table 4.3. Figure 4.24 shows the simulation results of hydrocarbon migration and accumulation on the carrier framework model shown in Fig. 3.47. The color bar at the lower side of the figure indicates the flux of hydrocarbons available for accumulation

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Fig. 4.24 Hydrocarbon resources of the Es3 –Es2 –Es1 migration-accumulation unit in Chengbei fault-step zone at 2 Ma Table 4.3 Evaluation results of hydrocarbon expulsion amount, migration and accumulation loss amount and resource amount in Chengbei fault-step zone Loss in Non-commercial Hc Migration—accumulation Total Hc expulsion (× migration accumulation (× 108 unit 108 t) pathways (× t) 108 t)

Total resources (× 108 t)

Western unit

0.40

0.36

0.02

0.02

Central unit

34.67

9.20

17.31

8.16

Eastern unit

7.11

1.75

3.65

1.71

4.5 Applicability Test of Research Methods

285

flowing through the migration pathways. The black-red-yellow color scale represents the gradual increase of migration flux. The maximum migration amount that can be accumulated in the migration pathway of Shahejie Formation in Chengbei Fault-step Zone is 2 × 108 t. According to this evaluation result, the distribution of hydrocarbon resources in different migration pathways can be directly determined, and the quantitative evaluation of hydrocarbon migration and accumulation efficiency and resource distribution can be truly realized. As shown in Fig. 4.24, the hydrocarbons expelled from the source rocks in Qikou Sag at the end of Minghuazhen formation deposition period (2 Ma) are controlled by both migration driving force and carrier system, and the hydrocarbons migrate and accumulate to the study area along multiple dominant migration pathways formed in the connected sand bodies of the Third Member of Shahejie Formation. Hydrocarbons migrate to the fault of Zhangdong-H4 Well and then enter the carrier bed of the Second Member of Shahejie Formation lying on rising wall, and converge with the hydrocarbons expelled from Qinan sag in Zhangdong area, where the migration and accumulation amount is nearly 2 × 108 t. The overflowed hydrocarbons along the slope zone between Zhangdong tectonics and Zhangdong East tectonics form a dominant migration pathways to Zhaodong and Zhangdong East tectonics, and can also form a scale of hydrocarbon resources of 2 × 108 t. Hydrocarbons migrate along the dominant pathways of the Second Member of Shahejie Formation to the Zhangdong East tectonics lying on the hanging wall of the Yangerzhuang fault, and then can enter the First Member of Shahejie Formation in the uplift area. At this point, because there is no carriers in the upward dip direction, hydrocarbons begin to accumulate, and the dominant migration pathways are not obvious. ZH8 Well area and Chenghai area are all covered by the migration range, and the migration volume of these two areas is 1 × 108 –2 × 108 t. The analysis of the migration and accumulation simulation results of all conduit framework models in the main reservoir-forming stage of Chengbei Fault-step Zone shows the following findings (Zhang, 2007): The high value area of migration and accumulation with dense migration pathways is basically the location of large tectonic traps, mainly including Zhangdong-Zhangbei, Zhangdong East tectonics, Guanjiapu area, Chenghai area, Youyi area, Liuguanzhuang area, Yangerzhuang area and the southern area of H9 Well, etc.; at the same time, industrial hydrocarbon accumulation can also occur in lithologic traps within the effective source rocks such as Zhangdong North and Qidong area. After the completion of this study, since 2006, Dagang Oilfield has been successively drilled in potential hydrocarbon accumulation areas such as the eastern wing of Zhangdong, the tectonic zone of Zhangdong North, the F-1 fault nose tectonic zone, and the Chenghai slope area, achieving a series of major hydrocarbon exploration breakthroughs. A total of nearly 300 million tons of proven reserves have been reported. The evaluation results have been verified by the later actual drilling results.

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Ungerer, P. J., Burrus, B., Doligez, Y., & Chene, B. F. (1990). Basin evaluation by integrated twodimensional modeling of heat transfer, fluid flow, hydrocarbon generation, and migration. AAPG Bulletin, 74, 309–335. Wei, Z. F., Zou, Y. R., Cai, Y. L., Wang, L., Luo, X. R., & Peng, P. A. (2012). Kinetics of oil group-type generation and expulsion: An integrated application to Dongying depression, Bohai Bay basin, China. Organic Geochemistry, 52, 1–12. Xiao, L. H., & Gao, D. L. (1998). A new calculation method for hydrocarbon generation and discharge amount in geochemical logging. Petroleum Geology and Experiment, 20(1), 98–102. Xie, X. N., Li, S. T., Dong, W. L., & Hu, Z. L. (2001). Evidence for episodic expulsion of hot fluids along faults near diapiric structures of the Yinggehai basin, South China Sea. Marine and Petroleum Geology, 18, 715–728. Xu, C. H., Xu, Y. D., & Qiu, L. G. (2001). Petroleum resource assessment: The present state and development trend. Offshore Oil, 4, 1–6. Xue, H. T., Tian, S. S., Lu, S. F., Zhang, W. H., & Du, T. T. (2015). Selection and verification of key parameters in the quantitative evaluation of shale oil: A case study at the Qingshankou Formation, Northern Songliao Basin. Bulletin of Mineralogy, Petrology and Geochemistry, 34(1), 70–78. Yan, J. Z., Luo, X. R., Wang, W. M., Chen, F., Toussaint, R., Schmittbuhl, J., Vasseur, G., & Zhang, L. K. (2012). Testing oil saturation distribution in migration paths using MRI. Journal of Petroleum Science and Engineering, 86–87, 237–245. Yan, Y. H., Zou, Y. R., Qv, Z. Y., Cai, Y. L., Wei, Z. F., & Peng, P. A. (2015). Experimental study on hydrocarbon retention-expulsion of Es 4 formation source rock in the Dongying depression. Geochimica (beijing), 44(1), 79–86. Zhang, L. K. (2007). Dynamic research of hydrocarbon migration and accumulation in Chengbei step-fault zone. Doctor thesis of Institute of Geology and Geophysics. Chinese Academy of Sciences. Zhang, F. Q., Miao, S., Wang, W. M., Zhou, B., & Luo, X. R. (2003). Experiments on oil migrating in a limited pathway and the mechanism analysis. Petroleum Geology and Experiment, 25(1), 69–75. Zhang, L. K., Luo, X. R., Liao, Q. J., Yuan, S. Q., Su, J. Q., Xiao, D. Q., Wang, Z. M., & Yv, C. H. (2007). Quantitative evaluation of fault sealing property with fault connectivity probabilistic method. Oil and Gas Geology, 28(2), 181–190. Zhang, W. Z., Yang, H., Li, J. F., & Ma, J. (2006). Leading effect of high-class source rock of Chang 7 in Ordos Basin on enrichment of low permeability oil-gas accumulation-Hydrocarbon generation and expulsion mechanism. Petroleum Exploration and Development, 33(3), 289–293. Zhao, J., Luo, X. R., Zhang, B. S., Zhao, F. Y., & Lei, Y. H. (2011). A quantitative characterization and effectiveness evaluation on sandstone carrier beds of the Silurian Kalpintag formation in the Tazhong area. Tarim Basin. Acta Petrolei Sinica, 32(6), 949–958. Zhao, W. Z., Hu, W. Y., & Shen, C. X. (2005). General philosophy, method system and key technology of conventional and unconventional oil & gas resource assessment. Acta Petrolei Sinica, 26, 12–17. Zhao, X. D. (1988). Quantitative evaluation of petroleum resources (p. 323). Geological press. Zhong, N. N., Lu, S. F., Huang, Z. L., Zhang, Y. S., Xue, H. T., & Pan, C. C. (2004). The evolution of TOC value for source rock and its controlling factor. Science in China (Series D: Earth Sciences), 34, 120–126. Zhou, J., & Pang, X. Q. (2002). A method for calculating the quantity of hydrocarbon generation and expulsion. Petroleum Exploration and Development, 29(1), 24–27. Zhou, J., Pang, X. Q., & Li, N. (2006). Characteristics of hydrocarbon expulsion for the lower tertiary resource rocks in the Jiyang depression, the Bohaiwan basin. Petroleum Geology and Experiment, 29(1), 59–64. Zhu, H. Q., Liu, P. L., Pang, W. Q., & Jiang, Z. X. (2008). Principle of hydrocarbon-generation potential method studying hydrocarbon expulsion characteristics of source rocks and its application. China Petroleum Exploration, 3, 5–9.

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Zhu, R. F., Zhang, L. Y., Li, J. Y., Liu, Q., & Li, Z. (2015). Quantitative evaluation of residual liquid hydrocarbons in shale. Acta Petrolei Sinica, 36(1), 13–18. Zou, Y. R., Yan, Y. H., Guo, J. H., & Cai, Y. L. (2012). Advance in the pyrolysis experiments and kinetic modeling of oil and gas generation. Journal of Heilongjiang Institute of Science and Technology, 22(4), 343–347.

Chapter 5

Study of the Hydrocarbon Migration and Accumulation Dynamics in the Eastern Part of the Southern Slope of the Dongying Sag

The main purpose of quantitative dynamics study of hydrocarbon migration and accumulation is to reveal the process of hydrocarbon migration and accumulation in the actual basin, quantitatively predict the pathways of migration and accumulation and estimate the amount of oil and gas resources, and thus provide scientific guidance for the selection of the favorable targets of oil and gas exploration. The previous chapters focused on the theory and related methods of dynamics study of hydrocarbon migration and accumulation. However, the applicability and validity of these methods need to be verified by oil and gas exploration results. In this chapter, the eastern part of the southern slope of the Dongying Sag in the Bohai Bay Basin is taken as the study area after systematical and comprehensive analysis of previous research results on basin evolution and oil and gas geology, the method of quantitative dynamics study of hydrocarbon migration and accumulation was adopted to analyze the characteristics of petroleum geological elements of hydrocarbon formation at the key reservoir forming periods, divide the hydrocarbon migration and accumulation units, model and characterize the composite carrier system, simulate the hydrocarbon migration and accumulation, estimate the amount of hydrocarbon loss and hydrocarbon resources, and predict the favorable exploration targets. The results of simulation of the hydrocarbon migration and accumulation and the prediction of favorable exploration targets were verified by the discoveries of reservoir exploration results.

5.1 Characteristics of Geological Elements of Reservoir Formation The study area is tectonically located in the southern gentle slope zone of the Dongying Sag in Bohai Bay Basin, which is bordered by the Chunhua tectonic zone to the west, Bamianhe fault-nose tectonic zone and Guangli South Sag to the east, Niuzhuang Subsag to the north and Guangrao Uplift to the south (Fig. 5.1). The © Science Press and Springer Nature Singapore Pte Ltd. 2023 X. Luo et al., Dynamics of Hydrocarbon Migration, https://doi.org/10.1007/978-981-19-5534-1_5

293

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5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

Fig. 5.1 Structural map of Dongying sag and location of study area (modified after Li et al., 2000)

exploration area is approximately 1100 km2 . A large number of studies had been carried out on the structure and evolution of basin, source rock geochemistry and other basic geology in the study area (Fu, 1995; Shuai & Wang, 1993; Wang & Qian, 1992; Wang, 2005, etc.). In order to quantitatively study the hydrocarbon migration and accumulation, this section briefly summarizes the previous research results and understanding on the basin structure, sedimentation, geochemistry and petroleum geological elements of reservoir formation.

5.1.1 Tectonic Characteristics The Cenozoic rift basin in Bohai Bay was developed on the basis of the Late JurassicEarly Cretaceous Mesozoic rift basin (Ren et al., 2009). The Dongying Sag is a typical Cenozoic half-graben rift lake basin in the Bohai Bay Basin, showing the tectonic characteristics of steep northern and gentle southern parts (Fig. 5.2). The southern slope zone is controlled by the structure of basement and fault system, and the Tertiary system descends northward step by step, showing a fault-step tectonic pattern in the gentle slope context. The study area can be divided into five tectonic units: Niuzhuang Subsag and Guangli Subsag, which are two secondary sags; Chenguanzhuang-Wangjiagang

5.1 Characteristics of Geological Elements of Reservoir Formation

295

Fig. 5.2 A geological cross-section showing the structural units across the northern Dongying Sag (After Li et al., 2000). The location of the section is shown in Fig. 5.1

fault-nose tectonic zone and Bamianhe fault-nose tectonic zone, which are two positive tectonic zones; and the overlap-denudation gentle slope zone on the northern slope of Guangrao. Generally, the fault system in the study area can be divided into two types (Fig. 5.2). The first type is the normal fault cut into the basement with earlier activity time, which cuts through the Mesozoic, Paleozoic and Archean crystalline basement, the fourth member of the Shahejie Formation (Es4 ) and the third member of the Shahejie Formation (Es3 ). The faults were opposite to that of the stratum, and the main active time of the faults was from Jurassic to Shahejie Formation sedimentation The trend of the faults were generally opposite to that of the stratum, and the main activity time of the faults were from the Jurassic to Shahejie period (Li et al., 2000). The activity time the second type was from Paleogene to early Neogene, which cut through the fourth member of the Shahejie Formation and the overlying strata. The trend of the second type faults were usually consistent with that of the strata. The faults in the Wangjiagang fault tectonic zone are very well developed, including south-dipping and north-dipping faults. The north-dipping faults are mostly developed in the northern region, mainly with NE strikes; while the south-dipping faults are developed in the southern region, mainly with EW strikes. The faults of the Chenguanzhuang fault zone mainly dip north, with a NE strike. Faults near the uplift zone and Caonan slope zone are not developed.

5.1.2 Characteristics of Stratigraphy The Cenozoic deposits in the study area chronologically include the include the Kongdian, Shahejie, Dongying, Guantao, Minghuazhen and Pingyuan Formation from bottom to top (Table 5.1). And the Paleogene deposits include Kongdian, Shahejie and Dongying Formation, which constitute the lower tectonic sequence, while the Neogene Guantao Formation and Minghuazhen Formation of constitute the upper tectonic sequence. There is an unconformity between the upper and lower tectonic sequence.

Shahejie

Eocene

Es1

Dongying

Oligocene

Second member

First member

Ed

Guantao

Miocene

Paleogene

Minghuazhen

Pliocene

Es2

Ng

Nm

Qp

Neogene

Pingyuan

Holocene

Code

Quaternary

Formation and member

Series

System

Stratigraphy

0–350

0–450

100–800

300– 400

100– 120

250–350

Thickness (m)

Table 5.1 Stratigraphic characteristics of Dongying Sag (Wang et al., 1993)

Floodplain

Floodplain

Sedimentary environment

Green and gray mudstone are interbedded with sandstone and pebbly sandstone and mixed with carbonaceous mudstone

Gray, grayish brown mudstone, oil mudstone, carbonate rock and oil shale

Grayish green, gray, a small amount of variegated mudstone interbedded with sandstone and glutenite

(continued)

Shore of shallow lake, river-delta

Lake

River-delta

The upper member is interbedded Fluvial Facies with purplish red and grayish green mudstone and siltstone; the lower member is thick gray and grayish white conglomerate and pebbly sandstone, and sandstone is mixed with green and purplish red mudstone

Brownish yellow, brownish red mud mixed with light gray, brownish yellow siltstone

Yellow and gray clay mixed with fine siltstone

Lithology

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System

Stratigraphy

Series

Table 5.1 (continued)

Fourth member

Third member

Formation and member

200–400

100–300

1500–1600

Es3 2

Es3 3

Es4 1

Middle

Lower

Upper

100– 300

Es3 1

Thickness (m) Upper

Code

Sedimentary environment

Deep lake—semi-deep lake

Deep lake—semi-deep lake, turbidite fan

(continued)

Brownish gray mudstone and dark Semi-enclosed salt lake gray oil shale are interbedded with different thicknesses, mixed with thin sandstone, dolomite, limestone and biological limestone

Mainly dark gray, brown mudstone, calcareous mudstone and oil shale, mixed with a small amount of siltstone and light gray unequal-grain sandstone

Gray and dark gray extremely thick mudstone mixed with turbidite sandstone or thin carbonate rock

Gray and dark gray mudstone are River-delta interbedded with siltstone, mixed with calcareous sandstone, pebbly sandstone, oil shale and thin carbonaceous shale

Lithology

5.1 Characteristics of Geological Elements of Reservoir Formation 297

System

Stratigraphy

Series

Table 5.1 (continued)

Kongdian

Ek2

Second member

Es4 2

Ek1

Lower

Code

First member

Formation and member

0–900

Thickness (m)

Purplish red/brown and gray mudstone mixed with fine siltstone

Brownish red sandstone and purplish red mudstone are interbedded with different thicknesses

Mainly purplish red and grayish green mudstone, mixed with sandstone, pebbly sandstone, thin carbonate rock and oil shale

Lithology

Confined lake

Alluvial plain, shallow lake

Semi-deep lake and alluvial fan

Sedimentary environment

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5.1 Characteristics of Geological Elements of Reservoir Formation

5.1.2.1

299

Paleogene System

(1) Kongdian Formation (Ek) There is an angular unconformity between the Kongdian Formation and the underlying pre-Cenozoic strata. The Kongdian Formation can be divided into upper and lower members with different lithology (Table 5.1). The lower member of the Kongdian Formation (Ek2 ) was deposited in a humid and hot lacustrine environment, which contains gray, dark gray and grayish purple mudstone mixed with sandstone, pebbly sandstone, siltstone, oil shale, carbonaceous mudstone and coal seams. The upper member of the Kongdian Formation (Ek1 ) is mainly alluvial fan deposit, and locally offshore and lakeside deposit, which contains brownish red sandstone and purplish red mudstone interbedded with different thicknesses. The lithology in the bottom of Ek1 is purplish red glutenite deposits with thicknesses ranging from 10 to 80 m, and the clastic grain size gradually becomes fine, and the sandstone thickness gradually becomes thin from bottom to top. (2) Shahejie Formation (Es) The Shahejie Formation is divided into the fourth of Shahejie Formation (Es4 ), the third member of the Shahejie Formation (Es3 ), the second member of the Shahejie Formation (Es2 ) and the first member of the Shahejie Formation (Es1 ) from bottom to top, and each member is subdivided into several submembers. The main source rocks and reservoirs are developed in Es in the study area. 1. Fourth member of the Shahejie Formation (Es4 ) The Es4 fourth is divided into upper and lower submembers according to the difference in sedimentary environment. The lower submember of the fourth member of the Shahejie Formation (Es4 2 ) is mainly coarse-grained alluvial fan deposits, and the lithology is mainly purplish red and grayish green mudstone interbeded with sandstone, pebbly sandstone, thin carbonate and oil shale. Salt rock and gypsum interlayers are common in the middle of the sag. The upper submember of the fourth member of the Shahejie Formation (Es4 1 ) contains salt-brackish lacustrine deposits. The lithology is brownish gray mudstone and dark gray oil shale interbedded with thin layers of sandstone, dolomite, limestone and biological limestone, and massive sandstone and reef limestone are found locally. The Es4 1 is one of the main source rocks in the study area. 2. Third member of the Shahejie Formation (Es3 ) The third member of the Shahejie Formation is characterized by dark sand and mudstone deposited in lacustrine facies, which can be subdivided into three submembers: upper, middle and lower. The lower submember of the third member of the Shahejie Formation (Es3 3 ) contains brackish-freshwater lacustrine deposits with a thickness of 100–300 m. The lithology is mainly dark gray mudstone and grayish brown oil shale or oil mudstone interbedded with unequal thickness strata, mixed with a small amount of siltstone

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and light gray unequal-grain sandstone, and the margin area of the sag is interbedded with sandstone and mudstone. The Es3 3 is another main source rocks in the study area. The lithology of the middle submember of the third member of the Shahejie Formation (Es3 2 ) mainly contains thick deep-semideep lacustrine argillaceous rocks mixed with multiple groups of turbidite sandstone, siltstone or thin carbonate rocks, with widespread distribution in the whole area and thicknesses of 200–400 m. There was no unconformity between the Es3 2 and Es3 3 in the middle of the Niuzhuang subsag, while the Es3 2 is in angular unconformity contact with the underlying strata. The upper submember of the third member of the Shahejie Formation (Es3 1 ) contains mainly fluvial–delta deposits with a thickness of 100–300 m. The lithology is interbedded gray, dark gray mudstone and siltstone mixed with calcareous sandstone, pebbly sandstone, oil shale and thin carbonaceous shale. Glutenite is mainly in the upper-fining sand-rich cycle. Calcareous sandstone, pebbly sandstone or oolitic limestone are common at the top of sandstone, and carbonaceous mudstone is relatively developed. The Es3 1 is one of the important reservoirs in the study area. 3. Second member of the Shahejie Formation (Es2 ) The lower part of the second member of the Shahejie Formation contains fluvial– delta deposits with a thickness of 0–220 m. The lithology is green and gray mudstone interbedded with sandstone and gravelly sandstone mixed with carbonaceous mudstone, and a small amount of purplish red mudstone is found in the upper half. The Upper Submember of Shahejie Formation contains mainly littoral shallow lake deposits with a thickness of 0–100 m. The lithology is grayish green and purplish red mudstone interbedded with gray sandstone mixed with calcareous sandstone and pebbly sandstone. 4. First member of the Shahejie Formation (Es1 ) The first member of the Shahejie Formation contains mainly shallow-semideep lacustrine deposits, widely distributed in the whole region with a thickness of 0–450 m, which is an important correlation marker interval. The lithology is mainly gray, dark gray, grayish brown mudstone, oil mudstone, carbonate rock and oil shale. Due to the shallow burial depth, the dark mudstone has not yet entered the hydrocarbon generation threshold and is not an effective source rock but can form the main regional caprock. (3) Dongying Formation (Ed) The Dongying Formation is dominated by fluvial–delta facies deposits, with a great thickness variation, generally 100–540 m, and its top has had different degrees of denudation and has an unconformable contact with the overlying strata. The lithology of the Dongying Formation is mainly thick massive brownish red mudstone, medium-thick pebbly sandstone, interbedded conglomerate and mudstone, and sandy mudstone mixed with grayish white fine siltstone.

5.1 Characteristics of Geological Elements of Reservoir Formation

5.1.2.2

301

Neogene System

(1) Guantao Formation (Ng) The Guantao Formation contains mainly braided stream to meandering stream facies deposits, with thicknesses of 200–400 m, and is in unconformable contact with the underlying strata. The lithology at the bottom is gray, light gray, and grayish white, thick-layered conglomerate; pebbly sandstone and sandstone mixed with green to purple mudstone; and sandy mudstone. The lithology at the top is interbedded purplish red, dark purple and grayish green mudstone; interbedded sandy mudstone and siltstone; and mixed with siltstone and fine sandstone. (2) Minghuazhen Formation (Nm) The Minghuazhen Formation contains mainly a set of floodplain facies deposits with thicknesses of 100–120 m. The lithology is brownish yellow and brownish red mudstone mixed with light gray and brownish yellow siltstone. Siltstone is developed in the upper part, and calcareous ferromanganese nodules, gypsum crystals and grayish green mudstone strips are mixed in the lower part. It has a conformable or pseudo-conformable contact with the underlying Guantao Formation.

5.1.2.3

Quaternary Deposits

The Quaternary Pingyuan Formation (Qp) is also a set of floodplain facies deposits, and its lithology is yellow and gray clay mixed with fine siltstone with a thickness of 250–350 m. The Plain Formation and the underlying Minghuazhen Formation are in regional unconformable contact.

5.1.3 Petroleum Geological Conditions After years of hydrocarbon exploration and basic research, it was confirmed that the Tertiary petroleum geological conditions in the southern slope area of the Dongying Sag are superior (Li et al., 2000; Li & Pang, 2004; Wang & Qian, 1992), which are mainly manifested as follows: The upper submember of the fourth member of the Shahejie Formation and the middle submember of the third member of the Shahejie Formation are developed with thick layers and large areas of high-quality source rocks, featuring high-organic-matter abundance and great hydrocarbon generation potential; the sand bodies in different facies zones such as alluvial fan, delta, beachbar sandstone and turbidite fan of the Shahejie Formation were superimposed and distributed contiguously in multiple periods, and the reservoir physical properties are good; and at the same time, the thick regional caprocks in the first member of the Shahejie Formation and Minghuazhen Formation above the reservoir have excellent

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5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

continuity and sealing property, and two favorable source–reservoir–caprock assemblages were formed longitudinally: self-generating, self-reservoir caprock and lowergenerating, upper reservoir top caprock. This favorable source–reservoir–caprock condition and a large number of tectonic and lithological traps developed during the rifting period constitute the basic geological conditions for large-scale hydrocarbon accumulation in the study area.

5.1.3.1

Source Rocks

There are many potential source rocks in the Tertiary strata of the Dongying Sag. From bottom to top, they are the upper submember of the fourth member of the Shahejie Formation (Es4 1 ), the lower submember of the third member of the Shahejie Formation (Es3 3 ), and the middle submember of the third member of the Shahejie Formation (Es3 2 ). The source rocks in Es4 1 , Es3 3 and Es3 2 all have good hydrocarbon generation potential. An oil-source correlation study showed (Li & Pang, 2004; Zhang et al., 2003) that the hydrocarbons discovered in the southern slope area of the Dongying Sag were mainly derived from the dark lacustrine mudstone in the Es4 1 and Es3 3 , followed by the source rocks in Es3 2 . The source rocks in the Es4 1 are mainly dark gray mudstone and grayish brown calcareous shale, which represent shallow lake-semideep lake saltwater or salt lake deposits. Source rocks are widely developed around the center of the Niuzhuang Subsag, with a maximum cumulative thickness of 350 m, generally 200–300 m. The organic carbon content (TOC) of dark mudstone in the Es4 1 is mainly distributed in a range of 1.5%–4.5%, and some shale samples reach 6.5%–10%, with an average value of 3.12%. The pyrolysis hydrocarbon generation potential S 1 + S 2 is mainly 4–28 mg/g, and that of some samples is up to 44–72 mg/g. The organic matter type is mainly Type I, and a few are Type II1 , with highly soluble hydrocarbon content, which is beneficial to hydrocarbon generation. At the same time, the source rocks in the Es4 1 contain organic matter with high maturity, and the Niuzhuang Subsag has entered the stage of large-scale oil generation except at the edge of the sag. The source rocks in the Es3 3 are mainly dark gray mudstone, and the bottom-up color change in the mudstone is from dark to light, which was formed in a humid and brackish deep lake environment. The cumulative thickness of dark mudstone is large, and its distribution is widespread, generally 200–3300 m in the Niuzhuang Subsag. The source rocks in the Es3 3 also contain high organic matter abundance, in which the organic carbon content of massive mudstone is mainly distributed in a range of 2.0%–3.5%, while that of the oil shale is up to 6.0%–10.5%, with an average value of 4.86%. The corresponding pyrolysis hydrocarbon generation potential value S 1 + S 2 also has two main distribution ranges: 4–20 mg/g and 36–56 mg/g. The kerogen type of the source rocks is mostly Type I and occasionally Type II1 . The vitrinite reflectance Ro value is 0.36%–0.80%, and the Niuzhuang Subsag has high organic matter maturity, has entered the peak period of oil generation and is comprehensively evaluated as a high-quality source rock.

5.1 Characteristics of Geological Elements of Reservoir Formation

303

The source rocks in the Es3 2 are mainly dark gray mudstone, formed in a semideepshallow lake front delta facies sedimentary environment with weak reduction. The dark mudstone has a large thickness, up to 500 m, in the Niuzhuang Subsag. Compared with the first two sets of source rocks, the abundance of organic matter in the Es3 2 is relatively low, and the organic carbon content shows a single-peak distribution, mainly ranging from 1.5% to 3.0%, with an average value of 1.98%. The sum of the amount of free liquid hydrocarbons values (S 1 ) and the amount of total residual hydrocarbons values (S 2 ) is usually used to determine the hydrocarbon generation potential of source rocks. The hydrocarbon generation potential value (S 1 + S 2 ) is between 4 and 12 mg/g. In addition to Type I, most kerogen consists of Type II1 and Type II2 organic matter. The vitrinite reflectance Ro is mostly between 0.32 and 0.6, which is comprehensively evaluated as good source rocks.

5.1.3.2

Reservoirs

Hydrocarbon exploration has shown that the hydrocarbon-bearing reservoirs discovered in the study area mainly include pre-Paleogene strata, Paleogene Kongdian Formation, fourth member of the Shahejie Formation, third member of the Shahejie Formation, second member of the Shahejie Formation and first member of the Shahejie Formation. The reservoir in the Kongdian Formation is mainly alluvial fan facies sand bodies, with a large sandstone thickness and good physical properties. The Es4 2 contains alluvial fan sedimentary sand bodies that act as reservoirs, and the beach-bar sand and calcareous bar sand in the Es4 1 are also important reservoirs, which are mainly distributed on the southern slope of the Niuzhuang Subsag. The reservoir in the Es3 3 is not developed, and only lenticular sand bodies representing deep-water turbidite fan deposits occur locally in the center of the Niuzhuang Subsag. The reservoirs in the Es3 2 include turbidite sand bodies and delta sand bodies. Turbidite sand bodies are mainly developed in the Niuzhuang Subsag, while delta sand bodies are mainly distributed in the southern slope zone. The reservoirs in the Es3 1 is a delta front sand body, and acts as the main reservoir in the study area, and the sandstone percentage is generally higher than 30%. The lithology of the reservoir is siltstone, with an average porosity of 29.9% and a permeability of 168 × 10–3 um2 . The reservoir in the Es2 is a delta sandstone body with great lithological changes and significant heterogeneity. The reservoir features a pore type with good reservoir physical properties and is one of the most important high-yield reservoirs. The reservoir in the Es1 is mainly sandstone, biological limestone and pinhole limestone.

5.1.3.3

Caprock and Source–Reservoir–Caprock Assemblage

There are many sets of caprocks in the study area, such as the Minghuazhen Formation and the first, third and fourth members of the Shahejie Formation (Dai & Li, 1991; Li et al., 2000; Liu et al., 2004), among which the first member of the Shahejie Formation and Minghuazhen Formation have regional caprocks. The first member

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5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

of the Shahejie Formation is mainly a set of terrigenous-biochemical deposits, such as semideep lacustrine grayish black mudstone and biological granular limestone beach deposits. The thick mudstone is widespread, with a thickness of more than 150 m, accounting for approximately 80% of the total thickness (Li, 1991). The Minghuazhen Formation is a set of terrigenous deposits, such as mudstone, sandy mudstone and sandstone, of river floodplain facies. The cumulative thickness of mudstone is generally approximately 400 m, and the maximum thickness is more than 600 m. The mudstone accounts for more than 70% of the total thickness of the layer. This set of mudstone strata is also a good regional caprock (Shuai & Wang, 1993). The lacustrine and delta plain mudstones in the Es3 and Es4 represent good local caprocks. In particular, the salt-gypsum rock and gypsum-mudstone developed near the middle of the sag in the Es4 have excellent sealing ability. The lacustrine mudstone in the Es3 is widely distributed, with a cumulative thickness of approximately 300 m and a maximum thickness of over 900 m. The continuous thickness of mudstones in the Es3 2 is over 100 m, and the mudstone ratio is approximately 70%. These thick mudstones can directly seal the hydrocarbons in the lower reservoir. According to the formation sequence of source rocks, reservoirs and caprocks and their spatial assemblage relationships, the source–reservoir–caprock assemblage relationship in the study area is mainly divided into three types: self-generating selfreservoir caprock, lower-generating upper-reservoir caprock and upper-generating lower-reservoir upper caprock. The self-generating self-reservoir caprock type mainly occurs in the Es3 and the Es4 1 . The Es3 2 middle, Es3 3 lower submembers and the Es4 1 upper submember are source rock series, within and around which the sand bodies are developed as reservoir bodies, with mudstone capping between layers, and characterized by the formation of self-source lithological hydrocarbon reservoirs. The lower-generating upper-reservoir caprock type is the most important type in the study area. The hydrocarbons generated in the Es3 and Es4 1 enter the reservoirs in the Es3 1 upper submember, Es2 t and Es1 first through the connected sand bodies, faults, unconformity surfaces and other carriers, directly sealed by the overlying thick mudstone or the regional caprock in the Es1 , forming other-source types of tectonic and tectonic-lithological reservoirs. The upper-generating lowerreservoir upper caprock type uses the Es3 2 , Es3 3 , and Es4 1 as source rocks, which are connected with the underlying lower submember of the fourth member of the Shahejie Formation and Kongdian Formation reservoir beds through a step-like carrier system composed of a fault-carrier bed unconformity surface. The source rock interval is both a hydrocarbon source stratum and a caprock belonging to an other-source reservoir.

5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation …

305

5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation Dynamics Studies based on hydrocarbon migration and accumulation dynamics need to reconstruct the process of basin stratum burial evolution, quantitatively recover the characteristics of temperature and pressure fields during basin evolution, and simulate the hydrocarbon expulsion range and expulsion amount of source rocks in different geological periods (Luo, 2008). In this study, previous research results on tectonic geology, stratigraphy and sedimentology were integrated, and a basin geological model was established through comprehensive sorting and analysis of various basic data, such as geological, geophysical and geochemical data, on the southern slope of the Dongying Sag. Under the constraint of the measured data of ancient and present temperature and pressure, the basin simulation method was used to quantitatively/semiquantitatively recover the evolutionary history of fluid pressure, thermal evolutionary history of source rocks and hydrocarbon expulsion history during the stratum burial evolution in the study area.

5.2.1 Establishment of the Basin Geological Model The basin simulation method is an important means to quantitatively study the basin burial evolutionary process. Reasonable and accurate establishment of a geological model is the premise of basin simulation, and researcher insight into geological phenomena, boundary conditions and selection of important parameters determines the authenticity and reliability of basin simulation results (Luo 2000). In the basin modeling, we carefully analyzed the basin tectonics, sedimentary characteristics and hydrocarbon geological conditions; made full use of various geological, geophysical and geochemical datasets in the study area; determined the key parameters in the basin model; and adopted Temis basin model software from the Institut Francais du Petrole to simulate and analyze the basin geological model.

5.2.1.1

Establishment of the Basin Geological Model

The establishment of a basin geological model uses the existing seismic and drilling data in the study area; synthesizes the previous research findings of basin evolution, key tectonic events and stratigraphic chronology; divides the time stages of basin evolution as finely as possible; and builds the current basin three-dimensional tectonic framework and lithological stratigraphic framework model.

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(1) Establishment of the Evolutionary Stages and Three-dimensional Tectonic Framework of the Basin In the basin model, the evolutionary history of the basin is set as a series of events with specific times and processes, and each evolutionary stage represents the occurrence time unit of sedimentation or uplift and denudation. According to previous studies on the tectonic evolution of basins (Pan, 1998; Shuai & Wang, 1993; Wang & Qian, 1992; Zong et al., 1999), the Cenozoic Dongying Sag mainly experienced two major stages: the Paleogene rift period and the Neogene postrift depression period. The Kongdian Formation, Shahejie Formation and Dongying Formation were continuously deposited in the Paleogene. Since the Neogene, the Guantao Formation, Minghuazhen Formation and Plain Formation were continuously deposited. The Dongying movement between the two led to the overall uplift and denudation of the study area. On the basis of previous studies on sequence stratigraphy in the study area (Feng et al., 2006; Pan et al., 2000), we divided the basin evolution into 13 time units (Table 5.2). The duration was from the period of the fourth member of the Shahejie Formation of Eocene age to the present, mainly including 12 sedimentary stages and 1 denudation stage. The corresponding geological ages of the basin evolutionary stages are shown in Table 5.3. Stratigraphic structural maps were transformed into three-dimensional discrete structural data that were imported into Temis3D basin simulation software and reinterpolated according to grids of 1000 × 1000 × 13, thus building the threedimensional basin structural framework model of the study area (Fig. 5.3). Table 5.2 Chart showing chrono-stratigraphy, geological ages of the basin evolutionary stages of the Dongying Sag Stratigraphy

Geological event

System

Series

Formation and member

Quaternary

Holocene

Plain formation

Neogene

Paleogene

Sedimentary

Geological age (Ma) End time

Start time

0

2

Pliocene

Minghuazhen

Sedimentary

2

6

Miocene

Guantao

Sedimentary

6

14

Denudation

14

24.6

Oligocene

Dongying

Eocene

Shahejie

Es1 Es2 Es3

Es4 Kongdian

Sedimentary

24.6

32.8

Sedimentary

32.8

36.7

Sedimentary

36.7

38.2

1

Sedimentary

38.2

38.6

Es3 2

Sedimentary

38.6

42

Es3 3

Sedimentary

42

43.7

Es3

1

Sedimentary

43.7

45

Es4 2

Sedimentary

45

50.5

Sedimentary

50.5

60.5

Es4

Muddy sandstone

50% arenaceous rock, 50% argillaceous rock Sandy mudstone

Muddy sandstone

60% arenaceous rock, 40% argillaceous rock

40% arenaceous rock, 60% argillaceous rock

Argillaceous siltstone

70% arenaceous rock, 30% argillaceous rock

Argillaceous rock

Siltstone

80% arenaceous rock, 20% argillaceous rock

2.74

2.725

2.71

2.695

2.68

2.665

Pure sandstone, glutenite, etc 2.65 Silty fine sandstone

100% sandy particle

90% arenaceous rock, 0% argillaceous rock

Sandstone

Arenaceous rock

Density (g/cm3 )

Actual rocks

Rocks in the model

Lithology

Table 5.3 Lithology and physical parameters in basin model of the southern slope in Dongying Sag

2.23404

2.5

2.54032

2.72727

3.1015

3.19797

3.5

Thermal conductivity (W·m−1 ·°C−1 )

904

930

936

952

968

984

1000

(continued)

Heat capacity (J·°C−1 ·kg−1 )

5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation … 307

Mudstone

Lithology

Table 5.3 (continued)

Silty mudstone Silty mudstone

20% arenaceous rock, 80% argillaceous rock

10% arenaceous rock, 90% argillaceous rock Mudstone, shale

Sandy mudstone

30% arenaceous rock, 70% argillaceous rock

100% argillaceous rock

Actual rocks

Rocks in the model

2.8

2.785

2.77

2.755

Density (g/cm3 )

1.8

1.89189

1.99367

2.10702

Thermal conductivity (W·m−1 ·°C−1 )

840

856

872

888

Heat capacity (J·°C−1 ·kg−1 )

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5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation …

a..3D discrete construction data

309

b.Gridded 3D basin tectonic framework model

Fig. 5.3 3-D structural framework model of the South slope in Dongying Sag

(2) Stratum Lithological Model The lithological data in the basin model were derived from the analysis of natural gamma ray (GR) and spontaneous potential (SP)-logging curves from 440 exploration wells in the study area, and the lithology of undrilled strata was obtained by interpolation of adjacent well data. According to the sandstone percentages in Tertiary strata, the rocks were divided into 11 types in this study (Table 5.3): sandstone (100% arenaceous), mudstone (100% argillaceous), and transition types between sandstone and mudstone (9 types), thereby establishing the lithological composition of the basin model in the study area. At the same time, using the comprehensive mud-logging data and acoustic-logging data of approximately 400 exploration wells, we counted the sandstone percentage data in the Es4 1 , Es3 3 , Es3 2 , Es3 1 , Es2 , Es1 , Ed, Ng, Nm and Qp. We also created a contour maps of sandstone percentage sandstone percentageof each set of strata. After numeralization and appropriate coarsening, the Temis 3D module was introduced to define the lithological distribution of different stratigraphic units, thus constructing a three-dimensional stratigraphic lithological framework model of the study area (Fig. 5.4). Different colors in the map represent different lithology types.

5.2.1.2

Key Simulation Parameters for Basin Modelling

To make the numerical simulation results close to the geological reality, the selection of simulation parameters is very important. (1) Compaction coefficient The compaction coefficient indicates the change in porosity with depth and is the key parameter to recover the burial history and hydrodynamics of the basin. After

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Fig. 5.4 Three-dimensional stratigraphic framework lithology model of the southern slope of Dongying Sag

sedimentation, under the action of overlying load or tectonic stress, sediments experience the process of continuous expulsion of formation water, decrease in porosity and decrease in formation thickness. Generally, under hydrostatic pressure conditions, there is an exponential relationship between porosity and depth, that is, the Athy formula (Athy, 1930): φ = φ0 · e−c·z

(5.1)

where φ is the porosity at depth Z, φ 0 is the porosity of the surface, c is the compaction coefficient, representing the compaction slope of the normal compaction section, and z is the depth. Because the compaction effect is controlled by many factors, such as tectonic interval, rock assemblage type, and anadiagenesis, the compaction rule in different positions of the basin is quite different. To avoid the accidental factors of a single tectonic unit, we selected 100 wells with complete stratigraphic development and relatively complete acoustic logging, mud logging and analysis and testing data in different tectonic units; examined their mudstone acoustic transit time values; and compiled compaction curves (Fig. 5.5). Through the analysis of the characteristics of the compaction curve, we determined the following findings: not only were the compaction rules in the sag area, the slope area and the uplift area different, but there were also different compaction intervals in different vertical horizons. Therefore, to ensure that the basin model is established

5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation …

311

Fig. 5.5 Characteristics of mudstone compaction curve of representative well of different tectonic units in the study area

as close as possible to the real geological conditions, the mudstone porosity–depth relationship should be established in different zones and intervals. The measured mudstone porosity data collected from the routine experiment reports of the Shengli Oilfield Institute of Geological Science were used to examine the acoustic transit time corresponding to the test depth interval, and the statistical relationship between the two was established: φ = 0.1146 ∗ Δt − 36.95

(5.2)

where φ is the porosity, %; Δt is the acoustic time difference, μs/m. Accordingly, the acoustic transit time–depth relationship of more than 100 wells was converted into the porosity–depth relationship, and the compaction coefficients of different tectonic units and different intervals were obtained by fitting the slope of the porosity–depth variation through regression (Table 5.4). (2) Stratum denudation thickness Denudation thickness is the key parameter to recover the burial history of the basin, which has the greatest influence on the recovery of the paleotectonic evolutionary history and hydrodynamic evolution. The denudation event caused by Dongying episode tectonics had an important influence on hydrocarbon generation, migration and accumulation stages, fluid pressure evolution, etc.; therefore, the denudation

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Table 5.4 Porosity-depth relationship of different tectonic units in the study area Tectonic zoning

Sag zone

Slope zone

Uplift zone

Guantao formation – Minghuazhen formation

φ = 0.69e(−0.0003076 z)

φ = 0.68e(−0.0003088 z)

φ = 0.67e(−0.0003736 z)

Dongying formation

φ = 0.69e(−0.0001769 z)

φ = 0.68e(−0.0003088 z)

φ = 0.67e(−0.0003736 z)

First member of Shahejie formation ~ middle submenber of the third member of Shahejie formation

φ = 0.69e(−0.0003936 z)

φ = 0.68e(−0.0004890 z)

φ = 0.67e(−0.0007217 z)

event in this episode must be considered. For denudation thickness data, please refer to the research results of Zhang et al. (2005) (Fig. 5.6). (3) Thermal conductivity The Shengli Oilfield Institute of Geological Science carried out rock thermal conductivity tests on representative samples of Tertiary strata in the Jiyang Depression. As shown in Table 5.5, the average thermal conductivity of the strata is 1.7– 2.23 W/(m·K). According to these measured data, the thermal conductivity parameters of each set of stratigraphic units in the basin model are set as follows.

Fig. 5.6 Denudation thickness of the southern slope of Dongying Sag at the end of Dongying period (Zhang et al., 2005)

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313

Table 5.5 Thermal conductivity data of rocks in Dongying Sag (Shengli oilfield institute of geological science, 2007a ) Stratum

Nm

Ng

Ed

Es1

Es2

Es3

Es4

Ek

Average value (W/(m·K))

2.04

1.97

2.09

1.90

1.70

1.81

1.98

2.23

± Standard Deviation (W/(m·K))

0.34

0.26

0.14

0.3

0.17

0.5

0.5

0.49

Number of samples

3

7

5

5

7

8

4

5

a

Shengli oilfield institute of geological science, 2007. Quantitative study on hydrocarbon generation and reservoir formation in Dongying Sag. Internal report

(4) Paleogeothermal parameters Previous researchers have carried out much research work on the paleogeotemperature field of the Dongying Sag by using numerical simulation methods (Zhang et al., 2005; Qiu et al., 2006), and the results are basically consistent. It is believed that basin thermal evolution experienced a process from “hot” to “cold”, which was consistent with the basin evolution of the early rift and late depression in the study area. The surface temperature, geothermal gradients and geothermal heat flow of rocks in different deposition stages summarized from previous research works (Zhang, 2005; Qiu et al., 2006) were taken as reference values (Table 5.6) in basin modelling of the southern slope of Dongying Sag. (5) Geochemical parameters of the source rocks In the southern slope area of the Dongying Sag, dark mudstone and oil shale were the main hydrocarbon-generating strata in the Es4 1 , Es3 3 and Es3 2 (Pan et al., 2000; Zhang et al., 2005). According to a kerogen maceral analysis on polished surfaces of whole rocks carried out by Zhang et al. (2005), the sapropelic components in source rocks of the Table 5.6 Paleo-geothermal data in Dongying Sag Geological age (Ma)

Surface temperature (°C)

Paleo-geothermal gradients (°C / 100 m)

Paleogeothermal heat flow (mW/m2 )

Qp

2

14

3.5

66.5

Nm

6

12

3.65

68.7

Ng

14

12

3.85

75.0g

Denudation period

24.6

12

3.85

70.9

Ed

32.8

12

4.1

75.0

Es1

36.7

15

4.2

77.1

Es2

38.2

15

4.3

79.2

Es3

43.7

15

4.6

79

Es4

50.5

16

4.95

81.2

Ek

60.5

15

5.41

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Es4 1 and Es3 3 are generally more than 85%; in particular, the sapropelic components in oil shale are more than 95%, more than 70% of the samples are Type I kerogen, and a few are Type II1 , with high oil generation potential. The source rocks in the Es3 2 have more terrigenous macerals compared to that in the Es4 1 and Es3 3 . The proportions of Type I, Type II1 and Type II2 kerogens of source rocks in the Es3 2 are generally equal, while Type I is slightly dominant. The distribution of organic matter types in each set of source rock intervals was determined according to the research results of Pan et al. (2000). The contour map of total organic carbon content (TOC) source rocks in each interval made by Li et al. (2003a, 2003b) was converted to a numerical value and then imported into the software module. Zhang et al. (2005) used a Rock–Eval device to conduct simulation experiments under an open system and established three sets of hydrocarbon generation dynamic models of the main source rocks. Figure 5.7 shows the dynamic parameter distribution of organic matter oil generation and gas generation of typical samples from the upper submember of the fourth member of the Shahejie Formation, the lower submember of the third member of the Shahejie Formation and the middle submember of the third member of the Shahejie Formation. As shown in the figure, the hydrocarbon generation activation energy of organic matter in the Es4 1 is obviously lower than that of the other two sets of source rocks, indicating that the source rocks in the Es4 1 may have started hydrocarbon generation under lower thermal conditions, which was beneficial to the generation of immature-low mature oil (Lu et al., 1999).

5.2.2 Modelling of the Evolution of Pressure System The characteristics and evolution of the pressure system are closely related to the dynamic mechanisms and processes of hydrocarbon migration and accumulation. Based on the analysis of present-day pressure characteristics, taking the measured present pressure and paleopressure data recovered by fluid inclusion as the calibration, the distribution and evolution of the pressure system in the study area were quantitatively studied by using a numerical simulation method.

5.2.2.1

Characteristics of Present Pressure System

The present pressure system is the result of the balance of various geological factors in the long evolutionary process, and it is also the premise of understanding the hydrodynamic mechanisms during the hydrocarbon reservoir formation period. This study mainly analyzed the characteristics of the present fluid pressure system in the study area based on the measured static pressure data of drilling and oil testing and the pressure estimated by acoustic transit time in acoustic logging. (1) Characteristics of the measured reservoir pressure

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315

a. Activation energy of oil generation (left) and gas generation (right) of organic matter in the Es41

b. Activation energy of oil generation (left) and gas generation (right) of organic matter in the Es33

c. Activation energy of oil generation (left) and gas generation (right) of organic matter in the Es32

Fig. 5.7 Activation energy of organic matter of source rocks in Dongying Sag (Zhang et al., 2005)

At present, repeat formation testing (RFT) and drill stem testing (DST) are the most direct and accurate means to obtain reservoir pressure. However, due to the limitations of test conditions, only a limited amount of pressure data can be obtained. Therefore, the measured pressure data of each well are usually integrated to reflect the longitudinal distribution of pressure in permeable formations in the form of measured pressure changes with depth. Through statistics of 710 pieces of measured pressure data from more than 200 wells in the study area (Fig. 5.8), the measured reservoir pressure data show vertical segmentation. In terms of depth, the top boundary of overpressure is generally located at 2200 m, the formation pressure measured in the formation with a buried depth of more than 2200 m is basically hydrostatic pressure, and abnormal pressure is found below 2200 m. The magnitude of excess pressure varies greatly, ranging from 0 to

316

5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

30 MPa. With the increase in the burial depth of the formation, the magnitude of overpressure increases gradually. There is no abnormal fluid pressure inthe strata overlying the Es3 1 , including Es2 , Es1 , Ed, Ng, and Nm. The measured pressure data with excess pressure generally occur in the Es3 2 , Es3 3 , Es4 1 , and Ek, which indicates that there are overpressure in the strata underlying the Es3 1 , including Es3 2 , Es3 3 , Es4 1 , and Ek. (2) Characteristics of fluid pressure in mudstone Based on the mudstone compaction curve of drilling data, the fluid pressure of mudstone was calculated by using the equivalent depth method (Magara, 1978), and the characteristics of fluid pressure in mudstone was analyzed and compared to that of sandstone. Figure 5.9 shows fluid pressure in the mudstone of two representative wells in the study area. The fluid pressure of mudstones in the stratum overlying the Es3 1 of the wells in or nearby the Niuzhuang Subsag is normal pressure, while that in the stratum

Fig. 5.8 Measured reservoir pressure-depth relationship on the southern slope of Dongying Sag

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317

a. T61 Well

b. W732 Well Fig. 5.9 Mudstone compaction curves and calculated fluid pressure in mudstones in the eastern part of the southern slope of Dongying despression

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underlying the Es3 1 is usually overpressure, especially in the Es3 3 and Es4 , as that of the well shown in Fig. 5.9a. Generally, there is overpressure in the strata with buried depth larger than 2700 m in the wells in or nearby the Niuzhuang Subsag. The value of excess pressure in the Es3 is usually greater than that in the Es4 , because the proportion of fine-grained rocks is smaller in Es4 and the lithofacies vary more frequently, which makes the pressure more difficult to accumulate in Es4 . However, in the southern slope zone and uplift zone, overpressure is not developed, and the pressure data are basically distributed near the hydrostatic pressure gradient line (Fig. 5.9b). The calculation results of fluid pressure in mudstone of wells show that the excess pressure in mudstones of the Es3 2 , Es3 3 , and Es4 1 in the Niuzhuang Subsag is relatively higher, the excess pressure in mudstone decreases gradually from the Niuzhuang Subsag to the southern slope zone and uplift belts, and the Niuzhuang Subsag Among the intervals, the excess pressure in the mudstones of Es3 3 in the central part of Niuzhuang Subsag is the largest, up to 20 MPa. The distribution characteristics of excess pressure in mudstones in the Es4 upper submember are similar to those of Es3 3 , and the maximum value of excess pressure in mudstones is about 16 MPa, which is less than that of Es3 3 , but higher than that of Es3 1 . The distribution characteristics of abnormal pressure shows that the abnormal pressure of mudstone in the Niuzhuang Subsag indicate that the undercompaction of mudstone is the main mechanism for abnormal pressure formation. During the sedimentary period of the Es4 , especially the sedimentary period of the Es3 , the whole Dongying Sag was at the zenith of lake basin rifting, and extremely thick fine-grained argillaceous sediments were deposited. The rapid accumulation of sediments led to high abnormal pressure generated due to impeded drainage in high-purity mudstone. In addition, the thick mudstone strata in the overpressure center area in the sag were also the main source rocks in the study area, hydrocarbon generation may have also been one of the important reasons leading to overpressure, and its contribution to the formation of overpressure has yet to be examined. Comparing the fluid pressure in mudstone with the measured pressure in sandstone (Figure 5.10), found that the pressure in mudstone is generally greater than or equal to the pressure in sandstone, which indicates that the overpressure in the sandstone in the study area mainly originates from the pressure transfer of adjacent mudstones. There is overpressure in the sandstone in a few wells near fault, and the fluid pressure of the sandstone is higher than that of mudstone. This is because the fault connects the high-pressure zone of the Es3 3 , Es4 , and the pressure is transmitted to the sandstones through the fault to form overpressure.

5.2.2.2

Simulation of the Evolution of -Pressure System

The study of hydrocarbon migration and accumulation for reservoir formation requires a numerical simulation method to understand the evolutionary process of the pressure system. To obtain reliable simulation results, it is necessary to use as much as possible measured data to calibrate the simulation process. In this study,

5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation …

319

Fig. 5.10 Calculated pressures in mudstones and measured pressures in sandstones of representative wells in the study area (the thin black broken lines represent the calculated pressures in mudstones, and the red five-pointed stars represent the measured pressures in sandstones)

we simulated the evolution of the pressure system using the measured present pressure data of the drilling oil test and the paleo-pressure obtained from the inclusion homogenization temperature test as constraints. (1) Calibration simulation results with measured present pressure The measured pressure data of approximately 100 wells were used to calibrate the simulation, and the calibration results of two wells are listed in Fig. 5.11. Well N11 is located in the Niuzhuang Subsag. The measured pressure at 3296.2 m was 46.09 MPa. After adjustment of the compaction parameter and porosity–permeability correlation coefficient, the corresponding simulated pressure was 45.98 MPa, which is roughly consistent with the measured pressure. Well W66 is located in the slope zone. The measured sandstone pressure at 1750 m was 18.08 MPa, and the simulated pressure was 17.98 MPa. The difference between the measured pressure and the simulated pressure of these wells was less than 2 MPa. (2) Calibration of simulation results with paleo-pressure The pressures obtained from the homogenization temperature test of the inclusions (Chen, 2007; Liu, 1995; Liu & Gu, 1997) were used to calibrate the simulation results of paleo-pressure evolution. Forty samples were selected for paleopressure analysis of fluid inclusions.

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Fig. 5.11 Calibration of simulated pressure by measured pressure data

Table 5.7 lists some of tested homogenization temperature, paleo-pressure, and salinity data of fluid inclusions. These paleopressure data were used to calibrate the simulation pressure during the formation of inclusions, and the key is to determine the forming ages of fluid inclusions. In this study, the forming ages and capture pressures of the inclusions were determined by comparing the burial depth, paleotemperature of the inclusion samples with the burial evolution curves and temperature evolution curves of the sampling well. Figure 5.12 shows an example of using measured paleo-pressures to calibrate the simulation result of the paleo-pressure evolution in Well N106. The homogenization temperature, capture paleopressure of fluid inclusions in the sandstone sample at 3203.5 m were 113.4 °C, 28.7 MPa, respectively, and those of the other sandstone sample at 3202.5 m were 107.6 °C, 23.3 MPa, respectively. The measured homogenization temperature values of samples at 3203.5 m and 3202.5 m to the burial depthtemperature evolution curves of Well N106, and the formation age of inclusions at 3203.5 m and 3202.5 m were estimated to be 5.6 Ma, 27.64 Ma, respectively. The simulated pressure of strata with present burial depth at 3203.5 m at 5.6 Ma is 30.17 MPa, and the measured paleo-pressure is 28.7 MPa. The simulated pressure is 25.84 MPa for the strata buried at 3202.5 m at 27.64 Ma, and the measured paleopressure is 23.3 MPa. The simulated pressure is roughly consistent with the measured pressure, which indicates that the simulated result is reliable. (3) Evolution of Pressure System

Fig. 5.12 Calibration of simulated paleo-pressure by measured paleo-pressure of fluid inclusions

5.2 Quantitative Study Based on Hydrocarbon Migration and Accumulation …

321

Fig. 5.13 Excess pressure contour map of the Es4 1 in the eastern part of the southern slope of Dongying Sag in different geological periods. a 24.6 Ma, b 6 Ma, c 2 Ma, d 0 Ma

Simulation results of three-dimensional basin model show the distribution and evolutionary characteristics of fluid pressure in mudstone (Figs. 5.13 and 5.14). There were no overpressure inoculation in the strata overlying the Es3 1 in the study area, which were hydrostatic pressure. Therefore, the characteristics of overpressure evolution of the strata underlying the Es3 1 , including the Es3 2 , Es3 3 and Es4 1 , were analyzed. The excess pressure value of the Es4 1 varies from 0 to 16 MPa at the end of the Dongying period (24.6 Ma).The high value of excess pressure is distributed around Niuzhuang and Well W55, and it decreased to the south and east. There is no overpressure in the south and southeast of the study area (Fig. 5.13a). After the abnormal pressure released by the uplift event at the end of the Dongying period, the characteristic of excess pressure at the end of the Guantao period (6 Ma) is similar to that at 24.6 Ma (Fig. 5.13b), which indicates that the overpressure was formed again in the Es4 1 during 24.6 Ma to 6 Ma. The distribution characteristics of process pressure at the end of the Minghuazhen (2 Ma) are consistent with those at 6 Ma, but the excess pressure value increases significantly, and the maximum excess pressure is more than 24 Mpa (Fig. 5.13c). Compared with that at 2 Ma, the overpressure pattern did not change obviously, but the overpressure magnitude increased further. Compared with the process pressure distribution at 2 Ma, the distribution characteristics of excess pressure at present is almost the same, but the excess pressure value is slightly increased (Fig. 5.13d). The characteristics of excess pressure evolution of the Es3 3 is similar to that of the Es4 1 , but the area with overpressure of Es3 3 is smaller than that of the Es4 1 in the same geological period (Fig. 5.14). Similarly, the high value of excess pressure

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Fig. 5.14 Excess pressure contour map of Es3 3 in the eastern part of the southern slope of Dongying Sag in different geological periods. a 24.6 Ma, b 6 Ma, c 2 Ma, d 0 Ma

is distributed around Niuzhuang, Well N50 and Well W55, and the excess pressure gradually decreased from the high value area to the south and southeast, and the hydrostatic pressure is in the south-central and southeast of the study area (Fig. 5.14).

5.2.3 Evolution History of Organic Matter Maturity Based on the numerical simulation of 3D basin geological model, the organic matter maturity (Easy Ro %) of source rocks at main period of hydrocarbon migration and accumulation were obtained. Figure 5.15 shows the evolution of the organic matter maturity of source rocks in the Es4 1 . The organic matter maturity of source rock in the Es4 1 in the central part of Niuzhuang Subsag began to generate hydrocarbons during the period from 36.7 Ma to 32.8 Ma, and the Ro in the Niuzhuang Subsag was generally 0.5%–0.65% at 32.8 Ma (Fig. 5.15a). The area of hydrocarbon-generating source rocks and the organic matter maturity increased during the deposition period of the Dongying Formation (32.8–24.6 Ma). The maturity of source rocks in the Niuzhuang Subsag was generally 0.55%–0.80% at 24.6 Ma (Fig. 5.15b). The area of hydrocarbon generation and the maturity of organic matter increased gradually with the increased of burial depth of source rocks from 24.6 Ma to 2 Ma (Fig. 5.15c, d). The organic matter maturity of source rock of the Es4 1 in the center of Niuzhuang Subsag was up to 1.1% at 2 Ma (Fig. 5.15d). The Ro of the source rocks increased slightly at present compared to that at 2 Ma (Fig. 5.15e).

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323

Fig. 5.15 Organic matter maturity (Easy Ro %) contour map of Es4 1 in the study area. a 32.8 Ma, b 24.6 Ma, c 6 Ma, d 2 Ma, e 0 Ma

The organic matter maturity contour map of source rock in the Es3 3 at different periods were shown in Fig. 5.16. Most of the source rocks of the Es3 3 in the study area have not started to generate hydrocarbon before 32.8 Ma, and the maturity has not exceeded the threshold of hydrocarbon generation, and the Ro of most organic matter is below 0.55%. The organic matter maturity was increased with the increasing burial depth of source rocks from 32.8 Ma to 24.6 Ma, and the source rocks in Niuzhuang Subsag began to generate hydrocarbon (Fig. 5.16a). The Ro in the center of Niuzhuang Subsag was up to 0.7% at 24.6 Ma (Fig. 5.16a). The Ro of source rock of Es3 3 in the Niuzhuang Subsag was generally 0.6%–0.8% at 24.6 Ma (Fig. 5.15b), and those at 2.0 Ma, 0 Ma increased to 0.7%–0.95%, 0.7%–1.1%, respectively (Fig. 5.15c, d).

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Fig. 5.16 Organic matter maturity (Easy Ro %) contour map of Es3 3 in the study area. a 24.6 Ma, b 6 Ma, c 2 Ma, d 0 Ma

5.2.4 The Hydrocarbon Expulsion History of the Source Rocks During the main hydrocarbon reservoir-forming period, the amount of total available hydrocarbon for migration played an important role in the direction of hydrocarbon migration, the location and amount of hydrocarbon accumulation. The amount of available hydrocarbon for migration mainly depended on the total amount of hydrocarbons expelled from source rocks during the hydrocarbon reservoir-forming period. Therefore, on the basis of previous research results on the hydrocarbon expulsion model and geological and geochemical parameters of source rocks in the Dongying Sag (Li et al., 2003a, 2003b; Pan et al., 2000), combined with the above simulation results of organic matter maturity, the hydrocarbon expulsion amount of the main source rocks in the study area in different geological periods was estimated by the hydrocarbon generation potential method based on the principle of material balance.

5.2.4.1

Hydrocarbon Expulsion Model of the Source Rocks

The hydrocarbon generation potential index—(S 1 + S 2 )/TOC variation trend with depth is usually used to determine the threshold depth or temperature of hydrocarbon expulsion and the hydrocarbon expulsion ratio of the source rock with different maturity. (Jiang et al., 2007; Li et al., 2003a, 2003b; Zhou & Pang, 2002). Due to the difference in the hydrocarbon generation potential of source rocks with different

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organic matter abundances and kerogen types, we established hydrocarbon expulsion models of Type I and Type II1 organic matter in dark mudstone and Type II1 organic matter in oil shale in the study area based on the TOC and pyrolysis data (Fig. 5.17). The threshold depth of hydrocarbon expulsion of Type I and Type II1 organic matter in dark mudstone in the study area were approximately 2600 m, 2700 m respectively (Fig. 5.17a, b), and that of the Type II1 organic matter in oil shale was 2200 m (Fig. 5.17c). Once the burial depth of source rock exceeds the threshold depth of hydrocarbon expulsion, the generated hydrocarbons begin to be expelled from the source rock, and the hydrocarbon expulsion ratio increases with the increasing organic matter maturity (Fig. 5.17). The hydrocarbon expulsion model can be used to estimate the amount of the hydrocarbon expelled from the source rocks in the study area.

5.2.4.2

Hydrocarbon Expulsion History of the Source Rocks

The hydrocarbon expulsion amount and intensity of source rocks during main hydrocarbon migration and accumulation periods were calculated by Formula (4.3), combined with the thickness, area, density, TOC, maturity, kerogen type of source rocks and the hydrocarbon expulsion model in the study area. The hydrocarbon expulsion intensity contour map of source rocks during main hydrocarbon migration and accumulation periods were obtained, which listed in Figs. 5.18 and 5.19. The source rocks of the Es4 1 began to expel hydrocarbons at 32.8 Ma, but the hydrocarbon expulsion area was only limited to the center of the Niuzhuang Subsag, and the highest hydrocarbon expulsion intensity was only 30 × 104 t/km2 and the hydrocarbon expulsion amount was only 0.17 × 108 t. There was no hydrocarbon expelled from the source rocks in the Es3 3 before 32.8 Ma. At the end of the Dongying period (24.6 Ma), the hydrocarbon expulsion area of source rocks in the Es4 1 was expanded, and the hydrocarbon expulsion intensity near the center of the Niuzhuang Subsag increased to 180 × 104 t/km2 (Fig. 5.18a), with a hydrocarbon expulsion amount of 0.75 × 108 t. The source rocks of the Es3 3 began to expel hydrocarbons during the deposition of Dongying Formation (32.8 Ma to 24.6 Ma), and the hydrocarbon expulsion intensity near the center of the Niuzhuang Subsag increased to 80 × 104 t/km2 at 24.6 Ma (Fig. 5.19a) and the hydrocarbon expulsion amount was 0.33 × 108 t. From 24.6 Ma to the present, the hydrocarbon expulsion intensity of the source rocks gradually increases with the gradual increase of organic matter maturity, and the hydrocarbon expulsion intensity of the source rock in Es4 1 in Niuzhuang Depression at 6 Ma, 2 Ma, and 0 Ma were 50 × 104 –400 × 104 t/km2 , 100 × 104 –550 × 104 t/km2 , 150 × 104 –600 × 104 t/km2 , respectively (Fig. 5.18b–d), and those of the source rock in Es3 3 were 60 × 104 –220 × 104 t/km2 , 60 × 104 –450 × 104 t/km2 , 100 × 104 –550 × 104 t/km2 , respectively (Fig. 5.19b–d). From 24.6 Ma to 6 Ma, the hydrocarbon expulsion amount of source rocks in the Es4 1 and Es3 3 were 1.88 × 108 t, 1.09 × 108 t, respectively, and from 6 to 2 Ma, the hydrocarbon expulsion

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a. Type I organic matter in dark mudstone

b. Type Ⅱ1 organic matter in dark mudstone

c. Type Ⅱ1 organic matter in shale

Fig. 5.17 Hydrocarbon expulsion model of different types of organic matters in Dongying Sag (TOC and pyrolysis data from Li et al., 2003a, 2003b)

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Fig. 5.18 Hydrocarbon expulsion intensity of source rocks in Es4 1 in the eastern part of the southern slope of Dongying Sag in different periods. a 24.6 Ma, b 6 Ma, c 2 Ma, d 0 Ma

Fig. 5.19 Hydrocarbon expulsion intensity of source rocks in Es3 3 in the eastern part of the southern slope of Dongying Sag in different periods. a 24.6 Ma, b 6 Ma, c 2 Ma, d 0 Ma

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amount of source rocks in the Es4 1 and Es3 3 were 2.93 × 108 t, 2.56 × 108 t, respectively. The hydrocarbon expulsion amount of source rocks in the Es4 1 and Es3 3 were 1.14 × 108 t, 1.82 × 108 t, respectively from 2 Ma to present. The cumulative hydrocarbon expulsion amount of the source rock in Es4 1 was the highest, up to 6.88 × 108 t, and those of the source rocks in Es3 3 , Es3 2 were 5.53 × 108 t, 4.18 × 108 t, respectively. The cumulative hydrocarbon expulsion amount of source rocks in the study area was approximately 16.59 × 108 t, which was consistent with the calculation results of the third resource evaluation of the Shengli Oilfield.

5.3 Hydrocarbon Migration and Accumulation Period and Petroleum Migration-Accumulation Systems The periods of hydrocarbon migration and accumulation in the study area were determined by using the chronological analysis method. According to the fluid potential diagrams obtained by 3D basin modelling, the petroleum migrationaccumulation systems in different periods were determined, that is, the petroleum migration-accumulation systems may be separated by the troughs on a potential diagram.

5.3.1 Chronological Analysis of Hydrocarbon Migration and Accumulation Periods Many studies have been carried out to determine the hydrocarbon migration and accumulation periods in the Dongying Subsag (Chen, 2007; Jiang et al., 2003; Qiu et al., 2000; Zhu et al., 2004, 2007). The results showed that there were two periods of hydrocarbon migration and accumulation, including the first period at the end of the Dongying period and the second period during the end of the Guantao period to-Quaternary. According to the measured homogenization temperature of inclusions in sandstone reservoirs, the evolution of hydrocarbon expulsion of source rocks, and referring to previous research results, the hydrocarbon migration and accumulation periods in Dongying Subsag were determined in this study.

5.3.1.1

Hydrocarbon Migration and Accumulation Periods Determined by Hydrocarbon Expulsion History

It is a traditional method to estimate the periods and the corresponding ages of hydrocarbon migration and accumulation based on the history of hydrocarbon generation and expulsion of source rocks. The generation and expulsion of a certain amount

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of hydrocarbons discharged from source rocks is a prerequisite for the formation of hydrocarbon reservoirs. The formation of hydrocarbon reservoirs must occur after the hydrocarbons expulsion from source rocks, which is the earliest time limit of the reservoir formation. According to the aforementioned study on the evolutionary history of Ro and hydrocarbon expulsion in source rocks (Fig. 5.20), the main source rocks in the study area experienced two peak periods of hydrocarbon generation and expulsion, corresponding to the Dongying period and Minghuazhen period, respectively. The source rocks in the Es4 1 upper submember of the fourth member of the Shahejie Formation and the Es3 3 lower submember began to expel hydrocarbons at the end of the sedimentary period of the first member of the Shahejie Formation and reached the first peak of hydrocarbon expulsion at the end of the Dongying period. However, due to the relatively small scale of hydrocarbon expulsion and its confinement in the deep Niuzhuang Subsag, the early hydrocarbon migration and accumulation may have been mainly concentrated in the source strata and the areas adjacent to the Niuzhuang Subsag. Since then, influenced by the regional uplift of the Dongying tectonic movement, hydrocarbon reservoir formation tended to stop due to the stagnation of hydrocarbon generation and expulsion from source rocks. From the sedimentary period of the Guantao Formation, the source rocks were deeply buried. With the increase in organic matter maturity, the source rocks generated and expelled hydrocarbons again, but the sedimentary thickness of the Guantao Formation was not large, and the hydrocarbon expulsion range and hydrocarbon scale provided by the source rocks were limited. During the sedimentary period of the Minghuazhen Formation, the source rocks in the Es4 1 upper and the Es3 3 lower submember were buried rapidly, and with the expansion of the maturity and scope of the source rocks, all source rocks reached the peak of hydrocarbon generation and expulsion, providing conditions for large-scale migration and accumulation. After the Quaternary, the amount of hydrocarbon expulsion from the source rocks decreased. According to the hydrocarbon expulsion history of the source rocks, there were mainly two periods of hydrocarbon reservoir formation in the study area. The first

Fig. 5.20 Histograms of hydrocarbon expulsion amount of source rocks in the Es4 1 and Es3 3 in eastern part of the southern slope of Dongying Sag in different periods

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period of reservoir formation at the end of the Dongying period was small in scale, and the second period of reservoir formation started from the end of the Guantao period and lasted for a long time and included multiple stages of continuous charging. Many reservoirs may have been the result of multiple stages of hydrocarbon charging, and the Minghuazhen period was the most important hydrocarbon reservoir-forming period.

5.3.1.2

Determination of Hydrocarbon Reservoir-Forming Periods by the Fluid Inclusion Method

In this study, 48 oil-bearing sandstone samples from the Shahejie Formation were selected, and microscopic observations, microbeam fluorescent spectrum analysis and microthermometry of fluid inclusions were carried out. Combined with the tectonic–thermal evolutionary and hydrocarbon generation history simulation results, the episodes and corresponding ages of hydrocarbon charging were studied. Fluid inclusion observations were completed in the Key Laboratory of Petroleum Resource Research, Chinese Academy of Sciences. The fluorescent identification instrument for organic inclusions was a Nikon 80I dual-channel microscope, and the microbeam fluorescent spectrum was measured by a French IHR320 Core 3 instrument. The inclusion microthermometer was a Linkam THMS 600G heating and freezing microscope equipped with a Leica DMLP polarizing microscope. During temperature measurement, the initial heating rate was 10 °C/min, and the change in inclusions was observed after every 20 °C rise. When the inclusion bubble decreased to a homogeneous state, the heating rate was adjusted to 2 °C/min. (1) Petrographic characteristics of fluid inclusions Through observations of the phase state and fluid composition characteristics of fluid inclusions under the microscope, the inclusions captured in the diagenetic minerals in the Shahejie Formation reservoir in the study area were mainly brine inclusions, oil-phase inclusions, gas–liquid hydrocarbon inclusions, hydrocarbon-bearing brine inclusions and bitumen inclusions, while the abundance of other types of inclusions was low. The host diagenetic minerals with fluid inclusions were mainly quartz and feldspar particles and carbonate cements. Organic hydrocarbon inclusions were mostly in the form of beads in quartz granule fractures, quartz overgrowths and calcite cements (Table 5.8). According to the diagenetic characteristics of the host minerals with inclusions and the cutting relationship with fractures, the diagenetic sequence was determined as follows: quartz overgrowth–cutting across quartz/feldspar intragranular fracture–feldspar overgrowth–calcite cementation. Fluid inclusions had various shapes, including elliptical, triangular, strip and irregular shapes. The gas–liquid ratio of organic inclusions was generally 8–25% for small individuals, and generally 1– 7 μm in diameter. Most of them were between 1 and 4 μm, with great differences in morphology. In single polarized light, the color of oil inclusions was mostly brown, light brown, sepia or colorless. The gas-phase inclusions were colorless or gray or

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brown and slightly darker than the liquid phase in single polarized light, and the gas/liquid ratio was larger than that of the saline inclusions in the same period, while the pure gas-phase inclusions were black or dark gray. The gas–liquid ratio of brine inclusions was generally 8–15%, and its gas phase and liquid phase were colorless under single polarization. Oil inclusions with yellow, yellowish-green, blue, and blueish-white fluorescence; gas inclusions with weak white fluorescence; and inclusions containing dissolved gas and brine were mainly detected under a fluorescent microscope (Fig. 5.21). Different Table 5.7 Homogenization temperatures, Salinity, and Paleo-pressure of fluid inclusions in the south slope of Dongying Sag Well

Depth (m)

Stratum

Homogenization temperatures (°C)

Salinity (%)

Paleo pressure (MPa)

Wang 126

3018.3

Es4

101.9

6.16

20.2

Wang 46

3392.6

Ek

111.2

22.17

26.6

Niu 103

3296.5

Es3 3

105.7

6.94

19.9

Niu 103

3297

Es3 3

114.7

5.66

32

Niu 105

3251

Es3 3

108.1

8.53

23

3

Niu 105

3254

Es3

115.9

9.64

36.5

Niu 106

3202.5

Es3 2

107.6

5.8

23.3

Niu 106

3203.5

Es3 2

13.4

12.66

28.7

2

109.5

15.66

24.3

133.1

11.66

38.2

Niu 107

3273

Es3

Niu 107

3273

Es3 2

Niu 876

3373.2

Es3 3

103.2

21.34

Niu 876

3397.5

Es3 3

122.5

5.49

34.9

Niu 5

2592

Es4

90.1

0.35

15.97

2

24

Niu 104

3042.5

Es3

104.1

8.18

26.6

Shi 11

3133.5

Es3 2

101.5

2.74

17.8

Shi 11

3142.3

Es3 2

134.2

1.05

40.94

2

Shi 128

3085.5

Es3

107.4

3.24

21.6

Wang 126

3002.4

Es4

114.7

1.05

34.79

Shi 123

2691.8

Es3 2

114.4

3.86

30.92

Guan 113

2491.3

Es4

110.9

4.03

30

Niu 11

3168.5

Es3 2

118

2.96

35.29

Niu 5

2595

Es4

116.1

19.29

31.33

2

Guan 116

3014.4

Es3

126.1

4.34

36.81

Shi 10

3038

Es3 2

106.3

6.93

22.2

Shi 10

3094.2

Es3 2

127.9

1.4

37.3

Shi 130

3045.6

Es3

2

Wang 63

2847.5

Es3 2

129.5

6.77

36.5

12.2

2.8

36.6

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fluorescent colors indicate the differences in the composition and thermal evolution of hydrocarbon inclusions. Usually, with the increase in organic matter maturity, the fluorescent color changes from fiery red to orange, yellow, green, blueish white or colorless (Burrus et al., 1991). Hydrocarbon inclusions with multiple fluorescent colors may indicate multiple periods of hydrocarbon charging. However, visual observations of the fluorescent color of inclusions can only provide qualitative results, and it is easily affected by factors such as lamina thickness, rock fabric and cement type. By detecting the fluorescent spectrum characteristics of organic inclusions, it is possible to distinguish the compositional differences between inclusions, which can be used as a reference for analyzing hydrocarbon-charging periods. Therefore, we used the microbeam fluorescent spectrum analyzer to measure the microscopic fluorescent spectrum of a single oil inclusion. Figure 5.21 shows hydrocarbon-bearing inclusions with yellow and blue fluorescent colors and their fluorescent spectra. The main peak wavelength of yellow fluorescence was distributed between 505 nm and 509 nm, and the main peak of blueish-white fluorescent hydrocarbon inclusions was significantly more to the left than the yellow fluorescence, generally between 501 nm and 503 nm. Due to the mixing of different hydrocarbon fluids and the different evolutionary maturities of the same fluid, the fluorescent color

Fig. 5.21 Fluorescence color and fluorescence spectrum of organic inclusions

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often showed transitional colors such as yellowish green and blueish green; therefore, the change in the main peak position of the fluorescent spectrum often showed continuity. The characteristics of the fluorescent spectrum indicate that there may have been two periods of main hydrocarbon charging in the study area. (2) Homogenization temperature of inclusions and hydrocarbon-charging periods The homogenization temperature of fluid inclusions represents the lowest temperature when inclusions were formed, and the homogenization temperature of brine inclusions formed at the same time as hydrocarbon inclusions is generally considered to represent the temperature when hydrocarbons entered the reservoir (Liu, 1995; Liu & Gu, 1997). According to the temperature when hydrocarbons entered the reservoir, combined with the paleogene temperature and burial history of the basin, the time of hydrocarbon migration and accumulation for reservoir formation can be determined. Based on the fluorescent observations of organic inclusions, we carried out microthermometry on 105 brine inclusion measuring points in the same period as that of organic inclusions. By eliminating some inaccurate factors of homogenization temperature caused by possible leakage and referencing the petrographic characteristics of host minerals with hydrocarbon inclusions, we divided the hydrocarbon-charging episodes (Table 5.7). The results show (Fig. 5.22) that there were three periods of brine inclusions coexisting with hydrocarbons in the Shahejie Formation reservoir in the eastern part of the southern slope of the Dongying Sag, and brine inclusions from different periods were detected in the samples of each well section, reflecting the heterogeneity of fluid activity. The homogenization temperature range of brine inclusions in the first period was 75–1100 °C, and the peak value was 90–995 °C, which mainly coexisted with pale-yellow and yellow fluorescent oil inclusions, representing early immature oil charging. In the second period, the homogenization temperature of brine inclusions was between 100 °C and 115 °C, and the peak value was between 105 °C and 110 °C, which mainly coexisted with yellow and yellowish-green fluorescent oil inclusions. A small amount of blueish-white fluorescent oil inclusions were found, which may have undergone pyrolysis in the later period, representing the charging of moderately mature oil. In the third period, the homogenization temperature of brine inclusions was 110–1150 °C, and the peak value was 115–1130 °C, which mainly coexisted with blue and blueish-white fluorescent oil inclusions, representing the charging of mature oil. (3) Determination of hydrocarbon reservoir formation periods The homogeneous temperature range of brine inclusions coexisting with oil inclusions in each period was projected on a burial history–thermal evolutionary history map of a single well; that is, the charging ages obtained from samples of each well were unified to the same time axis (Chen, 2007), thus eliminating the influence of sample depth differences and determining the periods and corresponding ages of hydrocarbon reservoir formation (Fig. 5.23).

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Fig. 5.22 Histograms of homogenization temperature of hydrocarbon inclusions and brine inclusions at the same period

Fig. 5.23 Hydrocarbon accumulation periods and corresponding ages of Shahejie formation in the eastern part of South slope of Dongying Sag (Chen, 2007)

Figure 5.23 shows the hydrocarbon reservoir-forming periods evaluations obtained by marking the charging ages on the single well burial history–thermal evolutionary history map. As shown in the figure, there were three periods of hydrocarbon charging in the Shahejie Formation in the study area. The first period of reservoir formation was 34–24 Ma, which corresponded to hydrocarbon generation and expulsion from the source rocks in the upper submember of the Es4 1 and the Es3 3 at the end of the Dongying period (end of Ed). The second period of reservoir formation was 13.8–8 Ma, which mainly corresponded to large-scale hydrocarbon generation and expulsion at the end of the Guantao period (end of Ng). The third

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period of reservoir formation was 8–0 Ma, which was mainly in the Minghuazhen period (Nm). The reservoir formation in this period corresponded to the most important peak period of hydrocarbon generation and expulsion in the study area. In fact, the second and third periods were continuous hydrocarbon-charging processes, which can be classified as one period of reservoir formation; therefore, we can consider that two periods of hydrocarbon reservoir formation mainly occurred in the study area.

5.3.2 Determination of Hydrocarbon Migration and Accumulation Systems Hydrocarbon migration and accumulation systems, include effective hydrocarbon sources, carriers, reservoirs, caprocks, traps and other reservoir-forming elements necessary in the process of hydrocarbon migration and accumulation and are the smallest independent unit in one period of hydrocarbon migration and accumulation (Luo, 2008). According to area of effective hydrocarbon sources and the fluid potential field characteristics of carrier beds and systems in different periods, the division of hydrocarbon migration and accumulation systems should be comprehensively considered regarding the organic geochemical characteristics and the understanding of the distribution characteristics of discovered hydrocarbon reservoirs and should be based on the characteristics of fluid potential dynamic separation and stratigraphic lithological separation. A basin or hydrocarbon-bearing system can be divided into several hydrocarbon migration and accumulation systems. Different migration and accumulation systems are separated by “separation troughs”. Hydrocarbons only migrate and accumulate in one system and do not enter other migration and accumulation systems. Depending on basin evolution and sedimentary-filling processes, the distribution range of migration and accumulation systems in different periods may be different. Based on the abovementioned basin burial evolutionary history and paleofluid pressure recovery, we used Temis 3D basin simulation software to calculate the fluid potential of the main carrier beds and systems in the key reservoir-forming period and plotted the streamline as a reference for hydrocarbon migration direction analysis. If the heterogeneity of the carrier beds is not considered, the streamline represents the direction with the largest fluid potential gradient, and the streamline convergence area is often the target area of hydrocarbon accumulation. Figure 5.24 shows the fluid potential and streamlines of the top surface of the carrier bed in the Es3 1 and the Es2 second in the eastern part of the southern slope of the Dongying Sag at the end of the Dongying period. As shown in the figure, the fluid potential on the top surface of the main carrier bed in this period was similar, and the fluid isopotential line was basically parallel to the tectonic contour. The fluid potential was generally high in the northwest and low in the southeast. The highpotential area corresponded to the sedimentary center, and the high-potential center was located in the Niuzhuang Subsag, from which the fluid potential decreased in the

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Fig. 5.24 Map showing the fluid potential and division of migration and accumulation systems on the top surface of the carrier beds at the end of the Dongying period. a Es3 1 , b Es2

slope area to the south and southeast. Depending on the distribution of fluid potential, the streamline pointed from the northern sag area to the southern slope area, and the paleotectonic ridge was the place where the streamlines were concentrated. At the end of the Minghuazhen period, the characteristics of the fluid potential field on the top surface of the carrier bed in the Es3 1 and the Es2 in the study area were very similar to those at the end of the Dongying period (Fig. 5.25), and the evolution and distribution of fluid potential showed inheritance development. The fluid potential of the main carrier bed decreased from the Niuzhuang Subsag to the southern and southeastern slope areas, and the fluid potential gradient was the largest at the ridges of the Chenguanzhuang, Wangjiagang and Bamianhe tectonic zones, which were places where migration streamlines were concentrated. According to the area of effective source rocks and the characteristics of the fluid potential field of carrier beds and systems in the main reservoir-forming periods and referencing the distribution of discovered hydrocarbon reservoirs and the understanding of organic geochemical characteristics, we divided the hydrocarbon migration and accumulation systems and determined the composition and scope of hydrocarbon migration and accumulation systems. At the end of the Dongying period, the source rocks in the Es4 1 and the Es3 3 reached the first hydrocarbon expulsion peak,

Fig. 5.25 Map showing the division of fluid potential and migration and accumulation systems on the top surface of the carrier beds at the end of Minghuazhen period. a Es3 1 , b Es2

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and the hydrocarbon expulsion center was in the northern deep sag area. The sand bodies in the Es4 1 upper, Es3 2 , the Es3 1 and the Es2 were the main reservoirs or carrier beds. The thick mudstone in the Es1 can act as a regional caprock, while the mudstones in the Es4 , Es3 third member and Es2 constitute direct caprocks. At the same time, a series of fault-nose tectonic traps and lithological traps formed in the Paleogene strong rifting stage. During this period, hydrocarbons mainly migrated and accumulated in steps from the high-potential area to the low-potential area in the south along the composite conduit framework composed of sandstone carrier beds and faults. The source rocks in the Es4 1 mainly acted as effective oil sources, while the source rocks in the Es4 1 and Es3 3 mainly acted as oil sources for the Es3 2 and the Es2 . According to the distribution of fluid potential and streamlines, the Es4 1 upper and the Es3 2 in the study area can be divided into two hydrocarbon migration and accumulation systems: the southern and northern slopes of the Niuzhuang Subsag, most of which are in the southern slope migration and accumulation system. There is only one hydrocarbon migration and accumulation system in the Es2 and the Es3 1 (Fig. 5.26). Because the fluid potential characteristics of each carrier bed and system in the Guantao period and the end of the Minghuazhen period were very similar, the division of hydrocarbon migration and accumulation systems involved the end of the Minghuazhen period. The recovery of hydrocarbon generation and expulsion history showed the following findings: The source rocks in Es4 1 , Es3 2 and Es3 3 underwent hydrocarbon generation stagnation during the Dongying tectonic movement, then generated and expelled hydrocarbons on a large scale again and reached the peak of hydrocarbon expulsion at the end of the Minghuazhen period. Hydrocarbons mainly utilized the connected sand bodies and active faults as the composite carrier systems

Fig. 5.26 Division of carrier bed of Shahejie formation in the eastern part of southern slope of Dongying Sag

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Fig. 5.27 Contour maps showing sandstone percentage of Shahejie formation in the eastern part of southern slope of Dongying Sag. a Es3 1 , b Es2

in Es4 1 , Es3 2 , Es3 1 , and Es2 and migrated and accumulated in the southern slope zone. The source rocks in Es4 1 still acted as an oil source, the dark mudstones in the Es3 2 middle and Es3 3 mainly acted as oil sources for the Es3 2 , and the source rocks for Es4 1 or Es3 2 and Es3 3 generated crude oil for mixed charging of the Es3 1 and Es2 . According to the distribution characteristics of fluid potential and streamlines (Fig. 5.27), the Es4 1 and Es2 can be divided into two hydrocarbon migration and accumulation systems on the southern and northern slopes of the Niuzhuang Subsag. The distribution of migration and accumulation systems was similar to that at the end of the Dongying period. The separation trough was located in the line of the W78–N24–N25–N111–S10 wells in the Niuzhuang Subsag.

5.4 Establishment and Quantitative Characterization of the Carrier System The southern slope of the Dongying Sag has a hydrocarbon carrier system of a typical terrestrial fault basin, controlled by regional tectonic evolutionary and sedimentary systems. Various types of sandstone carrier beds were formed in different areas in different periods. At the same time, tensile or extensional-shear syndepositional growth faults and fault–fissure systems were widely developed. These faults and connected sand bodies crossed and overlapped each other in the vertical and horizontal directions, forming complex three-dimensional hydrocarbon migration carrier system. Based on the quantitative evaluation method of the hydrocarbon carrier system proposed earlier, the connectivity of the sandstone carrier bed and fault was quantitatively studied, and the carrier system of the eastern part of the southern slope of the Dongying Sag during the key reservoir-forming period was established to evaluate its control on hydrocarbon migration.

5.4 Establishment and Quantitative Characterization of the Carrier System

339

5.4.1 Quantitative Characterization of the Sandstone Carrier Bed The Tertiary sediments on the southern slope of the Dongying Sag were developed against the background of an inherited gentle slope. Under the control of provenances in the southeast and east, many sets of sand bodies with different sedimentary facies were developed in the Shahejie Formation. Due to the wide and gentle tectonics, these sedimentary sand bodies are large in scale, wide in distribution and superimposed on each other longitudinally, forming a good lateral migration carrier bed of hydrocarbons. According to the hydrocarbon exploration degree, data acquisition, oil-bearing strata distribution and source–reservoir–caprock assemblage in the study area, the Shahejie Formation is divided into 4 main carrier beds: the upper submember of the fourth member of the Shahejie Formation (Es4 1 ), the middle submember of the third member of the Shahejie Formation (Es3 2 ), the upper submember of the third member of the Shahejie Formation (Es3 1 ) and the second member of the Shahejie Formation (Es2 ). To quantitatively evaluate the carrying characteristics of the sandstone carrier bed, based on previous research results on sedimentary systems and through the statistics of a large number of acoustic-logging and mud-logging datasets, we studied the geometric connectivity characteristics of sand bodies in space, carried out research on the diagenetic evolutionary process and the relationship with hydrocarbon charging, and quantitatively characterized the carrying capability of the sandstone carrier bed during the main reservoir-forming period combined with the measured physical property data of the reservoir.

5.4.1.1

Division of Carrier Beds in the Study Area

The concept of the carrier bed and the method of establishing the carrier bed model are introduced in the third chapter. The first step is to divide the carrier beds according to the understanding of sedimentary strata and hydrocarbon geological conditions in the study area and considering the data preparation. There are abundant drilling data and clear stratigraphic research in the study area, which provides a good foundation for the division of carrier beds. According to the analysis of drilling data, during the sedimentary period of the Es4 1 and the Es3 3 , the southern slope of the Dongying Sag was in the initial expansion stage of the lake basin, which generally deposited a set of shallow lake shore-semideep lacustrine deposits. The sand bodies were developed with poor connectivity, which makes it difficult for them to act as long-distance carrier beds for the lateral migration of hydrocarbons in the study area (Li et al., 2003a, 2003b; Li, 2003a, 2003b; Zhu & Jin, 2003). The Es3 2 was generally dominated by lacustrine-semideep lacustrine dark mudstone in the Niuzhuang Subsag, and the sand bodies mostly formed lenticular shapes, extending not far laterally but having good vertical connectivity. The slope edge gradually changed into delta front sand bodies, and its connectivity improved;

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therefore, the sand bodies could act as carrier beds. The Es3 1 consists of a set of deposits formed as shallow water bodies with a sufficient provenance supply, high sand body development degree and good connectivity, forming a set of important hydrocarbon carrier beds (Liu, 2005; Li et al., 2007). The Es2 consists of mainly fluvial–delta facies deposits, and thick delta front facies sand bodies were widely developed. The sandstone percentage was generally higher than 35%; therefore, the sand bodies could act as a good hydrocarbon migration carrier bed. The Es1 covering the Es2 was mainly a set of shallow lake-semideep lake mudstone deposits with a widespread distribution and a thickness of 40–450 m, acting as a set of regional caprocks. Therefore, the Shahejie Formation on the southern slope of the Niuzhuang Subsag can be divided into three sets of carrier beds (Fig. 5.26): the Es3 2 with the mudstone at the bottom of the Es3 2 or Es3 1 as the caprock, the Es3 1 with the argillaceous stratum at the bottom of the Es2 as the caprock (Wang et al., 2009) and the Es2 with the shale of the Es1 as the caprock. Based on the division of carrier beds, according to the method of establishing the carrier bed model introduced in the third chapter, the carrier bed in Es2 , Es3 1 , and Es3 2 were divided into 1000 × 1000 grids (Fig. 3.3), and then the carrier bed model of each one was established.

5.4.1.2

Geometric Connectivity of the Sand Bodies

The geometric connectivity of the sandstone carrier bed in space is a necessary condition for acting as a hydrocarbon migration conduit. Generally, under similar geological conditions, the higher the development degree of sandstone is, the better the geometric connectivity, and the greater the probability of acting as a hydrocarbon migration conduit. According to the probability model of sandstone connectivity of the carrier bed proposed in Chap. 3, the characteristics and geometric connectivity of the sandstone carrier bed were depicted based on the drilling sandstone thickness and sandstone percentage data. (1) Characteristics of the sandstone percentage in the sandstone carrier bed The sandstone thickness and sandstone percentage contour maps of the main sandstone carrier beds in the study area were obtained based on the logging data interpretation and wave impedance inversion results (Fig. 5.27), referring to the distribution characteristics of sedimentary system. The distributary channel, channel-mouth bars, sheet-like sand bodies and slump turbidite sand bodies around the multiperiod delta front of the Es3 1 were vertically superimposed and connected with each other. The percentage of sandstone was generally high (Fig. 5.27a), generally greater than 45%. The thickness of fluvial–delta facies sand bodies in the Es2 was large, and the percentage of sandstone in the second member of the Shahejie Formation was high (Fig. 5.27b), generally greater than 35%. The areas with high sandstone percentages were mainly distributed in Caoqiao and Chenguanzhuang in the southwest, which

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Fig. 5.28 Geometric connectivity probability of sandstone carrier bed in the Es3 1 in the eastern part of the southern slope of Dongying Sag

was generally more than 50%, and the sandstone percentage gradually decreased in the northern and northeastern directions. (2) Geometric Connectivity of the Sandstone Carrier Bed The Gaussian fitting mathematical relationship model shown in Formula 3.1 was used to describe the connectivity between sandstone bodies in the carrier bed. Figure 5.28 shows the geometric connectivity of the sandstone carrier bed in the upper submember of the third member of the Shahejie Formation. The green dots in Fig. 5.28 indicate the connectivity of the sand bodies. The denser the green dots are, the greater the geometric connectivity probability between sand bodies. The white area represents the mudstone or isolated sand body distribution area with poor connectivity. As shown in Fig. 5.28, the geometric connectivity probability of most areas of the carrier bed in the Es3 1 was above 80%, indicating that most sand bodies were interconnected, which was mainly due to the extensive development of delta plain distributary channels, delta front underwater distributary channels, channel mouth bars and sheet-like sand bodies in the Es3 1 . These sand bodies were vertically and laterally superimposed and connected with each other with good connectivity, and the connectivity of sand bodies was only poor in some areas.

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5.4.1.3

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Quantitative Characterization of the Fluid Connectivity of the Sandstone Carrier Bed

Based on an analysis of sedimentary facies types and geometric connectivity characteristics of sandstone carrier beds, according to quantitative research ideas and methods of fluid connectivity in carrier beds described in Chap. 3, taking the Wangjiagang Oilfield as a typical anatomical area, hydrodynamic connectivity of sandstone carrier beds were determined by various means, such as interwell sand body correlation, acoustic-logging constrained-wave impedance inversion, production dynamic data analysis and geochemical tracing, statistically analyzing the relationship between sand body connectivity and sandstone percentage, establishing a mathematical relationship model between sandstone connectivity and the sandstone percentage suitable for this area, and quantitatively studying the connectivity of the main carrier bed in the Shahejie Formation in the study area. (1) Gridding of the carrier beds The length and width of the plane grids of the sandstone carrier bed were determined according to the average length and width of the sand bodies. According to the statistics of sand body length and width of the Es2 and the ES3 1 in the T61 block of the Wangjiagang Oilfield, the sand body length was mostly 400–1200 m, and the peak value was 600–800 m. The width was mostly 200–600 m. Considering the results of hydrocarbon exploration and development in the T61 block, on the premise of ensuring that data wells were distributed in different carrier bed grids as much as possible, the longitudinal (x-axis) grid length took half of the lens length in the source direction, the transverse (i.e., y-axis) grid width took half of the lenticle width, and the depth direction (z-axis) took the top and bottom boundaries of each sand layer group in the Es2 and the Es3 1 upper as separation units and divided the T61 block into 350 m × 200 m × 9 three-dimensional grids (Fig. 5.29) as the basic units for sand body connectivity analysis. (2) Identification of fluid connectivity of interwell sand bodies Oilfield dynamic/static production data are parameters that directly reflect the characteristics of reservoir development. Analysis of production dynamic/static data is an effective means to study interwell sand body/reservoir connectivity. Common methods include reservoir pressure analysis, fluid property trend analysis, well test analysis, interwell productivity analysis, interwell chemical tracer monitoring, crude oil total hydrocarbon gas chromatography fingerprint analysis, etc. (Deng et al., 2003; Wei & Kang, 2005; Shi et al., 2006). These methods were combined in this study to identify the connectivity of interwell sand bodies in the anatomical area of the T61 block in the Wangjiagang Oilfield. ➀ Fluid pressure analysis for inter-well hydrodynamic connectivity: Each well in the same reservoir belongs to the same hydrodynamic system, the original formation pressure everywhere is balanced, the pressure at the same depth should be equal, and the correlation curve between the original formation pressure and

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343

Top of sha 2-1 Top of sha 2-2 Top of sha 2-3 Top of sha 2-4

Fig. 5.29 Schematic diagram of grid division for sandstone connectivity analysis of T61 block in Wangjiagang oilfield

depth should be an approximate straight line. In the production process, the pressure trend over time of each well should be generally similar (Deng et al., 2003; Wei & Kang, 2005). According to this characteristic, the interwell connectivity of sand bodies can be analyzed by using fluid pressure data. For example, the trends over time of reservoir pressure in the Es2 7 –Es2 8 sublayers in Wells T61C162, T61-XC135, and T61-X184 in the T61 block were basically the same from October 2007 to February 2010 (Fig. 5.30a). Accordingly, it can be assumed that the sublayer sand bodies in Es2 7 –Es2 8 in Wells T61-C162, T61-XC135, and T61-X184 are connected. ➁ Production trend analysis for inter-well hydrodynamic connectivity: With the development of reservoirs, the overall trends over time of oil production, water production and water cuts of each well in the same pressure system should be approximately consistent (Deng et al., 2003; Wei & Kang, 2005). According to this characteristic, the connectivity of interwell sand bodies can also be analyzed by using the productivity data of each well. For example, the trends over time of liquid production of the Es3 7 sand layer group in the Well T61-48 and Well T6134 from November 1982 to December 1985 were basically consistent, but they were quite different from the trend of the Well T61-46 (Fig. 5.30b). Accordingly, it can be preliminarily concluded that the sand bodies in the Es3 7 sand layer group in the Well T61-48 and Well T61-34 are interconnected, while the sand bodies in the Well T61-46 are not connected with those in the Es3 7 sand layer group in the Wells T61-48 and T61-34. ➂ Well test analysis of interwell connectivity: The interwell interference test, pulse well test and unstable well test analyses are important methods to determine interwell dynamic connectivity, of which the interwell interference test

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a. Trends of fluid pressure changes in three producing wells

c. Trends of fluid production changes by closing adjacent well

b. Trends of fluid production changes in three producing wells.

d. Correlation of water injection (Well T61-53) and fluid production (Wells T61-47 and T61-27)

Fig. 5.30 Trends of pressure and fluid production changes in T61 Block of Wangjiagang Oilfield (After Lei et al., 2014)

is the most commonly used (Zhu et al., 2008a, 2008b). The interwell interference test analyzes the reaction of one or several observation wells in the same hydrodynamic system by changing the working system of one or more active wells (new wells are put into production, oil wells are opened or closed, oil nozzles are changed, etc., to judge the connectivity of interwell sand bodies (Zhu et al., 2008a, 2008b). For example, the Well T61-32 in the T61 block was shut down in July 1987, and the liquid production of the Es2 4 sand layer group in the Well T61-25 increased significantly after the Well T61-32 was shut down (Fig. 5.30c). Therefore, it can be determined that the sand bodies in the Es2 4 sand layer group in Wells T61-25 and T61-32 are interconnected. ➃ Interwell water injection response analysis: The change in the water injection volume of water injection wells will cause fluctuations in the liquid production of the connected oil wells. The trends over time of the water injection volume and oil well production in the same hydrodynamic system should be generally similar. This characteristic can be used to analyze the fluid connectivity between oil wells and water wells (Liang, 2010; Tang et al., 2008). For example, in the Well T61-53 group in the T61 block, the liquid production of the Es2 5 sand layer group in the Well T61-27 from May 1986 to October 1988 was similar to the water injection volume of the T61-53 water injection well over time, but it was different from the trend of the Well T61-47. At the same time, after the water

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345

injection volume of the T61-53 water injection well increased significantly in October 1987, the dynamic liquid level of the Well T61-27 increased obviously, and the liquid production increased significantly, while the liquid production of the T61-47 well decreased instead. After the water injection volume of the Well T61-53 was significantly reduced in August 1988, the dynamic liquid level and liquid production of the Well T61-27 were significantly reduced, but the liquid production of the Well T61-47 was not significantly affected (Fig. 5.30d). The above analysis shows that the sand bodies in the Es2 5 sand layer group in Wells T61-27 and T61-53 are connected with each other but not with Well T61-47. (3) Establishment of the sandstone connectivity probability model Using the connectivity identification method mentioned above, we obtained the sandstone percentage data of a total of 431 grid nodes in the T61 block and their corresponding connectivity identification results and counted the correlation between the carrier bed connectivity and the sandstone percentage (Lei et al., 2013). The sandstone percentage data were divided into 20 ranges from 0 to 100%, with each 5%. The percentage of geometrically connected samples of sand bodies corresponding to each range in the total samples was counted, and the correlation between the sand body connectivity probability and sandstone percentage was obtained (Fig. 3.19). When the sandstone percentage is less than 15%, the sand bodies are basically disconnected. A sandstone percentage of 62% is the threshold for large-scale connectivity of sand bodies. The statistical results shown in Fig. 3.19 can be expressed by using the mathematical model of connectivity probability as follows: { P=

0 h ≤ 0.15 22 1 − e(−13.5808(h−0.15) ) h > 0.15

(5.5)

where P is the connectivity probability of the sand bodies and h is the sandstone percentage. Some data not involved in statistics to verify the reliability of t of the method of sandstone connectivity probability characterization. The sand body connectivity probability of the Es2 5 sand layer between Wells T61-29 and T61-98 in the Tong 61 block was 87%. However, the sand body connectivity probability of the Es2 5 sand layer in the Well T61-70 between Wells T61-29 and T61-98 was 85%. The sand body connectivity probability of the Es2 5 sand layer in these wells was relatively high. The fine comparison results of the sand bodies in the Es2 5 sand layer in Wells T61-29, T61-98, and T61-70 showed that the sand bodies in the Es2 5 sand layer in these three wells are interconnected in geometric space. In addition, according to the water injection volume of the Well T61-29 and the liquid production change over time of the Wells T61-98 and T61-70, the sand bodies in the Es2 5 sand layer in the above three wells are also interconnected (Fig. 5.31). The results obtained according to the connectivity probability model are basically consistent with the actual geological conditions, which indicates that the above sand body connectivity probability model

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Fig. 5.31 Correlation of water injection (Well T61-29) and fluid production of producing wells

is reliable and can be used to evaluate the fluid connectivity among sand bodies in the study area. (4) Quantitative characterization of fluid connectivity of the sandstone carrier bed Using the above mathematical model of sand body connectivity probability (Formula 5.5), the connectivity probability of each set of sandstone carrier beds in the study area was calculated according to the drilling sandstone percentage data, and its connectivity was quantitatively characterized (Fig. 5.32). The fluid connectivity characteristics of the carrier beds in the Es3 2 are described in Sect. 3.2 of Chap. 3, and only the sandstone carrier beds in the Es3 1 and the Es2 are analyzed here. The fluvial–delta deposits of the Es3 are distributed throughout the study area. Distributary channels, underwater distributary channels, estuarine dams, sheet-like sand bodies and slump turbidite sand bodies distributed around multiperiod delta fronts are superimposed and connected with each other, and the connectivity between sand bodies is good (Fig. 5.32a). Except for a few areas, such as the W121-T12 and S8 well areas, the connectivity probability of sand bodies in most other areas is generally greater than 70%. As the second member of the Shahejie Formation is mainly composed of superimposed and connected medium-thick sandstones, such as distributary channel sand bodies in delta plain subfacies, underwater distributary channel sand bodies in the delta front, sheet-like sand bodies, estuarine dams, etc., the connectivity probability of sand bodies is generally high, with the characteristics of low in the north and high in the south (Fig. 5.32b). The connectivity probability of sand bodies in the Niuzhuang Subsag in northern China is relatively low, generally between 40% and 60%, and the connectivity probability of a few areas, such as the N876, S133 and N8-H120 well areas, is lower than 40%. The connectivity probability in the Bamianhe area is

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347

a. Sandstone carrier bed of the Upper Submenber of the Third Member of Shahejie Formation

b. Sandstone carrier bed of the Second Member of Shahejie Formation

Fig. 5.32 Connectivity probability of sandstone carrier beds of Shahejie formation in the eastern part of the southern slope of Dongying Sag. (The denser the green dots in the figure, the greater the connectivity probability among sand bodies, and the white area represents almost no connectivity among sand bodies)

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generally 60–70%. The connectivity probability of sand bodies in the W21-T40 well area in the middle of the study area and the Caoqiao and Chenguanzhuang areas in the southwest is the highest, generally greater than 80%.

5.4.1.4

Quantitative Characterization of Sandstone Carrier Bed Capability

Hydrocarbon migration and accumulation for reservoir formation usually occurred in ancient times. During the burial process of the sandstone carrier bed after deposition, the conductivity f the sandstone carrier bed usually be modified due to multiple diagenesis. To analyze the process of hydrocarbon migration and accumulation, it is necessary to analyze the evolutionary process of the physical properties of the sandstone carrier bed and study the paleocarrier capability of the sandstone carrier bed in the key period of hydrocarbon reservoir formation. At present, various diagenetic phenomena preserved in the sandstone and their relationship with hydrocarbon charging are the most direct basis for inferring the paleocarrier capability. Lei et al. (2014) analyzed the diagenetic evolutionary process through systematic fluid–rock interaction research. On the basis of determining the diagenetic sequence and its relationship with hydrocarbon-charging time, Yuhong Lei studied the influence of diagenetic products and spatial distribution after hydrocarbon charging on sandstone physical properties. Their research results showed the following findings: The hydrocarbon-charging time of the Shahejie Formation in the study area was later than the formation time of the main authigenic minerals, and the physical properties of the sandstone during the reservoir-forming period were basically similar to those of the present sandstone. Therefore, the present sandstone physical properties could approximately reflect the paleocarrier conditions during the reservoir-forming period. Based on the statistics of measured core physical properties and corrected acoustic-logging interpretation physical property data, combined with the connectivity characteristics of the sandstone carrier bed, the permeability distribution map of different sandstone carrier beds in the Shahejie Formation was created, and the permeability parameters were used to characterize the carrying capability of the sandstone carrier bed. The sandstone carrier bed in the Es4 1 consists mainly of delta distributary channels, channel mouth bars, distributary channels and sheet-like sand bodies, with shallow burial depths and better capabilities than those of the Es3 1 (Fig. 5.33a). The permeability of sandstone in the Bamianhe T40-T11 well area and C62 well area is generally higher than 400 × 10–3 μm2 . From the Bamianhe area to the northwestern part of the study area, the carrying capabilities gradually deteriorate, and the permeability in the Wangjiagang-Chenguanzhuang area is 150 × 10–3 –400 × 10–3 μm2 . The sandstone carrier bed in the Es2 consists mainly of delta distributary channel sand bodies with permeabilities of 7 × 10–3 –400 × 10–3 μm2 (Fig. 5.33b). The characteristics of capability of the sandstone carrier bed in the Es2 are different from

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Fig. 5.33 Contour maps showing the carrying capability of sandstone carrier beds of Shahejie formation in the study area by permeability parameters (Logarithm). a Es3 1 , b Es2

Fig. 5.34 Quantitative characterization of connectivity and carrying capability of carrier beds in the Es3 1 and the Es2 . In the figure, the gray color represents areas of null conductivity, the green color scale represents the conductivity of carrier beds (Lei et al., 2014). a Es3 1 , b Es2

those in the Es3 1 , Es3 2 . The high-permeability areas are distributed in the W78-W12T11, G1115-G15 and L71 well areas, and the permeability is mostly 150 × 10–3 –200 × 10–3 μm2 . The permeability of sandstone in the northwestern part of the study area is relatively poor, ranging from 10 × 10–3 to 90 × 10−3 μm2 . Based on the characterization of the carrying capability of the carrier beds, considering the internal connectivity characteristics of the carrier bed, we can obtain a quantitative characterization diagram of the connectivity and carrying capability of the carrier bed (Fig. 5.34).

5.4.2 Quantitative Characterization of the Capability of Fault Carrier Deposition has occurred under the control of a large number of adjusting faults have developed in the eastern part of the southern slope of the Dongying Sag since the

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Paleogene because of the continuous transtensional tectonic stress field and continuous subsidence of deep faults in the north area. These faults with different orders, different trends and different opening periods constitute the channels of fluid flow and migration in the basin, which control hydrocarbon enrichment to a great extent. Representative faults were selected to study the opening and sealing properties, as well as the main factors affecting the opening and sealing of faults. On the basis of analyzing the geometric shape and the episodic opening activity of faults in different orders, through the detailed anatomy of typical fault hydrocarbon reservoirs, the influence of various geological factors on fault opening and sealing was examined, and a parameter model for the quantitative characterization of fault opening and sealing in the study area was established. The fault connectivity probability method was used to quantitatively evaluate the connectivity at different positions of faults.

5.4.2.1

Analysis of Fault Geometry and Activity Characteristics

(1) Geometry of the Fault System Faults with different scale are very developed in the study area, All faults that can be identified are growth faults, among which the nearly NE-trending faults show dextral transtensional properties, while the NW-trending faults mostly exhibit sinistral transtensional properties (Chen et al., 1997). These faults constitute a complex fault system. According to the fault scale and its control on tectonics and sedimentation, the fault system in the study area can be divided into three categories. Except for the central large fault in the north and the Bamianhe fault in the southeast, which are second-order faults, the faults mainly include third-order and fourth-order fractures (Figs. 5.35 and 5.36). Controlled by the regional transtensional tectonic background of the Bohai Bay Basin, the geometry of faults in all orders in the study area is complex and variable (Fig. 5.35). Fault strikes can be divided into three groups: NEE or NE, EW, and NWW, with NEE or NE being the most developed. The strike of the faults often changes at different positions, showing the characteristics of “wave” or “S” type changes. There are several types of plane assemblages of faults, such as the “λ” type, parallel type and echelon type, with the echelon type as the main type. Under the background of regional dextral tensile-shear tectonic stress formed in the Tertiary along the Tanlu deep fault, the superposition effect of dextral transtensional stress caused by the inherited activity of the base fault and the slope zone gravity was the key mechanical factor for the formation of the echelon fault assemblage (Chen et al., 1997). The section morphology of the fault is mainly manifested as shovel and plate types (Fig. 5.36). The fault plane of the plate fault is straight, the dip angle is steep, and the smaller fourth-order faults in the area are mainly plate faults. The fault plane of the shovel fault is steep at the top and gentle at the bottom, the fault displacement is large, the fault displacement decreases from bottom to top, and the lateral extension distance of the fault is long. The third-order basin-dipping faults in the study area are all shovel

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Fig. 5.35 Distribution and order division of faults in the eastern part of the southern slope of Dongying Sag

Fig. 5.36 Seismic interpretation section of line 3852 in the eastern part of the southern slope of Dongying Sag

faults. The assemblage forms of fault sections include stepped assemblages, graben assemblages, horst assemblages, Y-shaped assemblages, λ-shaped assemblages and so on. Among them, the stepped assemblage is the most important section assemblage type in the study area, and many NE-trending, basin-dipping faults dip northward in turn, forming a fault terrace structure pattern in this area. At the same time, the

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south-dipping faults cutting deep strata are also arranged nearly parallel, forming a reverse-stepped assemblage. In addition, the “Y” or inverted “Y” type assemblage composed of third-order faults and upthrown lower-order faults is also common, which is a typical fault assemblage type with rich hydrocarbons in the study area. (2) Fault opening period and its correlation with the hydrocarbon reservoir formation period Fault activity is closely related to the formation, destruction and reformation of hydrocarbon reservoirs. Only when there is a good correlation between the fault activity time and hydrocarbon reservoir formation period can the faults become the vertical and lateral migration conduits for hydrocarbons migration (Anderson et al., 1994; Hooper, 1991; Smith, 1980). If the faults are inactive during the reservoirforming period, most faults are sealed, causing hydrocarbons to accumulate near the faults (Bouvier et al., 1989; Fowler, 1970; Smith, 1980). Therefore, the correlation between the fault activity time and hydrocarbon accumulation period is one of the key factors affecting the effectiveness of fault carriers. Based on the interpretation of three-dimensional seismic data, the fault growth index method and fault throw method were used to study the fault activity period. The statistical results of the growth index and throw of faults with different orders in the main oil-bearing tectonic areas in the study area showed (Figs. 5.37 and 5.38) the following findings: The third-order faults are long-term inherited active faults which started to be active in the early period of Dongying Formation, and the activity of those faults was stronger in the Dongying period, Guantao periods to the Minghuazhen early period. Generally, the fourth-order faults were most active in the fourth memberthird member period of the Shahejie Formation, and the activity in the late period was different to some extent. According to the characteristics of late activity, faults can be divided into three categories: one includes faults that were active during the Minghuazhen early period; the second includes faults that were active during the Guantao period; and the third includes faults that were strongly active in the early sedimentary period of the Shahejie Formation and basically inactive in the late period. According to the previous research results on the reservoir-forming period in Sect. 3.2, there were mainly two periods of hydrocarbon reservoir formation in the study area, namely, the late Dongying period and the late Guantao period to the Quaternary, and the latter was a large-scale reservoir-forming period. A comparative analysis of fault activity and reservoir-forming period shows (Fig. 5.39) the following findings: Third-order faults were strongly active from the end of the Guantao period to the Minghuazhen early period, with a good temporal correlation with the second reservoir formation period, and they could have played a important role in hydrocarbon migration. However, the active time of the fourth-orders have a poor correlation with the reservoir-forming period, which mainly constituted the barrier boundary of hydrocarbon accumulation.

Fig. 5.37 Statistics of third-order fault growth index in the main oil-bearing tectonic zone in the eastern part of the southern slope of Dongying Sag

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Fig. 5.38 Statistics of paleo-fall of fourth-order fault in the main oil-bearing tectonic zone in the eastern part of the southern slope of Dongying Sag

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Fig. 5.39 Matching relationship between fault activity period and reservoir forming period in the eastern part of the southern slope of Dongying Sag

5.4.2.2

Parameters for Characterizing Fault Opening and Sealing

The parameters that could be obtained from the exploration data and could be used to quantitatively characterize fault opening and sealing characteristics were selected, to analyze the relationship between various geological factors and fault opening and sealing. Sixteen reservoir sections in the nose-like, oil-bearing background of the Wangjiagang Oilfield were made to measure or calculate the parameters for characterizing the fault opening and sealing, foot wall hanging wall. The nodes on the fault plane that can be determined whether they were open or closed during hydrocarbon migration period were selected, and the corresponding parameters for characterizing the fault opening and sealing were measured or calculated. These data sets were used to establish the fault opening and closing identification method. (1) Parameters of fault opening and sealing characterization Using common exploration geological data such as tectonic maps, logging curves, comprehensive mud-logging maps, oil test results, fracturing tests, etc., the parameters for characterizing fault opening and sealing on the 16 reservoir sections were measured and calculated. These parameters include the burial depth of the fault point, fault strike, dip angle, fault displacement, sandstone percentage of the faulted strata, fluid pressure, shale gouge ratio (SGR) and normal stress of the fault plane. As the method for obtaining the fault geometric parameters mentioned in Chap. 3, the burial depth of the fault can be directly calculated from the reservoir section. The fault strike was determined according to the fault geometric polygon on the tectonic map of the top surface of the stratum. The fault displacement took the difference in the depth of the same set of strata breakpoints between the hanging wall and the foot

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wall of the fault. The fault dip angle was measured in sections, and the dip angle of the connecting line between each section of strata and the intersection point of faults was taken as the fault dip angle corresponding to the strata. The lithology of faulted strata was derived from the data of more than 500 exploration wells adjacent to the fault. The thickness of the reservoir sandstone in each stratum unit was determined according to gamma-logging curves. Due to the gradual change in stratum thickness and lithology in the study area, the sandstone percentage on the fault plane is the lithological average of the hanging wall and foot wall. To obtain the mudstone fluid pressure of the whole fault system during the hydrocarbon migration period, this study used the 3D basin simulation method (Temis 3D software from the Institut Francais Du Petrole) to recover the mudstone paleopressure in different periods and used the mudstone compaction curve to calculate the mudstone pressure of 80 wells to calibrate the simulation results. The shale gouge ratio (SGR) was calculated according to the SGR formula defined by Yielding and Freeman (1997), the mudstone thickness was obtained by analyzing the gamma-logging curves of wells near the fault, and the ratio of the mudstone thickness to the fault displacement of the faulted strata was calculated as the corresponding SGR value at the position of the set of strata breakpoints. In the calculation of normal stress on the fault plane, it was necessary to determine the parameters of the in situ stress field in addition to the fault strike, dip angle and burial depth data. The magnitude and direction of in situ stress refer to the drilling hydraulic fracturing data of the Shengli Oilfield (Yellow River Delta region) from Wan (1993) and Zhang and Hou (2003). The maximum principal stress in the study area is vertical, and the direction of the maximum horizontal principal stress is 70°. The maximum principal stress is equal to the overlying load, and the maximum horizontal principal stress (σH ) and minimum horizontal principal stress (σh ) change with depth. The relationship is as follows: σH = −15.69 + 0.032H

(5.6)

σh = −6.93 + 0.021H

(5.7)

where H is the burial depth of the fault. Approximately 1243 data sets were obtained, which could beused to characterize fault opening and sealing. (2) Identification of faults as hydrocarbon migration conduits Using the previously proposed opening and closing identification method (see Chap. 3), based on the hydrocarbon distribution in the hanging wall and the foot wall of the fault and the genetic types of crude oil in the reservoirs on the two sides of the fault zone, we identified the opening and sealing of the intersection of the fault trajectory and the carrier bed in 16 sections. Figure 5.40 shows an example of fault opening and sealing identification in the reservoir section (P1 profile). The hydrocarbon expulsion threshold of the source

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357

rocks was 2600 m. Hydrocarbons migrated upward along the fault, with buoyancy as the main migration force after entering the Es3 1 and overlying reservoirs. On the reservoir section, we identified the genetic types of crude oil in the reservoirs on both sides of the fault, represented by saturated hydrocarbon mass spectra in different colors, and marked the possible direction of hydrocarbon migration through qualitative geological analysis, which served as a reference for fault opening and sealing identification. The identification results were represented by points in different colors. Green points represent the opening points where the fault could be clearly identified as a migration conduit, blue points represent fault sealing, and black points indicate that the opening and sealing could not be determined due to lack of data or lack of hydrocarbons in the upthrown and hanging walls. The parameters for characterizing fault opening and sealing of the opening and closing points were measured and calculated, and a total of 615 datasets for faultopeningand sealing characterization were obtained, some of the data sets were listed in Table 5.9.

5.4.2.3

Evaluation Method of Fault Opening and Sealing

Fault opening and sealing in the process of hydrocarbon migration are affected by many factors, which are different in different regions. It is necessary to evaluate the influence on fault opening and sealing according to the geological conditions of each actual region and find the most reliable parameters to identify fault opening and sealing. Using the fault connectivity probability method (Zhang et al., 2010, 2011), through the statistical analysis of the correlation between the geological parameters and the corresponding fault connectivity probability (N p ), the effective parameters that can quantitatively characterize the fault opening and sealing in the study area were determined, which could be used to quantitatively characterize the fault connectivity in the process of hydrocarbon migration. (1) Relationship between comprehensive parameters and fault connectivity probability As described in Chap. 4, geological parameters such as fault plane burial depth, strike, dip angle, fault distance, sandstone content in faulted strata and mudstone fluid pressure can often reflect their influencing trend on fault opening and sealing from a certain aspect, but each single factor parameter cannot effectively characterize fault opening and sealing. Therefore, a more comprehensive method is needed to quantitatively characterize fault opening and sealing. The normal stress of the fault plane comprehensively reflects the influence of many factors, such as the fault plane dip angle, burial depth, strike, and magnitude and direction of in situ stress, on fault opening and sealing. Normal stress observation values of 615 fault planes were obtained, and every 5 MPa was divided into 8 ranges to calculate the fault connectivity probability. As shown in the δ-N p relationship diagram (Fig. 5.41a), there is an obvious negative correlation between the two. With the increase in the normal stress of the fault plane, the probability of fault opening

Fig. 5.40 Identification of fault opening and sealing in reservoir section of W127-W94 well in Wangjiagang oilfield

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N106

N105

QF

QF

3297.0

3298.0

CZ3

QF

QF

CA

QF

3256.0

3257.0

3257.5

QG QF

3203.0

3203.5

QG

QF

QF

3135.0

QF

QF

QF

QF

QF

3202.5

QF

3133.0

QF

QF

QF

3255.0

3255.5

QG

3254.0

QF

QG

QG

QF

3253.0

QG

3252.5

QF

QG

3253.5

QF

QF

3251.5

3252.0

QF

3251.0

QG

QF

CZ4

QF

QF

QF

QF

QF

QF

QF

QF

QF

6

10

8

9

8

8

9

9

9

8

10

7

9

7

8

8

8

7

7

12

8

8

9

10

8

8

QY2

7

7

9

9

10

11

10

12

QY3

QY1

CZ2

CZ1

QF

3296.5

N103

Gas–liquid ratio (%)

Host mineral

Sample depth (m)

Well name

Table 5.8 Characteristics of fluid inclusions and homogenization temperature data

QY4

8

9

11

14

13

7

12

12

12

2

4

4

4

4

3

5

4

6

4

4

S1

4

5

3

4

3

3

3

3

4

4

4

5

4

4

5

S2

Size (m) S3

6

5

3

4

6

3

5

8

5

S4

6

6

2

2

4

3

8

2

104.2

89.3

116

77.5

105.9

95.1

109.8

79.9

105.2

110.4

105.7

Th1

113.4

112.2

116.9

122.5

126.8

121.6

120.6

113.2

113.2

112.6

114.6

116.5

113.5

116.4

Th2

Th4

140.3

149.7

153.1

150.9

154.8

145.2

157.3

151.3

154.8

(continued)

129.4

129.4

130.7

126.5

127.3

135.6

142.4

139.2

130.7

Th3

Average homogenization temperature (°C)

5.4 Establishment and Quantitative Characterization of the Carrier System 359

CZ4

QF

QF

QG

3284.0

QF

QF

3273.0

3275.0

QF

QF

QF

3269.5

QF

QF

QF

QF

3276.5

QF

QF

3180.9

QF

3205.9

3182.5

QF

QF QF

QF

QF

QF

7

7

8

7

6

8

7

8

8

7

8

8

7

9

8

8

6

QY2 8

QY3

QY1

CZ3

CZ1

CZ2

Gas–liquid ratio (%)

Host mineral

3205.0

3204.5

3204.0

Sample depth (m)

8

7

9

QY4

4

4

6

5

3

3

5

4

S1

6

4

5

5

5

4

5

5

3

S2

Size (m)

5

S3

4

6

5

S4

90.1

95.8

116.2

99

109.6

98.7

90.9

86.1

< 60

Th1

121.5

120

121.1

120.1

123.8

123.7

119.7

118

105.8

Th2 121.6

Th3

Average homogenization temperature (°C)

149.4

143.5

146.5

Th4

Note CZ1 , CZ2 , CZ3 , and CZ4 represent the occurrence of inclusions in the first, second, third, and fourth periods respectively, QF refers to quartz granule fracture, QG refers to quartz overgrowth, and CA refers to calcite cement; QY1 , QY2 , QY3 , and QY4 represent respectively the gas–liquid ratios of inclusions in the first, second, third and fourth periods; S1 , S2 , S3 , and S4 represent the average size of inclusions in the first, second, third and fourth periods; Th1 , Th2 , Th3 , and Th4 represent the average homogenization temperatures of inclusions in the first, second, third, and fourth periods

N107

Well name

Table 5.8 (continued)

360 5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

5.4 Establishment and Quantitative Characterization of the Carrier System

361

Fig. 5.41 Relationship between fault connectivity probability and comprehensive parameter. a Normal stress of fault plane (SN); b Shale gouge ratio (SGR)

gradually decreases. The fitting degree of the quadratic multinomial relationship is the highest, with a correlation coefficient of 0.91, which is significantly higher than that of the relationship between the fault plane burial depth, dip angle, strike and other parameters and the fault connectivity probability. The SGR involves the lithology of faulted strata and fault displacement, which are two important factors affecting fault opening and sealing. The 615 SGR observation values in the study area were divided into 10 ranges, each 0.1, and the fault connectivity probability in each range was counted. The relationship diagram of SGR-N p (Fig. 5.41b) shows that the two have a good correlation, and the correlation coefficient of quadratic polynomial regression reaches 0.93. The probability of fault opening decreases with increasing SGR values. When the SGR value is less than 0.45, the probability of a fault opening as a migration conduit is very high. Once the SGR value is greater than 0.70, the probability of fault opening is extremely low. Compared with geological parameters reflecting a single element, comprehensive parameters may be better quantitative characterization parameters of fault opening and sealing. On the one hand, the correlation coefficient between comprehensive parameters and fault connectivity probability is higher, and the fault connectivity probability characterized by comprehensive parameters can be more continuously distributed between 0 and 1. (2) Fault connectivity probability characterized by the fault opening index The fault opening index (FOI) is a comprehensive parameter composed of fluid pressure (P), normal stress (δ) and the shale gouge ratio (SGR) (Zhang, 2007; Zhang et al., 2010), which includes several geological factors related to fault opening and sealing. Using 615 observation values in the study area, the opening index value of each statistical point was calculated. The opening index varied between 0.5 and 5 and was divided into 16 statistical intervals each with 0.25 as increment. The relationship between the fault opening index and fault connectivity probability was determined (Fig. 5.42). As shown in Fig. 5.42, the fault opening index has a very significant correlation with the connectivity probability. When the FOI value is less than 0.75,

362

5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

Fig. 5.42 Relationship between fault opening index and fault connectivity probability in the southern slope of Dongying Sag

the connectivity probability is 0, indicating that the fault is sealed. When the FOI value is between 0.75 and 3.5, the correlation coefficient from quadratic multinomial regression is 0.99. When the FOI value is greater than 3.5, the connectivity probability is always equal to 1, indicating that the fault is open. Obviously, compared with the previous geological parameters, the fault opening index can better characterize the fault opening and sealing. The mathematical functional relationship between the two is expressed as: ⎧ FOI ≤ 0.75 ⎨0 Np = −0.0714 · FOI2 + 0.6489 · FOI + 0.082 0.75 < FOI < 3.5 ⎩ 1 FOI ≥ 3.5

(5.8)

where N p is the fault connectivity probability and FOI is the fault opening index. The data sets not involved in statistics of points on faults from another fault reservoir section in the study area was used to validate the abovementioned method of FOI parameter in evaluating fault opening and sealing. Taking the reservoir section passing through the G61 fault block as an example (Fig. 5.43), the W43, F1 and W41 faults are all oil source faults connecting source rocks and reservoirs, and the FOI and N p values at each breakpoint were calculated. The FOI values of the breakpoints in Wang 42 fault ranged between 2.21 and 2.97, indicating this fault with a high probability of opening (N p is 0.64 to 0.86) during the period of hydrocarbon migration. Therefore, the hydrocarbons from the source rocks in the Es4 1 and Es3 3 could migrate to the reservoir of Es2 and Es3 1 in the footwall of the Wang 42 fault, forming the T61 well reservoir in this area. The FOI value of the breakpoint in the fault at the bottom of the Es1 is only 1.3, indicating that the fault in the bottom of Es1 is well sealed (N p is 0.19), and it is difficult for hydrocarbons to migrate upward to the overlying strata of Es2 . No hydrocarbon reservoir has been found in the Es1 , Ed and Nm in the area. The FOI of the breakpoints of F1 fault in the Es2 , and Es3 were higher than 2.1, and the corresponding N p values were higher than 0.6, which indicated the F1 fault in the Es2 , and Es3 were opening and the hydrocarbons

5.4 Establishment and Quantitative Characterization of the Carrier System

363

from source rocks could migrate to the Es2 to form the reservoir in the W4 well. No hydrocarbon reservoir has been found in the Es1 because the top of F1 fault in the Es2 is closed (N p is only 0.28). The FOI of the W41 fault in the Es3 3 was 1.12, and the fault sealing in the Es3 3 is good (N p is only 0.18). It is difficult for hydrocarbons to enter the Es3 3 and the overlying reservoirs, and no hydrocarbons have been found in the area adjacent to W41 well. The evaluation results of fault opening and sealing in the reservoir section shown in Fig. 5.43 are consistent with the hydrocarbon exploration results, which proves that above-mentioned the method can be applied to evaluate fault opening and sealing when hydrocarbon migration and accumulation happened.

5.4.2.4

Quantitative Evaluation of the Fault Carrier

According to the fault modeling method described in Chap. 3, the gridding models of the main oil-controlling faults in the study area were established. The parameters of the nodes on the gridding fault model such as shale gouge ratio, mudstone fluid pressure, normal stress of the fault plane, and fault opening index used for fault opening and sealing evaluation were obtained based on drilling, logging, seismic and other exploration data (Fig. 5.44). And the connectivity probability of grid nodes of fault model were obtained by Formula (5.8), which could be used to quantitatively evaluate the opening and sealing of the fault when hydrocarbons migration and accumulation happened. Here, the F1 fault was taken as an example to introduces the evaluation of fault opening and sealing during the periods of hydrocarbons migration and accumulation by fault connectivity probability, The F1 fault is located on the third-order basin-dip fault in the transition area from the Niuzhuang Subsag to the Wangjiagang fault tectonic zone and is the oil-controlling fault of the Shahejie Formation reservoir in the T61 block. The fault is nearly E–W-trending, with an extension length of approximately 125 km. The fault cuts through the strata from Es4 to Ng, and the fault displacement of the Shahejie Formation is the largest, approximately 249 m. The dip angle of the fault is large at the upper part while small at the lower part, ranging from 40° to 72°. Figure 5.45 shows the connectivity probability of the F1 fault. In the figure, the ordinate is the burial depth of the fault plane, and the abscissa is the seismic CDP number. The oil shows revealed by drilling well on both sides of the fault are marked in the figure. The solid red circle represents the oil shows in the reservoirs of the hanging wall, and the hollow red circle represents the oil shows in the reservoirs of the foot wall, serving as a reference for the analysis of the control effect of the fault carrier on hydrocarbon migration. The connectivity probability of F1 fault shows (Fig. 5.45) that connectivity probabilities of the fault plane are heterogeneous. The connectivity probabilities of the segment between Es3 3 -hanging wall and Es3 2 -hanging wall nearby wells N92 and N303 are lower than 0.3, which indicates that the segment of the fault plane was sealing and prevented hydrocarbons to migrate to the strata overlying the Es3 3 , and few hydrocarbons found in the reservoirs of those strata of wells N 92 and N 303.

5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

Fig. 5.43 Reservoir section and FOI, N p values of main faults

364

Fig. 5.44 Sections of parameters of F1 fault for fault opening and sealing evaluation

5.4 Establishment and Quantitative Characterization of the Carrier System 365

366

5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

Fig. 5.45 Oil shows and section of connectivity probability of F1 fault opening and sealing

The connectivity probabilities of the segment between Es4 -hanging wall and Es3 3 hanging wall nearby wells W4 and T61 are generally higher than 0.5, and the segment of the fault plane was opening and hydrocarbons migrate into the strata overlying the Es4 . The connectivity probabilities of the segment between Es3 3 -hanging wall and Es3 1 -foot wall nearby well W1 were generally lower than 0.2, and that between Es3 3 -hanging wall and Es4 -hanging wall were higher generally than 0.4. The hydrocarbons from source rocks could be migrated into the strata underlying Es3 3 , but could not into the strata overlying the Es3 1 due to the sealing of the segment between Es3 3 -hanging wall and Es3 1 -foot wall.

5.4.2.5

Transportation Capability Characterization of Fault by Permeability

The fault connectivity probability is only a preliminary quantitative description of the fault carrier. It only reflects the relative effect of fault activity on hydrocarbon migration and accumulation. To characterize the transportation capability of faults to migrate hydrocarbons, it is necessary to convert the connectivity probability into a permeability reflecting the fluid flow performance by the theoretical formula (Formula 3.13) proposed in Sect. 3.4 of Chap. 3 to be consistent with the quantitative characterization results of the sandstone carrier bed. The key geological parameters involved in the mathematical model mainly include the average fracture width, average fracture length, mudstone permeability and faultopening threshold. The fracture geometric parameters can be obtained through the statistics of field outcrops and drilling cores in fault zones. This study was mainly

5.4 Establishment and Quantitative Characterization of the Carrier System

367

Fig. 5.46 Characterization of equivalent permeability of F1 fault carrier in the eastern part of the southern slope of Dongying Sag

based on the observation results of a small number of drilling cores of the fault zone in the Jiyang Depression by Xu et al. (2003). The average length c of fractures is 10–15 cm, and the average width w is approximately 1–2 mm. The mudstone permeability K 0 is 0.005 × 10–3 μm2 . The fault opening threshold was obtained by statistical analysis of fault connectivity probability and hydrocarbon distribution, and Pc was taken as 0.3. By substituting these parameters into Formula (3.13), the fault connectivity probability can be converted into permeability, and the carrying capability of different parts of the fault can be represented on the fault plane topography. Figure 5.46 shows the fault plane topography of the fault connectivity probability of the F-1 fault converted into the permeability parameter. The blue color from dark to light in the figure indicates the permeability of the fault zone from low to high, indicating that the fault carrying capability gradually increases. The equivalent permeability value of the fault ranges from 0.05 × 10–3 μm2 to 10,000 × 10–3 μm2 , thus realizing the unified quantitative characterization of fault carriers and sandstone carriers.

5.4.3 Establishment of Composite Carrier System In the eastern part of the southern slope of the Dongying Sag, the carrier system for hydrocarbon migration is a complex network system composed of faults, fractures and carrier beds. Hydrocarbons usually migrate in the composite carrier system

368

5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

composed of connected sand bodies and fault carriers—in zigzag pathways. Therefore, in order to determine the assemblage relationship between connected sand bodies and fault carriers in time and space to establish composite carrier system, it is necessary to study the evolution of the basin, the history of hydrocarbon generation and expulsion from source rocks, the age of hydrocarbon reservoir formation and reservoir geochemistry, in addition to quantitative characterization of sandstone carrier beds and fault carriers and the permeability is used as a unified parameter to quantitatively characterize the transport capability of sandstone carrier beds and faults in the composite carrier system.

5.4.3.1

Analysis of the Assemblage of the Composite Carrier System

According to the quantitative study of the sandstone carrier bed and faults mentioned above, considering the spatial position relationship between the source and reservoir, the long-distance lateral migration conduits of hydrocarbons in the study area mainly include sandstone carrier beds in the Es4 1 , Es3 2 , Es3 1 , and E3 2 . The multiperiod inherited active third-order faults, such as F1 – F10, act as the vertical migration carriers of hydrocarbons, and the second-order small faults mainly play an important role in the adjustment of hydrocarbon migration and accumulation. There are many assemblage composed of sandstone and fault carriers in time and space, but only a few of them play an important role in hydrocarbon migration and accumulation. These assemblages must be identified to quantitatively characterize their connectivity and transport capability. The possible pathways of hydrocarbon migration from the source to the reservoir in the assemblages composed of sandstone carrier beds and faults were traced by comparing the hydrocarbon fingerprint biomarker and the physical property parameters of crude oils in the reservoirs distributed in the hanging wall and foot wall of fault, in combination with evolution of hydrocarbon generation and expulsion of source rocks, hydrocarbon-charging periods, connectivity and spatial distribution of carriers. And then the effective assemblages composed of connected sandstone bodies and fault carriers for hydrocarbon migration were identified. The reservoir section shown in Fig. 5.47 across through the W542, W3-X1, T61, W14-51 and Wang 21 blocks in the study area was used to trace the pathways of hydrocarbon migration and accumulation to identify the effective assemblages. The fingerprint biomarkers parameters such as Pr/Ph, gammacerane/(gammacerane + C30 hopane), C35 /C34 homohopane, C27–29 rearrangement/C27–29 regular sterane, dinosterane/C29 regular sterane and 4-methyl C29 sterane relative content of crude oil from the reservoirs in hanging wall and foot wall of faults (Table 5.10) were selected to identify the genetic types to trace hydrocarbon pathways. The results showed that the oils of reservoirs in the Es3 2 of the W542 (Wells W542 and NZW542-2), W3-X1 blocks (Wells W631 and WJW3-10), and that in the Es2 and Es3 1 of T61 block (Well WJW14-51) were Class-I from the source rocks in the Es3 3 . The oils of reservoir in the Es4 1 of the W21 block (Well W21) were Class-II from the source rocks in the Es4 1 . The oil of reservoir in the Es2 of the W14-51 block (Well WJW14-51) was

5.4 Establishment and Quantitative Characterization of the Carrier System

369

Fig. 5.47 Identification section of carrier assemblage mode of reservoir passing through W542 block–W21 block

Class-III, which was the mixed oil migrated from the source rocks in the Es3 3 and Es4 1 . According to the identification of genetic types of oils in reservoirs, combined with the other geological data including the characterization of sandstone carrier beds, faults, etc., the possible pathways of hydrocarbon migration were inferred in Fig. 5.47 as follows: The hydrocarbons expelled from source rock in the Es3 3 in the Niuzhuang Subsag migrated upward into the subturbidite sand bodies of the Es3 2 to form the lithological reservoir in the W542 block at first. At the same time, hydrocarbons did not migrate upward along F6, but migrated along the carrier bed in the Es3 2 to the F1a fault, and then migrated upward to the carrier bed in the Es3 1 through the F1a fault, forming the W3-X1 block reservoir. The excess oil continued to migrate upward along the F1b fault to form the reservoirs in the Es3 1 and Es2 in the Tong 61 block. Afterward, the hydrocarbons continued to migrate along the F1 fault, mixed with oil migrated from the source rock in the Es4 1 along F2 fault, to form the Class III oil in the reservoir in the W14-51 area. One part of the crude oil generated by source rocks in the Es4 1 upper may have migrated upward along the F2 fault and F3 fault to the carrier beds in the Es4 1 and accumulated in fault–lithological traps in the W15 and W21 blocks. It should be noted that the hydrocarbon migration pathways traced by the geochemical indicators of crude oil in reservoirs at present was the comprehensive results of multiperiod hydrocarbon migration and accumulation. However, the results of hydrocarbon migration pathways of oils from different source rocks traced by the geochemical indicators was ambiguity, which is also one of the limitations of using the geochemical method to identify the carrier assemblage mode in the early reservoir formation stage. The reservoirs of the Shahejie Formation in the study area experienced two periods of hydrocarbon charging at the end of the Dongying period and the end of the Guantao period to the Quaternary, but the area and amount of hydrocarbon expulsion from source rocks in the Es3 3 and the Es4 1 at the end of the Dongying period were small, which indicates that the location of early hydrocarbon migration and accumulation may have been limited to the oil-generating sag area. The statistical results of reservoir formation age inferred from measured inclusion homogenization temperature show that, showed that most hydrocarbons were mainly charged in the latter period, and the samples shown the hydrocarbons charging at the

47.92 90.92 73.86 80.19 68.07

2176.92 77.00 47.39

2676.15 57.00 47.78

2905.38 57.00 47.39

3017.69 58.00 47.20

43.01

2133.85 77.00 48.32

2410.77 57.00 47.81

38.21 38.71

2005.38 77.00 48.97

46.15

1846.15 77.00 52.15

2070.77 77.00 48.39

71.88 38.68

3244.27 58.00 46.01

68.07

2949.62 58.00 47.39

1667.69 78.00 53.58

73.86 80.19

2602.29 57.00 47.65

90.92

2319.85 57.00 47.86

2825.19 57.00 47.50

38.21 47.92

1967.18 77.00 49.89

2129.01 77.00 48.04

42.74

35.94

26.96

24.05

21.71

21.28

20.66

20.00

18.41

16.63

45.95

41.78

34.94

26.22

23.14

21.24

19.62

16.25 17.95

38.68

0.02

0.02

0.22

0.50

0.27

0.29

0.51

0.53

0.08

0.45

0.22

0.33

0.01

0.23

0.39

0.24

0.45

0.20

0.36

0.84

0.78

0.78

0.66

0.64

0.73

0.74

0.60

0.88

0.55

0.98

0.97

0.68

0.37

0.68

0.66

0.93

0.64

12.00

13.00

13.00

13.00

7.00

7.00

7.00

7.00

7.00

8.00

12.00

12.00

13.00

13.00

13.00

7.00

7.00

7.00

8.00

32.13

32.09

30.24

27.01

21.81

21.06

20.38

19.52

16.94

15.06

35.23

31.28

31.09

29.37

25.88

21.10

18.84

16.36

14.55

1.58

1.43

1.14

1.34

1.56

1.38

1.37

1.71

1.23

2.02

1.36

1.16

1.31

2.43

1.48

1.58

1.17

1.74

Opening

Opening

Sealing

Opening Sealing

None

None

None

(continued)

Sealing

Opening

Oil formation Opening

Oil formation Opening

None

Oil formation Sealing

None

Oil formation Sealing

None

Oil spot

Oil formation

Oil spot

None

Oil formation Opening

None

None

None

None

Fault Fluid Sandstone SGR Angle Normal Fault Hydrocarbon Fault displacement pressure ratio difference stress of opening bearing opening/Sealing fault index property plane 46.15

Bottom Fault Fault depth of strike dip fault angle point

Section W102 1629.01 78.00 54.02 fault P1 1800.00 77.00 52.55

Section Fault name

Table 5.9 Calculation of basic geological parameters of reservoir section and some results of opening and sealing identification

370 5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

21.46 23.52 25.68 30.03 37.56

2151.15 54.00 47.17 145.80

2574.81 33.00 44.41 102.29

2736.64 33.00 43.52 132.82

2874.81 53.00 42.35 160.31

20.38

2043.51 54.00 48.12 130.53

2358.02 33.00 46.19 133.59

16.94 18.64

1698.47 49.00 52.96 124.43

38.42

2940.46 53.00 42.78 212.98

1868.70 54.00 50.71 140.46

28.57 35.47

2603.82 33.00 43.98 132.82

24.66

2472.52 33.00 45.46 102.29

2714.50 53.00 42.77 160.31

20.00 22.19

2005.34 54.00 48.97 145.80

2224.43 33.00 47.45 133.59

19.08

1912.98 54.00 49.19 130.53

0.33

0.01

0.24

0.37

0.26

0.43

0.29

0.54

0.02

0.02

0.09

0.18

0.50

0.33

0.33

0.26

0.73

0.70

0.70

0.81

0.41

0.52

0.57

0.65

0.46

0.73

0.73

0.91

0.78

0.46

0.72

0.66

0.82

17.00

37.00

37.00

37.00

16.00

16.00

16.00

21.00

17.00

17.00

37.00

37.00

37.00

16.00

16.00

16.00

16.00

21.00

12.00

34.89

42.80

40.86

36.67

25.17

23.52

20.61

19.33

35.64

32.66

40.36

38.87

34.05

22.81

21.55

19.70

18.52

17.37

36.43

1.54

1.01

0.77

1.56

1.64

1.52

1.40

1.90

1.47

1.48

0.78

0.81

1.43

1.21

1.34

1.12

1.28

2.27

Sealing

Sealing Opening

Opening

Sealing

Opening

(continued)

Oil formation Sealing

Oil spot

None

Oil formation Opening

None

None

None

None

Oil spot

Oil formation

Oil formation

Oil spot

Oil formation Opening

None

None

None

None

None

Oil spot

0.52

17.24 17.96

1800.76 54.00 50.87 140.46

0.40

0.60

46.97 15.70

71.88

3316.15 58.00 45.15

0.11

Fault Fluid Sandstone SGR Angle Normal Fault Hydrocarbon Fault displacement pressure ratio difference stress of opening bearing opening/Sealing fault index property plane

Bottom Fault Fault depth of strike dip fault angle point

Section W101 1574.05 49.00 53.98 124.43 P1 fault 1728.24 54.00 51.96 140.46

Section Fault name

Table 5.9 (continued)

5.4 Establishment and Quantitative Characterization of the Carrier System 371

Section T61 P3 fault

Section Fault name

42.31 39.96 41.53

1932.13 55.00 42.35

2244.76 59.00 35.09

39.18

1798.14 58.00 47.65

2074.73 59.00 38.45

41.53 46.23

2203.23 59.00 36.77

39.96

2034.77 59.00 39.81

1670.42 58.00 50.74

42.31 39.18

1889.81 55.00 44.47

39.18

1850.64 51.00 46.24

1983.06 58.00 41.87

39.18 36.04

1758.96 58.00 48.51

1798.92 51.00 45.59

46.23

1624.19 58.00 50.96

22.39

20.69

19.27

17.94

16.66

21.98

20.30

19.78

18.85

18.46

17.94

17.55

16.20

41.21

0.58

0.41

0.67

0.48

0.41

0.58

0.53

0.24

0.37

0.37

0.02

0.40

0.22

0.42

0.90

0.52

0.90

0.59

0.42

0.49

0.95

0.33

0.83

0.89

0.52

0.78

11.00

11.00

15.00

12.00

12.00

11.00

11.00

12.00

15.00

19.00

19.00

12.00

12.00

17.00

27.28

24.41

23.01

19.22

16.96

26.40

23.62

22.83

22.01

22.31

21.71

18.54

16.37

38.73

1.95

0.94

1.62

1.04

1.66

1.97

1.76

0.91

2.57

1.00

0.93

1.82

1.36

Sealing Sealing

None

None

None

None

None

Opening

Sealing

Sealing

Sealing

Oil formation Opening

Oil formation Sealing

Oil formation Sealing

Oil formation Sealing

Oil formation Sealing

None

None

None

Oil spot

Fault Fluid Sandstone SGR Angle Normal Fault Hydrocarbon Fault displacement pressure ratio difference stress of opening bearing opening/Sealing fault index property plane

3153.44 53.00 41.85 212.98

Bottom Fault Fault depth of strike dip fault angle point

Table 5.9 (continued)

372 5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

5.4 Establishment and Quantitative Characterization of the Carrier System

373

end of the Dongying period were mainly distributed in the Niuzhuang deep sag near the F6 fault. Therefore, for the eastern part of the southern slope of the Dongying Sag, the hydrocarbon migration and accumulation pathways predicted by geochemical data mainly the migrating results at the late charging stage (end of the Guantao period-Quaternary). According to the analysis of hydrocarbon migration and accumulation pathways in the above mentioned section (Fig. 5.47), there were 9 possible assemblages of the composite carrier system from the end of the Guantao period to the Quaternary: (1) the Es3 2 carrier bed–F1 fault–the Es3 1 carrier bed–F2 Fault–the Es3 1 carrier bed; (2) the Es3 2 carrier bed–F1 fault–the Es2 carrier bed–F2 fault–the Es2 carrier bed; (3) the Es3 2 carrier bed–F1a fault–the Es3 1 carrier bed –F2 fault–the Es2 carrier bed; (4) the Es3 2 carrier bed–F1a fault–the Es3 1 carrier bed–F1 fault–the Es3 1 carrier bed n–F2 fault–the Es3 1 carrier bed; (5) the Es3 2 carrier bed–F1a fault–the Es3 1 carrier bed–F1 fault–the Es2 carrier bed–F2 fault–the Es2 carrier bed; (6) the Es3 2 carrier bed–F1a fault–the Es3 1 carrier bed–F1b fault–the Es2 carrier bed–F1 fault–the Es2 carrier bed–F2 fault–the Es2 carrier bed; (7) the Es4 1 carrier bed–F1 fault–the Es3 1 carrier bed–F2 fault–the Es3 1 carrier bed; (8) the Es4 1 carrier bed–F1 fault–the Es2 carrier bed–F2 fault–the Es2 carrier bed; and (9) the Es4 1 carrier bed–F1 fault–the Es3 1 carrier bed–F2 fault–the Es2 carrier bed. Hydrocarbon migration and accumulation in actual basins are affected by various geological factors, with strong heterogeneity. Especially in such basins and areas where faults are developed, the vertical opening and sealing of faults makes the migration pathways in three-dimensional space more complicated. However, the number of oil-bearing samples with geochemical data that can be used to trace the hydrocarbon migration pathways are often limited. The heterogeneity of hydrocarbon migration and accumulation may lead to the ambiguity of hydrocarbon migration pathways identified by the geological data of oils from reservoirs in different tectonic parts or local areas. In order to improve the prediction accuracy of hydrocarbon migration pathways by geochemical data in a single section analysis as much as possible, 16 reservoir sections were selected in the study area to trace the hydrocarbon migration pathways by the aforementioned method, and nine kinds of possible model of composite carrier system were established, which would be description in detail latter.

5.4.3.2

Quantitative Characterization of the Composite Carrier System

The hydrocarbon migrates in the basin along the composite carrier system in 3D space. However, at present, our Migmod simulation software can only simulate two-dimensional hydrocarbon migration and accumulation and cannot couple the amount hydrocarbon for migration, migration dynamics and carrier system in three-dimensional space to simulate three-dimensional hydrocarbon migration and accumulation. Therefore, this study adopted a method of establishing multiple stepped plane models (Fig. 3.47) to describe the composite conduit system in three-dimensional space.

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5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

Fig. 5.48 Composite carrier system models in the eastern part of the southern slope of Dongying Sag

According to the assemblage of the composite carrier system in the main reservoirforming periods, the area of hydrocarbon expulsion of source rock, sandstone carrier beds and faults were combined and connected to construct nine composite carrier system models topologically expanded on the plane, and the transport capabilities of sandstone carrier beds and faults in the carrier system were quantitatively characterized by permeability (Fig. 5.48). In the figure, different colors are used to describe the transport capabilities of corresponding to sandstone carrier beds and faults. The transport capabilities of the sandstone carrier beds are expressed by a green color gradation, from light to dark, and the permeability of the carrier beds was reduced from 3000 × 10–3 μm2 to 0.5 × 10–3 μm2 . The fault transport capabilities are expressed by a blue color gradation, and a lighter color indicates a better carrying capability. Eight grades were divided from light to dark, and the corresponding fault equivalent permeability increased from 10,000 × 10–3 μm2 to 0.05 × 10–3 μm2 . Two composite carrier system models were selected as examples to introduce. Model 1 is composed of the source rocks in the Es3 3 , the carrier beds in the Es3 2 , Es3 1 and ten faults (Fig. 5.48a). The area to the north of the F6 fault was the source rocks in the Es3 3 . The sandstone carrier bed of the Es3 1 is located between the F6 and F8–F3–F5 faults, and that of the Es3 1 is located to the south of the F8–F3–F5 faults. The sandstone carrier beds of the Es3 1 and Es3 2 acted as lateral migration conduits, The F-6 was the conduit for hydrocarbons expelled from source rock in the Es3 3 migrating upward to sandstone carrier bed of the Es3 2 , while the F3, F5, and F8 faults were vertical migration conduits for hydrocarbons in the Es3 2 moving upward to the Es3 2 . The F1, F2, F7, and F10 faults were the segments in the carrier bed of the Es3 2 , and the F9 and F4 faults were the segments in the carrier bed of the Es3 1 . Model 2 is composed of the carrier beds in the Es3 2 , Es3 1 , and Es2 and ten faults (Fig. 5.48b). The north of F6 fault is the carrier bed in the Es3 2 , and the carrier bed in the Es3 1 is located between F6 and F8-F3-F5 faults, and the south of F8-F3-F5 faults is the carrier bed in the Es2 . The sandstone carrier beds in the Es3 1 , Es3 2 and Es2 were the conduits for hydrocarbons migration laterally, and the F6, F8, F3 , and F5 faults were the conduits for hydrocarbons migration vertically. The hydrocarbons in the Es3 2 were migrated upward into the Es3 1 through fault F6, and that in the Es3 1

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375

were moved upward into the Es2 through F8, F3, and F5 faults. The F1, F2, F7, and F10 faults were the segments in the carrier bed of the Es3 1 , and the F9 and F4 faults were the segments in the carrier bed of the Es2 . In summary, permeability can be used to quantitatively characterize the transport capabilities of the above nine carrier system models composed of carrier beds and faults.

5.5 Evaluation of Hydrocarbon Migration and Accumulation Efficiency and Prediction of Favorable Area The purpose of hydrocarbon migration and accumulation research is to quantitatively study the pathways of hydrocarbons migration and accumulation and predict the possible location of hydrocarbon resources in basins so as to provide scientific guidance for oil and gas exploration. The exploration and research results confirmed that the hydrocarbon resources in the Paleogene and Neogene in the eastern part of the southern slope of the Dongying Sag are rich, and it has great exploration potential. However, to evaluate the undiscovered hydrocarbon resources, predict favorable exploration targets, and improve exploration efficiency is one of the key problems to be solved in areas with high exploration degree. On the basis of previous research results including the amount of hydrocarbon generation and expulsion, the fluid potential field, quantitative characterization of the composite carrier system, the oil loss during secondary migration and the amount of industrial oil resources were estimated the hydrocarbon migration and accumulation efficiency and the evaluated by the methods proposed in Chap. 4. Using self-developed Migmod hydrocarbon migration and accumulation simulation software (Luo, 2011; Luo et al., 2007a), the amount of hydrocarbon expelled from source rocks, hydrocarbon migration dynamics (the fluid potential field) and composite carrier systems (resistance) were coupled to simulate hydrocarbon migration pathways and estimate the amount of hydrocarbons available for accumulation in the pathways in the eastern part of the southern slope of the Dongying Sag, and then the favorable exploration target areas were predicted.

5.5.1 Estimation of Hydrocarbon Loss During Migration and Accmulation The hydrocarbons expelled from source rocks migrated into traps through carrier systems to form reservoirs, and the tectonic activities would lead to the destruction or adjustment of the reservoirs. There will be loss of hydrocarbons during the whole process. Estimation the amount of hydrocarbon loss in these processes is important

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for hydrocarbon resources evaluation by using a material balance method and it is also important to evaluate hydrocarbon migration and accumulation efficiency. According to the petroleum geological conditions, and the tectonic evolution history and hydrocarbon generation and expulsion history in the study area, the regional cap rocks of the Es3 2 had been formed before the hydrocarbons migration and expulsion in the late Dongying period., In addition to the regional cap rocks of the Es3 2 and Es1 , the regional cap rocks of Minghuazhen had also been formed before the hydrocarbons migration and accumulation in the late Minghuazhen period. The cap rocks in the Es3 2 , Es1 , and Nm could have played a good role in sealing and preventing hydrocarbon loss when hydrocarbon migration and accumulation happened. Most of the hydrocarbons were formed during the periods from Guantao to Minghuazhen, and the regional tectonic activity was weak thereafter, and the cap rocks did not destroyed by tectonic activity. Hydrocarbons were not lost on a large scale due to a lack of damage of effective cap rocks. Therefore, the loss during hydrocarbon migration and accumulation mainly included the loss during secondary migration and the loss of nonindustrial accumulated hydrocarbons.

5.5.1.1

Estimation of Loss During Secondary Migration of Hydrocarbons

The hydrocarbon loss during secondary migration of the Paleogene Shahejie Formation of the study area was estimated by the method in Chap. 4. (1) Parameters for estimation of hydrocarbon loss during migration The hydrocarbon migration and accumulation system is the basic unit for calculating migration loss. The study area mainly includes the southern part of the Niuzhuang Subsag and the southern slope (Fig. 5.1). Because of the inheritance of basin subsidence and sedimentation processes, each composite carrier model in the study area can be regarded as a migration and accumulation unit. The hydrocarbon expulsion area (S t ) and boundary width (W ) of the source rocks were determined according to the contour maps of hydrocarbon expulsion intensity of the Es3 3 and the Es4 1 at the end of the Minghuazhen period (Figs. 5.18 and 5.19). The effective source rock area in the Es3 3 in the southern slope migration and accumulation system was 378 km2 , and the width of the hydrocarbon supply boundary was 44 km. The effective source rock area in the Es4 1 was 461 km2 , and the width of the hydrocarbon supply boundary was 45 km. The hydrocarbon migration distance (L D ) in the carrier bed outside the source area was mainly determined according to the distribution of discovered hydrocarbon reservoirs in each interval of the Shahejie Formation. Figure 5.49 shows the distribution of discovered hydrocarbon reservoirs and the hydrocarbon sources in different intervals of the study area. In the southern slope migration and accumulation unit, the reservoirs in the Es3 3 and the Es3 2 are all distributed within the hydrocarbon expulsion area of source rocks, mainly the oil source of the Es3 3 , while the reservoirs in the Es2 Es3 1 , and the Es4 1 are widely distributed outside the hydrocarbon expulsion

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377

area. The reservoirs in the Es2 outside the source area include the W14-8 and G7 blocks. The oils in W14-8 block was the mixed source from the Es4 1 and the Es3 3 , while that in the G7 block was from the source rock in the Es3 3 . The reservoirs in the Es2 farthest from the source area are located in the Le’an Oilfield, in which the oil mainly migrated from the source rocks in the Es4 1 (Zhang et al., 2003). The reservoir is approximately 11 km from the hydrocarbon expulsion boundary. Most reservoirs in the Es3 1 are far from the source area, including the W140 block of the Wangjiagang Oilfield, Bamianhe Oilfield and Le’an Oilfield, mainly derived from the source rocks in the Es4 1 . The reservoir farthest from the hydrocarbon expulsion boundary is located in the Bamianhe Oilfield, with a migration distance of approximately 11 km. The reservoirs in the Es4 1 far from the source area mainly include the W73 block, Bamianhe Oilfield and Le’an Oilfield, in which the oils were mainly from the source rocks in the Es4 1 . The reservoir farthest from the hydrocarbon expulsion boundary is located in the Bamianhe Oilfield, with a migration distance of approximately 14 km. The sand body thickness (h) within the hydrocarbon expulsion area was derived from the statistics of interpretation results of logging data in the study area. The results show that the average number of sand body layers in the Es4 1 is 10, and that in the Es3 3 is 2, and the number of sand body layers in the Es3 2 is 5. The thickness of a single sand body layer in each interval varies greatly, generally between 0.2 and 3 m. The cumulative thickness contour maps of the sandstone carrier beds were obtained according to the interpretation results of logging data, with reference to sedimentary facies distribution (see Fig. 5.27).

Fig. 5.49 Distribution of discovered reservoirs and the oil source in different intervals in the eastern part of the southern slope of the Dongying Sag

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5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

The height of the lateral migration path (d l ) was mainly calculated by analyzing the similarity between previous laboratory simulations (Thomas & Clouse, 1995; Yan et al., 2012a, 2012b) and the actual geological conditions in the study area using the scaling formula from the experimental to the basin scale proposed by Rapoport (1955) (see Formula 4.25 in Chap. 4 for details), and the height of the lateral migration path was calculated to be approximately 1 m. The migration pathway saturation of vertical migration (S c , the ratio of saturation pathway volume to the total volume of carriers) in the hydrocarbon expulsion area was determined according to the research results of Hirsch and Thompson (1994). This study assumed that the shape of independent vertical migration units (IMU) was a cube, and the characteristic side length was the average thickness of sandstone above the source rock (that of the Es4 1 was 22.6 m), the number of independent migration and accumulation units within the hydrocarbon expulsion area of the source rocks was approximately 9 × 105 , and the value of S c of the vertical migration pathway was obtained as 6.6%. The average sand body thickness of the Es3 3 was 13.5 m, the number of independent migration and accumulation units was 2.5 × 106 , and the value of the vertical migration pathway was 8.7%. The average thickness of the sand body in the Es3 2 was 82 m, the number of independent migration and accumulation units is 7.2 × 104 , and the value of vertical migration pathway was 3.5%. According to the simulation results of Luo et al., (2007a, 2007b), the migration pathway saturation of hydrocarbon lateral migration (Sl' , the ratio of saturation pathway volume to the total volume of carriers) in the hydrocarbon expulsion area was determined. When the number of independent migration and accumulation units was sufficient, the value of S l ’ gradually tends to a constant, approximately 16%. The number of independent migration and accumulation units in the source rocks in the Es3 2 of the study area was 7.2 × 104 , which is the lowest; therefore, the value of Sl' in the hydrocarbon expulsion area was 16%. The density of crude oil in different intervals were obtained through the statistics of the measured results. The density of the crude oil in the Es4 1 was about 0.874 g/cm3 , and those of the Es3 3 , Es3 2 , Es3 1 , and Es2 were about 0.88 g/cm3 , 0.879 g/cm3 , 0.874 g/cm3 , and 0.905 g/cm3 , respectively. The residual oil saturation in pathways (S r ) was determined according to the statistical relationship between the porosity and the migration pathway residual oil saturation established by Luo et al. (2008) using experimental data. Please see Sect. 4.2 of Chap. 4 for details. (2) Estimation of hydrocarbon loss during secondary migration The hydrocarbon loss during secondary migration in the slope migration system in the southern part of the study area was calculated by Formula (4.20) on the basis of the above-mentioned parameters, and the contour maps of hydrocarbon loss intensity of each interval of were obtained. The amount of hydrocarbon loss during migration in the Es3 1 and the Es2 was extremely low, and which were mainly in the Es4 1 , Es3 3 . and Es3 2 (Fig. 5.50). Figure 5.50a shows the hydrocarbon loss intensity during migration in the Es4 1 . The hydrocarbon loss intensity in most areas was between 10 × 104 and 40 ×

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379

Fig. 5.50 Contour maps of hydrocarbon loss intensity during secondary migration of main intervals in the study area. a Es4 1 , b Es3 3 , c Es3 2

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5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

104 t/km2 . The loss intensity was greater than 60 × 104 t/km2 in the northeastern of the study area. The amount of hydrocarbon loss of secondary migration south of the study area was extremely low and could be neglected. The total hydrocarbon loss during migration of the Es4 1 in the southern slope migration and accumulation system was approximately 1.55 × 108 t. The hydrocarbon loss intensity of the Es3 3 during migration was, generally less than 40 × 104 t/km2 (Fig. 5.50b), and that of south of the study area was extremely low and almost negligible. The total hydrocarbon loss during the secondary migration in the Es3 3 in the southern slope migration and accumulation system was approximately 0.91 × 108 t. The hydrocarbon generation intensity in the center of Niuzhuang Subsag was relatively high, generally greater than 40 × 104 t/km2 , and gradually decreases from the center to the periphery (Fig. 5.50c). That of south of the study area was extremely low and almost could be ignored. The total hydrocarbon loss during the secondary migration in the Es3 2 in the southern slope migration and accumulation system was approximately 1.6 × 108 t.

5.5.1.2

Estimation of Nonindustrial Hydrocarbon Accumulation

The amount of nonindustrial hydrocarbon accumulation was estimated by the reservoir scale sequence method (Jin & Zhang, 1999; Masters, 1993). At present, four oil fields, including Wangjiagang, Niuzhuang, Bamianhe and Le’an, have been discovered in the study area, and they are mainly distributed in the southern migration and accumulation system. As of 2008, about 21 reservoirs had been reported in the Wangjiagang Oilfield, with proven reserves of 4680 × 104 t. There were about 61 discovered reservoirs in the Niuzhuang Oilfield, with proven reserves of 10,162.89 × 104 t. There were about 46 proven reservoirs in the Bamianhe Oilfield, with proven reserves of 13,778 × 104 t. There were about 11 proven reservoirs in the Caoqiao-Le’an Oilfield, with proven reserves of 12,894 × 104 t. Using the reserve data of these discovered reservoirs, the reservoir scale sequence was established according to the Pareto principle, and the reserves in the southern block of the Le’an Oilfield were the largest, which was 2683 × 104 t. The reservoir scale sequence was expanded according to the angle step size of 5° in a range of K = tg25°–tg65°, where the standard deviation σ = 0.00058 when K = 1; therefore, this value was selected to predict the reservoir scale sequence. Figure 5.51 shows the predicted reserves in the southern slope area of the Niuzhuang Subsag. Based on an economic evaluation of the Shengli Oilfield according to the oilfield development cost (Pan et al., 2003), taking the minimum economic reservoir reserve Qmin = 10 × 104 t, using Formula (4.30) in Chap. 4, it is predicted that the amount of nonindustrial accumulated hydrocarbons in the migration and accumulation system of the southern slope in the study area is 4.83 × 108 t.

Geological Reserves (10,000 tons)

5.5 Evaluation of Hydrocarbon Migration and Accumulation Efficiency …

381

6000 Predicted Reserves Actual Reserves

5000 4000 3000 2000 1000 0 0

50 100 Oilfield Scale Serial Number

150

200

Fig. 5.51 Predicted reserves by reservoir scale sequence scale sequence method in the eastern part of the southern slope area of Dongying Sag

5.5.2 Evaluation of Hydrocarbon Resource The amount of hydrocarbon available for accumulation was obtained by subtracting the estimated amount hydrocarbon loss during migration from the amount of hydrocarbon expelled from source rocks (Table 5.11). Figure 5.52 shows contour maps of the intensity of hydrocarbon available for accumulation intensity of the Es4 1 , the Es3 3 , and the Es3 2 . As shown in the figure, the intensity of hydrocarbon available for accumulation of the Es4 1 in the Niuzhuang Subsag and the northwest of the study area was relatively high, generally greater than 90 × 104 t/km2 , up to 90 × 104 t/km2 (Fig. 5.52a). There were no hydrocarbons for accumulation in the south and southeast of the study area (Fig. 5.52a). The total amount of hydrocarbons available for accumulation was about 4.29 × 108 t of the Es4 1 in the study area. The intensity of hydrocarbon available for accumulation of Table 5.10 Geochemical parameters of crude oil samples in section Sample well

G/(G + C30 hopanes)

4-Methyl sterane/C29 regular sterane

Dinosterane/C29 regular sterane

Pr/Ph

C35 /C34 homohopane

Crude oil type

W542

0.17

0.66

0.17

0.43

0.59

I

NZW542-2

0.21

0.56

0.19

0.46

0.6

I

W631

0.23

0.7

0.20

0.42

0.65

I

WJW3-10

0.25

0.56

0.16

0.38

0.71

I

WJTN61-613

0.27

0.63

0.21

0.42

0.74

I

WJW14-51

0.44

1.0

0.46

0.38

1.06

III

W21

0.51

1.1

0.48

0.31

1.36

II

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Table 5.11 Evaluation of hydrocarbon resources in the migration and accumulation system of the southern slope of the study area Horizon

Hydrocarbon expulsion amount (× 108 t)

Migration loss Amount of amount (×108 t) hydrocarbon available for accumulation (×108 t)

Non-industrial Resource accumulation amount (× hydrocarbon 108 t) 8 amount (×10 t) 4.83

Es4 1

5.84

1.55

4.29

Es3 3

5.71

0.91

4.8

Es3 2

4.06

1.60

2.46

6.73

the Es3 3 in the northwest of the study area was relatively high, generally greater than 40 × 104 t/km2 , up to 140 × 104 t/km2 (Fig. 5.52b). There were no hydrocarbons for accumulation in the south and east of the study area (Fig. 5.52b). The total amount of hydrocarbons available for accumulation was approximately 4.8 × 108 t of the Es3 3 in the study area. The intensity of hydrocarbon available for accumulation of the Es3 2 in the northwest and north of the study area was relatively high, generally greater than 70 × 104 t/km2 , up to 210 × 104 t/km2 in the northwest (Fig. 5.52c). There were no hydrocarbons for accumulation in the south and southeast of the study area (Fig. 5.52c). The total amount of hydrocarbons available for accumulation was approximately 2.46 × 108 t of the Es3 2 in the study area.

Fig. 5.52 Intensity of hydrocarbon available for accumulation in each interval in the eastern part of the southern slope of Dongying Sag. a Es4 1 , b Es3 3 , c Es3 2

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383

Furthermore, the amount of hydrocarbon available for accumulation was obtained by subtracting the estimated amount of hydrocarbon loss during migration from the amount of hydrocarbon expelled from source rocks (Table 5.10). The amount of industrial hydrocarbon accumulation in the migration and accumulation system in the study area was obtained by subtracting nonindustrial accumulation hydrocarbons from the hydrocarbon available for accumulation (Table 5.10). The cumulative amount of hydrocarbon expulsion from source rocks is 15.61 × 108 t, the loss amount during migration is 4.06 × 108 t, and the amount of nonindustrial accumulation hydrocarbons is 4.83 × 108 t, and the amount of industrial hydrocarbon resources in the migration and accumulation system in the study area is 6.73 × 108 t.

5.5.3 Distribution of Hydrocarbon Migration Pathways and Migration and Accumulation Amount On the basis of the estimation of the hydrocarbon loss amount during migration, the amount of hydrocarbon available for accumulation, the pathways of hydrocarbon migration and accumulation in the study area were quantitatively simulated by the Migmod hydrocarbon migration and accumulation simulation software, which coupled the amount of the intensity of hydrocarbon available for accumulation, migration dynamics (fluid potential field) and the capillary resistance in composite carrier system in the key reservoir-forming period, and the simulation results were verified by the discovered reservoirs. According to the simulation results of pathways of hydrocarbon migration and accumulation, the favorable exploration target were selected and evaluated in the study area. In the actual simulation study, we simulated and analyzed the migration and accumulation of hydrocarbons according to the 10 conduit framework models established in Sect. 5.4.3 of this chapter and selected 2 representative simulation results to show the characteristics of the dominant migration pathways of hydrocarbons in the composite conduit framework of the study area and the distribution of potential hydrocarbon resources. The pathways of hydrocarbon migration and accumulation shown in Fig. 5.53 was the simulation results based on the two representative composite carrier system models. The hydrocarbons expelled from the source rocks in the Es4 1 migrated upward into the carrier bed in the Es3 2 through the F7, F1, and F10 faults and then migrated southward along multiple pathways in the Es3 2 (Fig. 5.53). When migrating to the F8, F5, and F3 faults, hydrocarbons migrated vertically through the faults into the carrier bed in the Es3 1 and continued to migrate southward in the carrier bed of the Es3 1 . Five dominant migration pathways were formed from west to east, including those along N8–G7–TG1–W732, W133–W12–W93–C26, M6– M2–M11, G127–G16–C62, and M26–G115–TG11. The migration and accumulation efficiency of the pathways along N8–G7–TG1–W732 and W133–W12–W93–C26 were relatively high, with a migration amount of 0.5 × 108 t.

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5 Study of the Hydrocarbon Migration and Accumulation Dynamics …

Fig. 5.53 Map showing simulated secondary migration pathways and discovered reservoirs of model 1 in the study area. The zigzag lines, whose colors vary from black to red and yellow, are migration pathways, solid red lines outline discovered oil fields

Another simulation results of migration and accumulation pathways was shown in Fig. 5.54. The hydrocarbons from source rocks were migrated into the turbidite sand bodies in the Es3 2 within the hydrocarbon expulsion area of the Niuzhuang Subsag to form multiple migration and accumulation areas. Although the total amount of hydrocarbon was generally less than 0.3 × 108 t, there were many hydrocarbon migration pathways with wide distribution. The hydrocarbons in the Es3 2 migrated vertically through the F6 fault into the carrier bed in the Es3 1 , and then continued to migrate southward to form four dominant pathways, including the pathways along N8–G12–G102 block, N18–N3, N12–G104, and T61–T56. When hydrocarbons migrated vertically along the F8 fault, F5 fault and F3 fault to the carrier bed in the Es2 , the hydrocarbons continued to migrate southward and there was no obvious change in the extension direction and migration flux of the four pathways. Combining the simulation results of hydrocarbon migration pathways of all 10 composed carrier system models, it was found that there exist differences among migration pathways in different carrier beds. As a result, the oil and gas were vertically accumulated in multiple layers in the study area.

5.5 Evaluation of Hydrocarbon Migration and Accumulation Efficiency …

385

Fig. 5.54 Map showing simulated secondary migration pathways and discovered reservoirs of model 2 in the study area. The zigzag lines, whose colors vary from black to red and yellow, are migration pathways, solid red lines outline discovered oil fields

5.5.4 Prediction and Evaluation of Favorable Exploration Target Areas According to the quantitative simulation results of hydrocarbon migration and accumulation pathways, in combination with the comprehensive analysis of the geological factors controlling the formation of hydrocarbon reservoirs in the study area, the potential favorable exploration target areas in the Shahejie Formation were predicted.

5.5.4.1

Prediction of Favorable Target Areas

According to the previous understanding of Tertiary hydrocarbon accumulation conditions in the Dongying Sag (Li & Pang, 2004; Jiang et al., 2007), the Shahejie Formation in the study area has sufficient hydrocarbon sources, a large-area distribution of high-quality reservoir sand bodies, good reservoir–caprock assemblage conditions, and widely developed various types of traps. The formation and enrichment of hydrocarbon reservoirs mainly depend on the effectiveness and scale of traps, fault carriers and sandstone carrier beds connecting hydrocarbon sources and effective traps.

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Fig. 5.55 Map showing simulated secondary migration pathways and prediction of favorable exploration areas of Shahejie formation in the eastern part of the southern slope of Dongying Sag (modified from Lei et al., 2014) (I. N8–G12–GX15 well block; II. G9–G2 well block; III. W662–WX731 well block; IV. W93–T11 block; V. W125–123–M6 well block)

Therefore, based on the simulation results of hydrocarbon dominant pathways and hydrocarbon resource distribution, in combination with the comprehensive analysis of the geological factors controlling the formation of hydrocarbon reservoirs in the study area, five favorable targets were predicted for the further hydrocarbon exploration (Fig. 5.55).

5.5.4.2

Drilling Results of Favorable Targets

The drilling results of favorable exploration targets are important indicators for evaluating the reliability and validity of the method of quantitative dynamic study of hydrocarbon migration. After the completion of this study, Wells G2-X1 and G2-X2 were drilled in the predicted II favorable target areas in the G9–G2 well block in 2010. The drilling results revealed that a large number of oil and gas displays had been found in the Es2 and Es3 1 , and industrial oil flow had been obtained from oil testing results. The newly drilled W90 well in the predicted IV favorable target area also reveals industrial oil flow in the Es2 , and the proven geological reserves were approximately 136 × 104 t, confirming the reliability of the prediction results of favorable targets. Since then, more oil reservoirs were discovered in other predicted

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favorable target areas, and the cumulative proven oil geological reserves exceed 4000 × 104 t. According to actual drilling results, the research results of hydrocarbon migration and accumulation dynamics carried out in the southern slope area of the Dongying Sag have high credibility and good application effects. The results can provide not only a scientific basis for the next pre-exploration deployment in this area but also important methods and technical support for hydrocarbon exploration in other basins. The later exploration discoveries in the predicted favorable exploration areas show that the pathways of hydrocarbon migration predicted by the method of quantitative dynamic study of hydrocarbon migration were reliable. And the favorable exploration targets predicted based on the simulated hydrocarbon migration pathways was helpful to exploration.

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