Economic Decision Analysis: For Project Feasibility Studies (SpringerBriefs in Petroleum Geoscience & Engineering) 3030961362, 9783030961367

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SPRINGER BRIEFS IN PETROLEUM GEOSCIENCE & ENGINEERING

Babak Jafarizadeh

Economic Decision Analysis For Project Feasibility Studies 123

SpringerBriefs in Petroleum Geoscience & Engineering Series Editors Jebraeel Gholinezhad, School of Engineering, University of Portsmouth, Portsmouth, UK Mark Bentley, AGR TRACS International Ltd, Aberdeen, UK Lateef Akanji, Petroleum Engineering, University of Aberdeen, Aberdeen, UK Khalik Mohamad Sabil, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, UK Susan Agar, Oil & Energy, Aramco Research Center, Houston, USA Kenichi Soga, Department of Civil and Environmental Engineering, University of California, Berkeley, USA A. A. Sulaimon, Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

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Babak Jafarizadeh

Economic Decision Analysis For Project Feasibility Studies

Babak Jafarizadeh Institute of Geo-Energy Engineering Heriot-Watt University Edinburgh, UK

ISSN 2509-3126 ISSN 2509-3134 (electronic) SpringerBriefs in Petroleum Geoscience & Engineering ISBN 978-3-030-96136-7 ISBN 978-3-030-96137-4 (eBook) https://doi.org/10.1007/978-3-030-96137-4 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Introduction

Which course of action should we take? It is a common real-world problem. It appears in different guises and contexts, but they all have features in common. To answer, we should compare the costs and benefits and choose the best course of action. Yet often these courses are complex and have uncertain outcomes. In this book we discuss a narrow subset of this problem. We discuss the question: which course of action should we take for an energy project? Still, there are no simple answers. Energy projects are often costly, technically complex, and uncertain. In this book, we discuss a workable solution based on logical thinking and clear understanding. To improve our understanding, we further use concepts from economics, corporate finance, and decision analysis. Chapter 1 discusses the fundamentals. Anyone working in industry either makes decisions or supports others who make decisions. For any project, all we do is making decisions. It could be a series of decisions or a major decision nesting all. Should we undertake a project or not? To recommend a course of action, we need consistent valuations. Therefore, all valuations are for making decisions. Value outside the context of decisions is meaningless. Chapter 2 discusses the concepts in more details. We make decisions because we want to achieve our goals. Yet, we live in an uncertain world and the outcome of our decisions would be uncertain. We describe projects’ uncertainty using the language of probability. Our description makes the decision models. Still, they cannot (and should not) reflect all aspects of the real world. We should simplify. Our models should be simple enough (and realistic enough) to be useful. All models are approximations. In Chapter 3, we discuss key approximations for economic decision models. Finance theory shows the relationship between uncertainty and value. It tells us what we should expect in return for taking economically risky opportunities. This understanding is key to a consistent comparison of our courses of action. We will discuss that project decisions should be for the benefit of their real owners, the investors. Chapter 4 applies the key concepts from earlier chapters to real-world problems. Often these applications have subtleties that traditional models ignore. Therefore, we discuss how to reflect a further aspect of the real world in our decision models. v

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The common theme in our discussions would be consistency of implementation and clear understanding. We use concepts of real options and dynamic decision making to enhance our models of project appraisal.

Contents

1 Project Feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Big Bets of the Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Valuation for Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Finance Theory and Single Objective of a Firm . . . . . . . . . . . 1.2.2 Valuation in Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 The Decision Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Precise Decision Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Do Not Expect the Expected . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Analyses are Transitory (and Subjective) . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Soft versus Hard Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Decision Makers’ Bias in Valuations . . . . . . . . . . . . . . . . . . . . 1.5 The Journey is Important . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Extinct by Instinct versus Analysis Paralysis . . . . . . . . . . . . . 1.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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

2 Understanding the Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Geological Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Division of Certainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Probability Tree Reversal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Challenges in Eliciting Information . . . . . . . . . . . . . . . . . . . . . 2.2.4 Multiple Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Compound Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Uncertainty in Upstream Value Chain . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Errors of the Third Kind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 The Chance of Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Value of Appraisal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Minimum Economic Volume . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.5 Development Scheme: From Reservoir to Market . . . . . . . . .

13 13 14 15 17 19 20 23 24 25 25 27 29 30

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2.3.6 Joint Development Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.7 The Market Determines the Fate of the Projects . . . . . . . . . . 2.4 “All Models Are Wrong, Some Are Useful” . . . . . . . . . . . . . . . . . . . . 2.4.1 The Time Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Geologically Correlated Opportunities . . . . . . . . . . . . . . . . . . 2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

32 34 35 36 37 38 39

3 Economics of Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Relative Valuations and the Law of One Price . . . . . . . . . . . . 3.1.2 Black Swans in the Energy Industry . . . . . . . . . . . . . . . . . . . . 3.2 Comparisons Across Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Valuing a Series of Cash Flows . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Comparisons Across Different Levels of Uncertainty . . . . . . . . . . . . 3.3.1 Define Goals from the Point of View of Investors . . . . . . . . . 3.3.2 Market Data to Measure Risk and Return . . . . . . . . . . . . . . . . 3.3.3 Beta and the Capital Asset Pricing Model . . . . . . . . . . . . . . . . 3.4 Company’s WACC and the Project Discount Rate . . . . . . . . . . . . . . . 3.4.1 WACC for Company Valuation . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Project Discount Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Influence Costs, Hurdle Rates, and Experience . . . . . . . . . . . 3.5 Making Valuation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 The Cash Flow Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 The Sunk Cost of an Exploration License . . . . . . . . . . . . . . . . 3.5.3 Valuation and Behavioral Issues . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4 The Dilemma of Energy Projects . . . . . . . . . . . . . . . . . . . . . . . 3.5.5 Making Models: Granularity . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.6 Making Models: Flaw of Averages . . . . . . . . . . . . . . . . . . . . . 3.6 Commodity Markets and Their Valuation Insights . . . . . . . . . . . . . . . 3.6.1 Uncertain Prices in Corporate Culture . . . . . . . . . . . . . . . . . . . 3.6.2 Price Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 Hedging Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.4 Alternative to Project WACC: Certain-Equivalent Cash Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.5 Market vs. Technical Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 41 42 43 43 44 45 46 47 49 51 52 53 54 55 55 57 58 59 59 62 64 64 65 66

4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Non-linearity from Capacity Constraints . . . . . . . . . . . . . . . . 4.1.2 Real Options (Value of Flexibility) . . . . . . . . . . . . . . . . . . . . . 4.2 Prices Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Randomly Walking or Mean-Reverting Prices . . . . . . . . . . . . 4.2.2 Dynamics of Price Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . .

71 71 72 75 77 81 84

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4.2.3 Price Forecasts in Mean-Reverting Framework . . . . . . . . . . . 85 4.2.4 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.3 Real Options and Price Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.3.1 The Option to Wait . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.3.2 Sequential Exploration and Uncertain Prices . . . . . . . . . . . . . 89 4.4 Environmental Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.4.1 Emission Taxes and Project Feasibility . . . . . . . . . . . . . . . . . . 96 4.4.2 Carbon Accounting and the Single Objective of a Firm . . . . 99 4.4.3 Market Mechanism for Emission Control . . . . . . . . . . . . . . . . 101 4.5 Conclusions: Useful Analyses and Common Pitfalls . . . . . . . . . . . . . 102 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Chapter 1

Project Feasibility

Abstract This chapter discusses the fundamentals. Anyone working in industry either makes decisions or supports others who make decisions. For any project, all we do is making decisions. It could be a series of decisions, or a major decision nesting all: Should we undertake a project or not? To recommend a course of action, we need consistent valuations. Therefore, all valuations are for making decisions. Value outside the context of decisions is meaningless.

1.1 The Big Bets of the Industry While traveling across Oregon Trail in the 1850s and passing through where today is Northern Wyoming, American pioneer travelers could buy a lubricant made of flour and bitumen to grease their wagon axles. This was a brilliant business idea. Natural oil seeps in this remote and unpopulated region were too far away from population centers to sell as medicinal oil—the primary market of crude oil at the time. Yet minor entrepreneurs of the day made a product that turned bitumen into money. Here, not for the first time, the story of petroleum connected with economics. Later in the early twentieth century, in another turn of events, the popularity of internal combustion engines brought crude oil into spotlight. The region’s petroleum exploration activities escalated and with several oil discoveries, for a time, the city of Casper in Wyoming became the “oil capital of the West”. The economics had changed. With an established market for crude and an abundant source of supply, distance was less of a barrier. The restriction was now in technology—the high-Sulphur oil discoveries were hard to sell with the refining technology of the day. Besides economics, from early days the story of exploration connected with technology.

Supporting Material The Supplemental material (including spreadsheet models of the examples discussed in this document) are available for download in the link below: https://www.dropbox. com/sh/oudm7mamy39aa94/AAAVN_SituocIsiKIt340Osla?dl=0

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Jafarizadeh, Economic Decision Analysis, SpringerBriefs in Petroleum Geoscience & Engineering, https://doi.org/10.1007/978-3-030-96137-4_1

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With those technology barriers, few could believe that the energy saga of Wyoming could extend to the twenty-first century. Yet, markets grew stronger, innovation removed technical barriers, and the inexorable forces of economy drove exploration and production further in the region. Even the recent production decline in Salt Creek—the major petroleum reservoir in this basin—was not a sign of its end. Advances in horizontal drilling and hydraulic fracturing reinvigorated production and renewed the expectations from exploration. Under the new economics, some previously unattractive prospects are now drillable. The story of Wyoming oil is not unique. We have used it as a narrative to discuss general principles of hydrocarbon business—or for that matter, any resource business. Like in Wyoming, any decision rests on the economics of the projects—a function of technology, product prices, costs, and the governments’ attitude. In addition, success depends on luck. As drilling for oil and gas is an uncertain venture, we could always be the lucky winner or the unlucky loser, no matter how promising the prospects are. Everywhere in the world, petroleum exploration is a bet on geology and economy. Nevertheless, many companies (and some individuals) bet on these multi-milliondollar projects. Every year, they lose millions of dollars on dry holes. All the same, some make millions on discoveries. What drives these exploration decisions? Moreover, what is a good exploration decision? In this book, we introduce and discuss a consistent procedure for valuation and decision-making. Our suggestion is not a panacea. We just do not know of any better approach. For uncertain ventures, no procedure could guarantee best outcomes. Failure is part of the game. Yet, consistency and transparency will go a long way and eventually generate better results. We discuss models and methods to support good decision-making. For only by making good decisions can projects create monetary value. We address valuation for decision making, as opposed to valuation in compliance with regulations. Regulations too are born out of necessities; somebody at some point in time realized that consistent valuations are essential for good decision-making and made them required. However, it is easy to lose sight and perform valuations just for the sake of valuations. Here, we oppose that view. We introduce a process for economic decision analysis and discuss its implications for creating value through improved decisions. In addition, we argue that the only approach to long term value creation from exploration investments is a consistent analysis process leading to clarity and transparency in decision making.

1.2 Valuation for Decision Making Through exploration and development projects, companies produce hydrocarbons and sell them in the markets. Yet, finding resources within the earth’s crust and taking them to market is not trivial; it may take many attempts, often at great costs, to find hydrocarbons. Once found, it takes even more time and effort to produce,

1.2 Valuation for Decision Making

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process, and deliver. It is a risky business, but many are prepared to take the bet for its expected benefits. Consider the time and efforts to think up oil prospects, say in the Powder River basin in Northern Wyoming, and then to justify drilling them given limited information. Depending on how we interpret the past, the average rate of success in the basin (based on activities during 1956–1976) was slightly short of 20%. On average, only one in five exploration wells succeeded [1]. Is any discovery commercial? Obviously, spending a hefty sum of money to gain a bucket of oil (traditionally, the smallest amount for an “oil find”) is not. The answer lies in how much, how costly, and at what selling price we could produce the discovered volumes. As these conditions change, so do the economics of projects. For example, advances in horizontal drilling and hydraulic fracturing could produce more out of a discovery, elevating the exploration economics. Changes in oil prices could as well change the business outlook for petroleum exploration. Exploration in Powder River basin is but one example of technical and economic factors swaying the decision to drill. These factors are uncertain and despite technological advances will remain uncertain. For the decision to drill into a prospect, we will never know the amount of producible hydrocarbon. No matter how much seismic or electro-magnetic data we gather. Even the amount of hydrocarbon in a producing reservoir stays uncertain—up until the very end, when we plug and abandon all wells. By then, and not before, will we know exactly how much we produced. In addition, we will always remain uncertain of future prices. Traditional price predictions never work, and surprises are inevitable. Lack of data and more fundamentally, lack of knowledge, results in uncertainty about the outcome of exploration decisions. Uncertainty is a cause of both disappointment and delight. Dry holes are unwelcomed news to most. Even those that praise dry holes for the information they provide, still admit the negative connotation of breaking the unwelcome news about them. All that preparation time, effort, and finances lost. On the other hand, discoveries that turned around companies’ fortunes were for the lucky strikes. Contrary to the common belief that superior skills make discoveries, chance plays a crucial role. Uncertainty has given rise to enterprises and professions that otherwise would not have existed. Who would want to work for, or invest in, a company that knows the result of all its future drillings?1 The main justification for any industrial activity should be generating expected value—a value forecast based on the uncertainties. Drillers and developers, whether individuals or organizations, run businesses. This needs a business mind-set. National oil companies on behalf of governments are also in it for the money, or business value. The value expectation is their guideline.

1

Even with the phrase “no pain no gain”, we normally do not want to know all our lifelong pains and gains. Knowing that some of us will gain for small pains, and some will have to endure large pains for small gains could make life appear unfair and unexciting. Ignorance is bliss. Uncertainty leads to hope that fuels exploring the unknowns.

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In this context, an oil discovery by itself does not guarantee value, even though the media often herald the success in exploration as the ultimate prize. It is always the expected value, and not the volume, which matters. For example, a gas find in a remote location may become worthless after considering the cost of storage and transportation. Making exploration decisions is challenging because it is uncertain whether they eventually create value.2

1.2.1 Finance Theory and Single Objective of a Firm Projects are about making decisions and decisions are our only way to intervene in the real world. Decision making without a goal is confusing. What purpose should the decision-making serve? In theory of corporate finance, the single goal of the firm (and the decisions they make) should be to maximize shareholder value. This applies to any industrial corporation. Shareholders are the owners and maximizing their value means creating as much wealth as possible for them. Any corporation should strive to achieve this goal, even if it entails taking risky positions or going against emotions. With this principle, any decision that increases expected value is a good decision—be it downsizing at unfavorable times, merging with other entities, or spending money on exploration and development. Yet, working with a single goal (while theoretically elegant) has also attracted criticism. Critics often mention the corporate responsibility to generate value for stakeholders, including government, creditors, the employees, and the society. If corporations neglect these stakeholders and leave them unsatisfied, then the theory should be short-sighted. The proponents of the theory answer most critics by mentioning the law of limited liability and ownership in corporations. Owners (shareholders) are not responsible for a companies’ liabilities. In this arrangement, the shareholders are the last in line to receive the benefits of the business. Being last in line means that, once the government deducts taxes, creditors receive loan payments, and employees receive salaries, then the shareholders receive their value. Maximizing shareholder value already satisfies the needs of other stakeholders.3 Even with the uncertainty in value creation, a competent company often outperforms its mediocre rivals. Everything else equal, successful companies are not necessarily those that “know” more. Long term success means they excel in the game of chance—using available information and making good decisions. In practice, nothing is equal. There are data asymmetries, regional and historical advantages, and even 2

Uncertainty signifies lack of knowledge about an outcome. With uncertainty, we could create value by changing course and taking advantage of the desired outcomes or to protect against the downsides. Uncertainty could both destroy or create value. On the contrary, risk is an unwanted effect, or the downside. We always want to mitigate or entirely remove the risks. 3 More discussions about the goal of a firm in corporate finance textbooks e.g., [2].

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Fig. 1.1 Good decisions are logically consistent with the objectives we prefer, alternatives we identify, and information we have. Yet this does not guarantee good outcomes

unequal government support. Still, playing with “the card they have been dealt”, a successful business calls for a systematic approach to valuation and decision-making. We often hear about good or bad decisions. For example, the news about failure in exploration drilling usually follows with endless comments like “how unexpected” this dry hole is, or that “the well should not have been dry”, or “we should not have drilled it in the first place”. It is as if the decision makers should see the future and have a sixth sense of foretelling. But we less often hear about good (or bad) decision making. The decision makers are all human. They have the limitations as everyone else. No one can see the future. We can only control our decisions—their outcome is often uncertain. The distinction between decisions and outcomes, what we control and what is uncertain, is at the heart of a sound analysis. We make good decisions as shown in Fig. 1.1. We consider the information and our preferences, we think of the alternatives, and we use sound logic to assess the situation. Even with good decisions, the uncertainty eventually affects the outcome. Bad decisions are illogical, or are inconsistent with information, preferences, and alternatives. Good decisions apply clear thinking to take clear actions. Then, what is a good approach to valuation and decision making for organizations? Again, it should be a transparent process of using the available information, assessing preferences, evaluating alternatives, and making good decisions. Compared to personal decisions, more people involve in an organization’s decision process. Therefore, clarity and transparency go a long way toward a successful business. Does this guarantee success? No, working in an uncertain world could always bring about surprises. Adverse outcomes are part of the game. All we can do is to make good decisions.

1.2.2 Valuation in Practice Economic value should be the basis of business decisions. Without value, decisionmaking is meaningless. Yet sometimes managers make decisions without relying on official valuation and blame it on lack of time or lack of relevant information. Is this an acceptable practice? We do not think it is acceptable. Our view on valuation is that we can measure and evaluate anything. Even the most obscure form of art (albeit not agreeable amongst

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all) has a defined value. Yet, when it is time to make important investment decisions, some opportunities do not carry a value tag. What makes managers exclude such opportunities from formal valuations? A common excuse in exploration and development business is lack of time and resources. Followed by “it is highly uncertainty” and “it lacks vital information for valuation”. Surprisingly, these are the very reasons that we need valuation in the first place. The value itself is not that important. It is not real money. Companies never publicly report project valuations. We merely need valuations as a basis for decision making. In any project, the clarity and decision insight that comes with the valuation process is the real benefit. From data scrutiny to assessment of uncertain outcomes, a consistent analysis could reveal less known, even previously ignored, alternatives that could eventually lead to value creation.

1.3 The Decision Model The story of valuation begins with the frame, a scope that defines the valuation problem within its technical and economic context. This is a crucial step because whatever follows will one way or another relate to this frame. Think of it as a distinction that separates the valuation problem from all the outside factors; something that helps you decide what goes into valuation. Without a proper frame, the formulation may become too narrow to be useful or too wide to be affordable. Consider for example a project consisting of drilling a production well into an oil-bearing formation. It will be a deviated well that the engineers hope will penetrate the right place in the formation. In the framing meeting, one manager argues that “while we are in the vicinity why not drilling a side–track well that explores a nearby prospect?” The proposed side–track costs only a fraction of an individual exploration well and does not introduce any added risks. The suggestion stirs up the discussion with many others pointing out the pros and cons of this sidetrack. Some praise the suggestion because they believe it leads to useful information at low cost. It would be good for business. Others call it a bad suggestion as it delays the much-needed production. They desperately need to generate income as soon as possible and fill the empty export pipeline. Before the discussion is confused, they’d better refer to the frame. A proper frame clarifies the decision making by addressing the production timeliness and future exploration plans. It settles the side–track discussion by showing a viewpoint on how to measure its overall costs and benefits. A good frame is a powerful element of any decision. It not only settles the irrelevant arguments, but also directs the analysis efforts. It directs the resources we would spend on data collection and analysis. Consider the millions-dollar implicit losses in terms of lost time and missed opportunities just because the analysis teams focused on a wrong frame of decision. Losses that they could entirely avoid only if they had spent more time on the framing of their decisions and less on confused modelling

1.3 The Decision Model

7

Fig. 1.2 A simple decision tree model showing the decision to drill an exploration well

and number crunching. As Peter Drucker mentioned, “the true dangerous thing is asking the wrong question”.4

1.3.1 Precise Decision Language Petroleum industry has amassed a distinct vocabulary, a combination of geological terms mixed with the language of uncertainty and corporate jargon. To an outsider, common expressions like “expected production for P90 volume” or “de-risking a target based on seismic anomalies” may seem unusual. While some terms (like the verb “risking” or even “de-risking”) could be confusing, without such a set of agreed terms communication would have been difficult. We, however, suggest that the decision language should be fundamental: having a limited number of precisely defined words.5 A fundamental decision language is a powerful tool. It brings clarity to the framing sessions. It will further ease the communications throughout the decision-making process. For example, a manager once said, “The information de-risked the likelihood of chance of success to 55%”. In a fundamental decision language, this translates to “Considering the added information, the chance of success increases to 55%”. This makes it a clear and informative statement. We suggest organizations hold multi-disciplinary framing sessions that reflect the multi-dimensional nature of their projects. Such sessions will hopefully lead to a representative decision model—consistent with information, preferences, and alternatives. A decision tree model, like the simple decision tree in Fig. 1.2, could be the result of an effective framing session. A decision tree model is effective in both communicating and evaluating project decisions. Most energy companies have adapted this modeling language for its clarity. Up to the point that these models have become part of their fundamental decision language. In Fig. 1.2, and reading from left to right, the exploration decision (square node) has two alternatives: “to drill”, or “to walk away” (shown as two branches emanating 4 5

https://www.goodreads.com/quotes/76796-the-most-serious-mistakes-are-not-being-made-as-a. Our suggestion is based on the discussions in: [3].

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from the square node). The alternative “walk away” leads to no outcome. We invest nothing and gain nothing. Therefore, this branch ends to the outcome of zero. The alternative “drill” leads to uncertain outcomes. It could lead to either a discovery or a dry hole. Therefore, the circle node (for uncertainty) has two branches showing these outcomes. On each branch, we also show the probability for each outcome. To evaluate each course of action, we estimate their “expected value”. The decision maker know that the outcomes are uncertain. In Fig. 1.2, drilling could lead to discovery or dry hole. But the best we can do is to take the course of action with the highest expected value. The expected value of drilling is the “mathematical expectation”. We multiply the chance of success by the reward for success and add it to the chance of failure multiplied by the cost (a negative reward) for dry hole.

1.3.2 Do Not Expect the Expected The “expected value” is a common term, yet it often causes confusion. We will meet this concept repeatedly through our discussions. For now, we define it as a measure of value creation in economic decisions with uncertain outcomes. This holds for most corporate decisions, most of the time. Note that expected value is just a measure. It is a guideline for making decisions. In mathematics, taking an expectation simply means summing up the product of probabilities and values If you toss a coin and receive a dollar if it sits heads up (and nothing if it sits tails up), then the expected value of this game is: 0 × 0.5 + 1 × 0.5 = USD 0.5. But you will never receive fifty cents. You will receive either one dollar or nothing. The expectation is just the measure of value for this game. You should play the game if the cost of playing is less than fifty cents. In an energy company, we should also make decisions with positive expected values. Like the game of tossing coins, a project will never lead to its expected value. An exploration well will either be a discovery or a dry hole. We should never expect the expected value. A decision tree is also a description of our project. It is a model of decisions and uncertainties. In Fig. 1.2 we are telling a story: if the exploration well leads to a discovery, then we will develop the field, we will then produce oil or gas, sell them in the markets, and generate revenue. Over the years, the revenue will pay for the costs and will hopefully continue to make profit. All this is like receiving a net reward today. But if the exploration well is dry, then we lose; we must pay for the cost of drilling. Then, what is the best course of action? This is the story of an uncertain venture. As uncertainties resolve, the value of the project also changes, the managers would try to change course in the project so that they mitigate the risks and capitalize on the upsides. The decision tree describes the story so that we agree on the details and the valuation that follows.

1.4 Analyses are Transitory (and Subjective)

9

1.4 Analyses are Transitory (and Subjective) By the time you conclude a project feasibility study, it is already obsolete! This is because we live in an ever-changing world with never-ending flow of information. For energy projects, we constantly learn about the uncertain factors. The price forecasts often change. Therefore, you would always need to update the analysis considering the added information. But this is easier said than done. We would never have the resources (and the energy) to constantly update our analyses. The decision makers would have to learn to live with expired analyses. Knowing that any analysis expires right after conclusion is a useful skill. The key is to use an expired, but still helpful, analysis. In this book, we discuss quantitative methods of valuation and analysis. In general, we build cash flow models and rely on the language probability to describe the uncertainty. We usually use decision trees to present our decision models. Such a numerical treatment may cause the illusion that the results are exact or definite. Most often they are not. Valuation is not an exact science. It is as good as its assumptions and assessment inputs [4]. The building blocks of any economic decision model (including, but not limited to the geological assessments, cost estimates, production levels, and price forecasts) are all assessed. Humans pass judgement on these parameters. We had better describe economic analysis as part art and part science. The assessors who estimate the inputs and the analysts who use those estimates, all affect the decision models.

1.4.1 Soft versus Hard Analysis In this book, we describe the quantitative methods for valuation. We encourage measurements and quantitative presentations. This works well for numerical disciplines. The geologists, engineers, and analysts that work in energy business often use a common mathematical language to communicate and conduct analyses. The results are quantitative decision insights. For example, analysis of a project leads to either a positive (favorable) or a negative (un-favorable) economic measure. Yet, in our process of simplification, we may leave some crucial factors out. For example, it is common to ignore the factors outside the standard work process. We could say “we choose to ignore it because we have never modeled this factor before”. We also tend to omit factors because they do not fit in pre-defined disciplines. In the absence of direct measurements, we treat these factors as soft issues. For example, by drilling a wildcat well, we could gain a strategic foothold in a region. It could open the door to further opportunities. Yet, it is difficult to measure such strategic advantage. Even with an unfavorable economic analysis, the strategic advantage may justify drilling the well. Overall, good valuations are not about soft versus hard analysis. They are about coexistence of these analyses. Even though measuring factors leads to more consistent

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comparisons, there will still be un-measured considerations. It is best to understand the limits of insights and to use the available information—both measured and unmeasured. Humans assess the inputs to any analysis. The valuations are then as subjective as their inputs. Good inputs lead to useful valuations and eventually form the basis of good decisions. On the other hand, biased inputs taint the valuation process often to their detriment. Although our focus in this document is not on elicitation and estimation, we stress that useful analysis needs (as much possible) unbiased estimates.

1.4.2 Decision Makers’ Bias in Valuations Instead of valuations supporting decisions, sometimes it is the other way around: a preferred course of action informs the valuations. Some managers want valuations to show specific outputs! Once they commit to a course of action without following analysis process, valuation become a formality. A manager was once heard telling the analyst: “we decided this is a good project, please now perform the required analysis. We want to submit supporting documents yesterday”. The manager’s quote may seem comical, but this same thing could happen in more subtle ways with similar effect. Biases often lead to confused decisions and loss of value. The biases are often context specific. For example, depending on whether the companies negotiate on selling, buying, or swapping an asset, they could artificially deflate or inflate the value of that asset. Every valuation is subjective. It is important to consider them all with enough skepticism.

1.5 The Journey is Important Some managers may regard valuation models as black boxes. To them, the methods of cash flow calculation, expected value, or decision tree models may seem nontrivial, but necessary, steps to calculate value. Once results are ready, the managers would go about making their important decisions—often with no regard for the process that led to those numbers. This mind-set is potentially destructive. Not knowing how inputs turn into outputs could ignore some real culprits, lead to bad decisions, and destroy value.6

6

We often hear that “managers do not need to know about the details of analysis model, as they also do not need to know how sausages are made”. On the contrary, we believe learning about the process is helpful. Like in sausage consumption, the added understanding helps make better decisions.

1.5 The Journey is Important

11

The real value of analysis is in the resulting clarity. Through analysis steps, we turn an opaque situation into a clearer situation. By following the analysis process, managers may also come to know the value drivers that make up the bulk of value. For example, how important is the price forecast? In most energy projects, this is a key assumption that could potentially overturn the entire business case. Where do these assumptions come from? Usually, a business unit responsible for market studies produces these informed assumptions. If the assumptions are outdated or unrealistic, they could make the entire analysis worthless. The decision makers who know about the mechanics of analysis are more likely to know about such hidden insights.

1.5.1 Extinct by Instinct versus Analysis Paralysis Some models are overly simplistic; they consider few things of importance but in the end, produce illusory decision insights. They are common in everyday life. They include the age-old folklore stories of the working of the universe. On the other hand, some models are overly complicated. Corporate decision models could well fall on this other end of the complexity spectrum. Such models may include many unnecessary (but good to have) details that confuse the decision making. They are the manifestation of the so-called “analysis paralysis”. George P Box famously wrote: “all models are wrong, some are useful”. In that, he meant that all models are approximations. We have either useful or useless approximations. But to model any decision in the real world, we must inevitably simplify. Of course, the principles of simplification and approximation also apply to energy projects. For example, think of the Original Oil in Place in a project. It is absurd to assume this parameter has a lognormal distribution. The real world is much more complex than the lognormal probability distribution. However, we have learned that using a lognormal distribution to describe the Original Oil in Place is convenient and often useful in decision making. It reflects some the aspects. It is a simple (but not simplistic) model of subsurface uncertainty. When do models cease to be useful? It really depends on the context, but a few recommendations could serve as guidelines: First, all models are abstractions of reality; the goal is to make good decisions not to have a comprehensive model. We need to build models with decisions in mind. Second, the details of a model serve their purpose only when scrutinized. A well thought out model is the result of rounds of refinements and clarity checks. Third, some variables may be easier to model but may not be necessarily useful. This is not an exhaustive guideline. Still other issues could either refute or paralyze our analysis. It is always good to judge all models, even this guideline, with the principles of parsimony, clarity, and usefulness.

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1.6 Conclusions Investing in uncertain and complex energy projects is a marathon not a sprint. Success or failure of e.g., a wildcat exploration well, does not necessarily show good or bad practice. It may be a fluke, or it may be the result of our decisions. Good practice is about using sound logic, utilizing the information available, finding the alternatives, and making value-maximizing decisions. Yet it does not guarantee success, but it is the best we have. Decision making is all around us. From personal life to corporate settings, decisions permeate all dimensions; they separate our future from countless other eventualities. We make decisions to interfere in the natural world and amend it to our benefits. Yet because they are so common, the idea of learning how to make good decisions may seem peculiar. We believe the business would improve by better decision making. Here, the unwanted consequences are sometimes immense and intense. Hasty decisions have costed companies dearly. Even more common is thinking of decisions as too complex and lingering too long in the state of evaluation. This opportunity loss from indecision also damages projects and degrades shareholder value. It is however ore difficult to measure such a loss. How can we improve on decision making? The next chapters introduce economic decision analysis as an effective tool. In summary, most business decisions are hard, but we should not complicate decision making if the goals are clear, the uncertainty is understood, and the alternatives are appreciated. In an uncertain world, these are keys to clarity of thought and clarity of action. We will further discuss inherent uncertainties of the upstream petroleum business and suggest analytical tools to support decision-making.

References 1. Data and statistics from Goldstein, BA (1994) Explicating a gut feel-benchmarking the chance for exploration success. The APPEA Journal 34(1):378–417 2. Brealey RA, Myers SC, Allen F, Mohanty, P (2012) Principles of corporate finance. Tata McGraw-Hill Education 3. Abbas, AE, Howard, RA (2015) Foundations of decision analysis. Pearson Higher Ed 4. Damodaran, A (2012) Investment valuation: Tools and techniques for determining the value of any asset (Vol. 666). John Wiley & Sons

Chapter 2

Understanding the Uncertainty

Abstract This chapter discusses the concepts in more details. We make decisions because we want to achieve our goals. Yet, we live in an uncertain world and the outcome of our decisions would be uncertain. Therefore, we describe projects’ uncertainty using the language of probability. Our description makes the decision models, yet they cannot (and should not) reflect all aspects of the real world. We should simplify. Our models should be simple enough (and realistic enough) to be useful.

2.1 Introduction In the words of investors “hydrocarbon exploration and development are risky ventures”. From our human point of view, success in these projects is truly a lucky event. Unseen geochemical forces led to formation of oil and gas accumulations over eons. Today’s oil is the chemically transformed microscopic sea animals trapped under layers of mud and silt ages ago, and then accumulated in places we can access. By luck (and skill), we tap into these accumulations. A purely fortuitous series of events—from formation to migration, accumulation, and drilling in the right place—leads to an oil or gas find. With these odds, the norm is dry hole; discovery, by far, is an exception. Instead of risky ventures, we should more appropriately call them bonanzas. What makes discoveries (or success in petroleum projects) lucky events? The geological processes leading to the formation of oil and gas are themselves not random. A series of known events, like erosion of mountains by rivers, burial of organic matter, deposition, pressure, and the resulting heat over millions of years eventually makes oil and gas. It is complex and detailed, but we understand the mechanism. If we could go back in time and visit, for example, Wyoming during the cretaceous period, we could see it as covered by a shallow tropical sea teeming with minuscule life forms. We could further follow one of those life forms through history. It lives and dies, sinks to the bottom of the sea and, before it decays, mud covers it. Fastforward the time, and the erosion covers it with more layers of sediments. Layers © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Jafarizadeh, Economic Decision Analysis, SpringerBriefs in Petroleum Geoscience & Engineering, https://doi.org/10.1007/978-3-030-96137-4_2

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upon layers increase the pressure and temperature until under right conditions, the tiny animal becomes a droplet of oil. As droplets join, migrate, and accumulate under impenetrable layers, following physical forces, they form a subsurface reservoir like the infamous Salt Creek. The oil in Salt Creek rested there for ages. On the surface, life forms evolved and then went extinct, ice sheets advanced and receded, until we humans came along. Our industry and our economy needed energy and encouraged petroleum exploration, leading to the discovery of Salt Creek oil reservoir. This lucky discovery would not be lucky if we knew the story from the beginning. We call it lucky because of our vantage point. We did not know about the tiny life forms, the shallow cretaceous sea, and oil accumulations. In other words, this was uncertain because of our lack of knowledge. We live in an uncertain world because of the ever-imperfect state of our knowledge. Granted, we know a lot about the world. Our knowledge of science and mathematics lets us build reliable machines that improve our life experiences. Every day we learn even more, updating our models and manufacturing ever more robust products. Yet our knowledge is never perfect, we have insufficient information to know everything. We constantly learn about the exploration business—gaining skills in finding promising prospects, increasing efficiency of drilling, and even beating the hydrocarbon markets. Yet, when making decisions, there will be things we do not know. Unless we know everything, hydrocarbon exploration and development will remain an uncertain business.

2.2 Geological Uncertainty Uncertainty is for our lack of knowledge. To describe uncertainty, we use the mathematical language of probability. There are other languages to describe uncertainty, but economic decision analysis relies on the concept of probability. A measure of likelihood of an event and taking values between zero and one, probability is the degree of our knowledge. The more we know of an event, the more certain we will be. We assign a probability closer to one to more certain outcomes. With this definition, we use the same measure to portray either geological or market uncertainties: “probability as a degree of belief”. Superficially thinking, it is tempting to consider market and subsurface uncertainties as different concepts. The market prices are uncertain because they fluctuate. The subsurface does not fluctuate, it has been unvarying for ages. The subsurface is uncertain only because we do not know about it. Yet, our mathematical descriptions of these two factors are still identical. It is only about our lack of knowledge.1

1

On a deeper level, market fluctuations are also because of our lack of knowledge. Prices move up or down for reasons. The forces of supply and demand lead to an equilibrium that affects the prices. We are uncertain about prices because we do not know of supply and demand in the future.

2.2 Geological Uncertainty

15

As an example, we can describe the chance of finding oil in an exploration prospect. Depending on who we ask, this chance could be different. The probability reflects the persons understanding—the total information and experience that they bring to this situation. While the CEO of the company may see this as an opportunity in a basin with 30% historical success (and assigns 30% to the chance of success), the chief advisor could assign it a 50% chance because she has seen direct fluid indicators in seismic data. At the same time, the oil rig worker on site who just struck oil assigns 100% chance of success to this opportunity. Who is right? They all are. They all assigned probabilities based on their degrees of knowledge. We should instead ask: What is relevant to the decisions? As probabilities are subjective, the decisions should be based on all available information. Those with fiduciary duty to shareholders (top level management) are not necessarily the most knowledgeable about an exploration prospect. They delegate such assessments to those who are most knowledgeable about prospects—the exploration managers and their team of experts.

2.2.1 Division of Certainty We gather information to build a geological model. Then using this model, we guess where we can find hydrocarbon resources. An exploration well will assess this guess. Before drilling, the existence of hydrocarbons is just a “guesstimate”. We use probabilities to describe it. For example, with our understanding about drilling prospect Alpha and our total experience of the situation, we assign the probability of 40% to its success. We have one unit of certainty. Therefore, we must assign 60% to the probability of its failure. The mathematical notation for probabilities of success and failure are P(Success) = Ps = 0.4

(2.1)

P(Failure) = 1 − Ps = 0.6.

(2.2)

Of course, the outcomes of “success” or “dry hole” are merely our conventions. We define the distinctions of “success” and “dry hole”. Depending on the context our definition may change. In the early days of oil industry, the formal definition of an oil discovery so that the owner could register a license with the US government was: “at least a bucket of extracted oil”. Today, we need a more precise language and clearer distinctions. For example, we could define a “commercial discovery” as: discovery of a hydrocarbon accumulation (by drilling a well) having a median producible volume exceeding in value the costs of its development and production. This is a comprehensive distinction. We can distinguish accumulations that cannot cover their cost of development and tag them as non-commercial discoveries. Still, this definition

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depends on myriads of assumptions. To precisely pinpoint a commercial discovery, we need to know of e.g.: oil prices, cost of development, production strategy, and the probability distribution of volume. In practice, an exploration well would not be just success (a discovery) or failure (a dry hole). There could be several outcomes in between discovery and dry hole. A well could lead to trace amounts of hydrocarbons, a few drops of oil, or rarely, a giant multi-billion-barrel discovery. The outcome space is a continuum—from zero to exceptionally large volumes. For our convenience, we define a threshold and assume an outcome beyond this threshold a discovery. We could describe the uncertainty about the outcome of a well using a continuous probability distribution as in Fig. 2.1. We divide the continuum based on the purpose of our analysis. Often the purpose is to support drilling decisions. Here, a binary distinction (success or failure) is useful. It is important to clearly define distinctions of “success” and “dry hole”. As Fig. 2.1 shows, the chance of success for a discovery of commercial volumes would be different from the chance of geological success. The probability tree on the left side of Fig. 2.2 shows a division of certainty for the outcome of drilling. On the right side, we further define distinctions of “oil” or “gas” discovery. Our models should account for revision of probabilities with the arrival of added information. Once we make a discovery, we may learn that it is either an oil or a gas discovery. We call these conditional probabilities. They depend on knowing

Fig. 2.1 Continuous probability distribution describing the outcome of a well

Fig. 2.2 Probability trees showing the division of certainty

2.2 Geological Uncertainty

17

the outcome of the earlier event. The mathematical notations for these conditional probabilities are P(Oil | Success) = 0.2

(2.3)

P(Gas | Success) = 0.8

(2.4)

In Fig. 2.2, the probability tree on the right side shows that we divide the certainty twice. Once for “success” or “failure”, and again for “oil” or “gas” given success. We show the fractions of original certainty at the end of each branch and call them elemental (or joint) probabilities. They are simply the multiplication of all probabilities in branches leading to a specific end node. For example, the upper branch of this probability tree shows the outcome of “oil and discovery” and has the chance of 8%. We have one unit of certainty. No matter how we split the certainty in a tree, the sum-total of elemental probabilities always adds to one. With probabilities we express our state of knowledge. Therefore, as we gain more information our probability assessments should change. For example, we may use seismic data to build an understanding of the subsurface, then once satellite data (a new source of information) reveal a magnetic anomaly in unexpected places, we accordingly need to update our understanding. With added information, our assessment for chance of success for drilling may change. With their transitory nature, we had better assign probabilities with timestamps. In practice, information could arrive in subtle ways. Sometimes, it is even difficult to notice. For example, we can readily notice the information in seismic anomalies, direct fluid indicators, or news about nearby drilling. It is usually harder to discern the information in events such as a dry well or a competitor relinquishing a license. It is even harder to assess the information when the order of events is different from our common experiences. To address these subtle issues, we use a formal process of modeling uncertainty with conditional probabilities. We first define the relationships between events. We then apply a procedure called “probability tree reversal” to update our belief about the new order of events.

2.2.2 Probability Tree Reversal Prospect Alpha has two segments. The geologists believe that the overall chance of success in Alpha is 40% and that the deeper segment, segment two, is more promising. According to the geological model, the migration of hydrocarbons is upward. If segment two holds oil, then the up-dip segment one has 30% chance of holding oil. If segment two is dry, then segment one is also dry. The probability tree in Fig. 2.3 shows the conditional probabilities for this multi-segment prospect. The figure also shows the elemental probabilities.

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Fig. 2.3 The probability tree showing the geologist’s belief of the chances of success in prospect Alpha

While drilling, we first penetrated the upper segment one and found that it is dry. Should we stop here or continue with costly drilling? To make this decision, we need to know about the (updated) chances. What would be the chance that segment two is also dry? The order of events in the geological model of Fig. 5 is different from the order of information arrival when drilling. The geological model gives the chance of success in segment one conditional on success or failure in segment two—the order the geologist thinks about migration of hydrocarbons. For drilling, the events have the reverse order. We first know about the upper segment and then we need to know the chance of success in segment two conditional on success or failure in segment one. To reverse the order of events in any probability tree, we first need to calculate the elemental probabilities. The tree on left side of Fig. 2.4 shows the probabilities for the four outcomes of drilling prospect Alpha. These will be

Fig. 2.4 Probability tree reversal for prospect Alpha

2.2 Geological Uncertainty

• • • •

19

Oil in segment two and segment one: chance of 12% Oil in segment two, dry segment one: chance of 28% Dry segment two, oil in segment one: chance of 0%, this will never happen Dry segment two and segment one: chance of 60%.

The reverse probability tree on the right side of Fig. 2.4, will have these same elemental probabilities, though differently distributed. In this reverse tree, we would like to know the chance of success and failure in the deeper segment two conditional on the outcome of segment one. After re-arranging the elemental probabilities, we could reconstruct the division of certainty in the reversed tree. One unit of certainty in the reversed tree is split first by “success” and “failure” in segment one. Then the “success” branch will lead to elemental probabilities that sum to 12%. The “failure” branch will sum to 88%. Using these probabilities, we could calculate the conditional probabilities of success or failure in segment two given the outcome of segment one. The probabilities in each branch should split proportionally so that they lead to elemental probabilities: • Chance of success in segment two, given failure in segment one: 28% = 32% 88% In other words, once drilling reveals that segment one is dry, the chance of finding oil in prospect Alpha does not drop significantly. There will still be 32% chance of success in segment two. The reversal of probability trees by itself does not create information. It is just a way of consistently re-arranging our beliefs. If the results seem somehow surprising or inconsistent, then it must have been because of inconsistency in our original estimates. The conditional probabilities are not to blame! We could go back and refine our beliefs. We could revise probabilities in the original or the reversed trees. This may take multiple rounds of refinement, but it is worth the clarity of thinking that ensues.

2.2.3 Challenges in Eliciting Information Clear statements go a long way in describing information. Assume you receive news from two sources. They both inform you about the outcome of drilling in an area with average 20% chance of success. However, they use different wordings: • Source I: at least one of the two wells led to discovery • Source II: the first of the two wells led to discovery. Are these statements the same? What is a chance that the other well also lead to discovery? Most people consider the above statements identical. Yet they have subtle differences. Using a probability tree, we define two events: The first event describes the outcome of the first well. It has 20% chance of discovery and 80% chance of dry hole. The second event describes the outcome of the second well (with the same

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Fig. 2.5 Probability tree for information elicitation problem

probabilities). The probability tree in Fig. 2.5 shows the conditional and elemental probabilities for the two events. Considering the different statement from our sources, what would be the chance that the other well is also a discovery? The answer would be different for each source. • Using information from source I: P(two discoveries|at least one discovery) =

4% = 11% 36%

• Using information from source II: P(Second well discovery|first well discovery) = 20% The principles of division of certainty and conditional probabilities apply to any pairs of related events. They even apply to compound events where three or more events are related. The example below shows the application of multi-event conditional probabilities to finding a potential play opener.

2.2.4 Multiple Events We consider three prospects in a frontier exploration play. The prospects α, β, and γ are correlated. Once we drill any of them, we learn about the others and could reassess their chances of success. The geological model that defines these relationships usually distinguishes between chance of success for play and chances of success for individual prospects. If a piece of information proves the play, then the chances of success for individual prospects improve. Table 2.1 shows the chances of success for the three prospects. In addition, the geologist has expressed pairwise correlations between prospects. A high correlation

2.2 Geological Uncertainty

21

Table 2.1 Original assessments for probabilities and correlations Correlations for Success Chances of Success α

20%

α

β

30%

β

γ

10%

γ

α

β

γ

1

0.1

0.3

1

0.4 1

coefficient (close to one) shows strong ties between two prospects while a correlation number close to zero usually means independence.2 Mathematically, we can calculate the conditional probabilities from correlations. The general relationship between the two expressions is ρ( A, B) =

Cov( A, B) . σ AσB

(2.5)

In this equation,ρ(., .) is the coefficient of correlation,σ. is the standard deviation, and Cov(., .) is the covariance of the two variables. For our binary variables where the outcome of a well could be either success or failure, we have Cov( A, B) = P(A)P(B|A) − P(A)P(B) σA =



P( A)(1 − P( A)).

(2.6) (2.7)

After replacing and rearranging, equation 2.5 for binary variables becomes √ ρ( A, B) P( A)(1 − P( A))P(B)(1 − P(B)) + P(B) P(B|A) = P( A)

(2.8)

Assuming the success and failure in prospects have symmetrical correlations, we can construct probability trees for pairs of prospects. In other words, we assumed if for example the correlation of success in α and β is 0.1, then the correlation between success in α and failure in β is −0.1. Fig. 2.6 shows the conditional probabilities for pairs of prospects. These pairwise distinctions show that the result of drilling prospect γ has the largest impact on the chances of success for the two other prospects. If γ is a success, then the chance of success in β dramatically rises (from 30% to 85%). Likewise, the news about success in γ increases α’s chance of success to 56%. No other prospect in this play has such profound potentials. In exploration jargon, prospect γ is a play opener. 2

Correlation coefficients only show a linear relationship between two variables. Even with a correlation of zero, the two variables might still have a non-linear relationship. To keep our analysis simple, we assume zero correlation stands for independent prospects.

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Fig. 2.6 Pairwise conditional probabilities for success and failure in prospects α, β, and γ

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A play opener prospect is like a window into a less known system; success in it could open a range of possibilities. Similarly, a dry well could mark the end of the exploration campaign. In practice, the value of drilling such prospects is primarily in the information they provide, the immediate payoff is less of a concern. Yet the decision to drill a play opener well depends on its total value, including the value of information about neighboring prospects. A decision tree, showing the best course of action about contingent drilling decisions, could reveal the value potential of such a well. We will discuss decision tree models later. Here, we show how to construct a probability tree for compound events. The principles are like the binary events: the elemental probabilities add up and form the compound events.

2.2.5 Compound Events Earlier we made pairwise probability trees for the conditional outcomes of drilling prospects α, β, and γ. Combined with their reversed versions, the six trees show all possible pairwise distinctions and divisions of certainty. Still, for valuation and decision-making, we need a compound probability tree for all three prospects. In principle, we do not need more information to construct a compound tree. The information already available—individual chances of success and the correlation matrix—is enough. However, we might need to perform more calculations to reveal the interrelationship between these distinctions. The compound tree will have elemental probabilities that agree with the pairwise elemental probabilities and the principle of division of certainty. The compound probability tree in Fig. 2.7 shows the chances of success for prospect β given the outcome of γ and α. To calculate these conditional probabilities, we used the relationships in pairwise trees of the earlier example. In the compound tree, we needed to calculate eight elemental probabilities. Using the conditional probabilities of “α given γ” and “β given γ” from before, we also built eight equations. The elemental probabilities were simply the answers to the system of equations including eight equations and eight unknowns.3 A key characteristic of exploration is limited information. Even with the advent of new data gathering and storage techniques, the abundance of data does not necessarily lead to abundance of information. Some sources of uncertainty never resolve. We use available information, draw on analogies with similar projects, and build conceptual models for exploration decisions. In the end, we must deal with uncertainty for making decisions.

3

We do not need to over-complicate these calculations. A simple spreadsheet search model could supply answers to this system of equations and unknowns in a few seconds.

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Fig. 2.7 A compound probability tree for valuation and decision making

2.3 Uncertainty in Upstream Value Chain Petroleum exploration and development is an uncertain venture. In addition, depending on the context, our portrayal of the uncertainty might vary. For example, looking for conventional petroleum accumulations draws on different geologic controls compared with unconventional resources. In the former, we look for hydrocarbons that migrate and accumulate under traps. The challenge would be in finding traps with large enough producible accumulations. For the latter, there is no migration and accumulation. We look for hydrocarbons trapped in the source rock. The challenge would be in finding opportunities with large enough vertical and horizontal extent and favorable properties so that we could produce large enough amounts by hydraulic fracturing. Yet with all these fundamental differences in their uncertainty profiles, the valuation and decision models are remarkably comparable. They all are about paying for playing a bet with uncertain outcome. Within all such models, we use the language of probability to describe uncertainty. We model the major decisions, and we further consider the resolution of uncertainty. Finally, with all models we conduct analysis using economic insight. With all this in mind, we could use a representative example from the conventional offshore business to discuss the general properties of value chain. Within this context, we also discuss the sources of uncertainty in a decision model. Granted, each project is unique, otherwise we would not call it a project in the first place! Even re-considering a shelved opportunity after a dormant period could pose new challenges, making it

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look like a new opportunity. However, here we discuss the elements that re-appear in most exploration decisions. We believe that understanding these general features creates a mind-set that goes beyond understanding specific models.

2.3.1 Errors of the Third Kind It is easy to commit to a wrong frame of decisions. For example, once the people of influence regard an available frame as the “right” frame, then that frame will be there to stay. In such an environment, re-considering other frames would seem impractical or even wasteful. Even though those other viewpoints better serve our needs. We call such errors of “working on the wrong problem” the errors of the third kind. How can we avoid these errors? The most proper frame for a decision has the scope and details that best serves our needs. Recall the earlier discussion about drilling a sidetrack from a production well to explore a nearby prospect. In that example the proper frame was one that satisfied the needs of the stakeholders. Then, who are the stakeholders? The most immediate stakeholders are those managers who need information to make better future decisions. In addition, the next level would be the company’s shareholders who expect value creation from hydrocarbon production. Admittedly, it is not easy to distill the will of all the stakeholders in every specific decision but at least this is a useful concept to guide our framing sessions. We recommend starting the analysis with a multi-disciplinary framing session. This session should answer the question: what is the exploration decision? It may seem obvious; often the decision is to drill or not to drill. Sometimes the decision is about running seismic, drilling an appraisal well, or adjusting ownership in a license. Whatever the context, we should clearly show the alternatives and available information for making the decision, along with how the decision would achieves our goals. There is little to lose if we re-iterate and refine, the big loss is in hasty decisions resulting from an improper frame. Good framing sessions lead to clear definition of decisions and identification of key uncertainties. They also uncover previously ignored but potentially life-changing aspects of the opportunities.

2.3.2 The Chance of Success It all starts with the geoscientists. They study the data and use their background knowledge to find subsurface structures that potentially hold hydrocarbons. These “locations of interest” reflect the geologists’ degree of belief. As their understanding improves, the less known “leads” could become better known “prospects”. What would be the chance of success if we drill into a prospect? The geological understanding helps in assessing this probability. A measure born out of limited data and skilled postulations. Yet, this assessment is so crucial that overshadows everything else. For most projects, the effect of slight adjustments to the chance of

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Fig. 2.8 Decision trees showing the best course of action

success dwarves the effect of adjusting any other value driver. The chance of success (and the accompanying mean volume) is the key uncertainty in exploration decisions. The example in Fig. 2.8 shows the effect of chance of success on a simple exploration decision. The reward for a successful exploration well is USD 60 million. If the cost of drilling is USD 20 million and the chance of success is 30%, then it is best not to drill the well. The upper decision tree shows that the expected value of drilling would be negative. Yet, a mere 10% increase in the chance of success (lower decision tree) would result in a positive expected value for drilling and a recommendation to drill. With all its importance, assessment of this probability calls for clear definitions and assumptions. In conventional resources, the chance of success naturally depends on the underlying sub-processes in geological models. The hydrocarbons need to form in source rocks, migrate and then accumulate in traps. The geologists assign probabilities to each of these sub-processes (and their detailed sub-sub-processes) along with considering the geometry, depth, segmentation, and distribution of hydrocarbon volume of the prospect. Still, factors outside the domain of geology could affect the chance of success. For example, the drilling plan with the angle and location of penetration, or even the assumption of oil prices (for success at high price times is different from success in low prices), in theory could affect our assessment. With such a blend of factors, we first need a clear definition of “success”. We could define success as the outcome of a well that shows the existence of hydrocarbons in volumes exceeding a specific threshold. While a discovery is a cause for celebration, in practice it barely marks the start of the value creation campaign. It would be the beginning of a long plot that follows with development, production, and eventually, sales of hydrocarbons. We celebrate a discovery merely because later it could create monetary value. Whether we sell the discovery at once to a third-party, or we set about the development ourselves, the value of an asset fundamentally reflects its potential for future value creation.

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Companies often plug and abandon the exploration wells once they reach the targets, even if they lead to a discovery. In practice, the value of these wells is in the information they provide. Contrary to common belief, we do not produce oil or gas from exploration wells. It calls for usually the more expensive and designed production wells to extract the discovered hydrocarbons. Overall, exploration wells are means of gathering information. With them, we update our original belief about a prospect. Sometimes even with the updated belief, we are still uncertain whether the amounts of producible hydrocarbons justify the investment in development. In practice, the information we get from a single exploration well seldom resolves the subsurface uncertainty. The subsurface will still be uncertain, though a bit less so because we now know a bit more. We may need to gather more information if the understanding is not clear enough to make next level decisions. Here, drilling appraisal wells, could further improve our understanding.

2.3.3 Value of Appraisal In everyday conversations we say: “more is better”. Likewise, we prefer more information to less. But information comes at a cost. We often need to drill a well or run seismic to gain information—all costly operations. In principle, the added information should be valuable enough to justify the cost. This applies to all information acquisitions, and especially to appraisal wells. The value of an appraisal well is in making better decisions. By showing a subeconomic discovery early on, right before we invest millions in a doomed development project, an appraisal well could help in avoiding future losses. The appraisal well could also help us show larger-than-expected potentials. We might have opted for a “budget but inflexible” development, only to learn later that we need an expensive upgrade because the hydrocarbon volumes are large than expected. Here, appraisal information could show the upside potentials early on and lead to a fit-for-purpose development solution. Assume in an offshore setting, we drilled and found oil. Experience with similar cases shows that we have 60% chance of successfully developing the discovery. Such a successful development will have a net present value of USD 100 million, but nevertheless, an ill-fated development will lead to a net loss of USD 50 million. There is 40% chance of failure, so before deciding we consider getting more information. The engineers estimate that an appraisal well will cost an added USD 10 million. If the well’s outcome is negative, then we will walk away from this unpromising opportunity. Yet, the appraisal well is not a perfect predictor of success or failure. We have never developed a field when appraisals were negative, but even with positive outcomes, 20% of the developments failed. With this record, what is the best course of action? Should we accept the risks and develop the field right away or drill an appraisal well and decide later?

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The decision to gather information depends on the conditional probability of development success given a positive appraisal outcome. However, our experience with appraisal wells does not show this likelihood. Our experience only reflects the record of appraisals given the development results. To make decisions, we need the reverse. We need the (likelihood of) result given the signal, yet our data shows signal given the result. To calculate these reverse conditional probabilities, we have shown in Fig. 2.9 the data in a probability tree and its “reversed” version. Our prior knowledge is that 60% of developments succeed. The chance of seeing positive appraisal for successful developments is 80% (20% fail even with positive appraisals). We have always developed fields following positive appraisal results, therefore the chance of successful development with negative appraisals is 0%. We show this prior knowledge in the probability tree on the left. The reversed probability tree on the right uses the elemental probabilities and gives the conditional probability of development success (or failure) given a positive appraisal outcome.

Fig. 2.9 Decision tree and probability tree reversal for the appraisal problem. The best course of action (in bold arrows) is to drill the appraisal well and then develop if its outcome is positive

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Finally, we can use the conditional probabilities in the decision tree and calculate the expected value for each course of action. The best course of action has the highest expected value. Here, it would be best is to drill an appraisal well. If the outcome is positive then we should develop the field, otherwise we should walk away. Although the appraisal well is costly, it will lead to better decisions with a higher expected value —USD 46 million compared to USD 40 million if we develop without appraisal. The appraisal programs supply valuable information, but they never entirely resolve the subsurface uncertainty. In decision analysis jargon, appraisals are imperfect sources of information—as opposed to (the elusive) perfect sources of information, which replace uncertainty with certainty. In general, more information means less uncertainty. As we learn, we update our belief about the subsurface. But reduction of uncertainty by itself should not be the goal. We deal with uncertain factors because in the first place we needed to make decisions. Uncertainty is the born out of thinking about decisions. It is not a free-standing concept. The goal in exploration decisions is to eventually create monetary value, therefore the relevant uncertainty is about producible volumes sold in the market. In practice, not all discoveries are producible. They need to be sufficiently large. In addition, only part of a sufficiently large discovery is producible. We better define the goal of exploration decisions as “discovery of large recoverable volumes” rather than just any discovery. Yet, this is also a loose definition. How much is large recoverable volume? It depends, at least, on the development scheme, the production technology, and hydrocarbon prices. In deeper sense, the frame of the exploration decisions encompasses the development, production, and price outlook.

2.3.4 Minimum Economic Volume In assessing the uncertain subsurface, we consider a range of possibilities. While geologically interesting, not all such possibilities would be commercially valuable. Only a sufficiently large discovery justifies the investment in development and would be valuable. This leads us to the concept of minimum size discovery with “economically viable” volumes. Any discovery larger than this minimum size would be valuable, smaller discoveries would not produce much to cover the expenses. In practice, finding this minimum economic volume is a challenge. It would be a multi-dimensional assessment as we need to think of things to come—development, production, and sales of hydrocarbons. A good assessment would also need to consider reservoir management plans or even price forecasts. Any minor adjustments to these assumptions would result in different value creation assessments and would change the minimum economic volume. For example, as in Fig. 2.10, everything else remaining intact, merely changing price forecasts will lead to different assessments for economic thresholds. We would end up with smaller minimum size discovery at higher price forecasts. With all these intricacies, finding the minimum economic volume could be analytically challenging. It may even seem like a “catch-22” situation. We need minimum

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Fig. 2.10 Minimum Economic Volume varies with varying hydrocarbon prices

economic volume to estimate the chance of success and assess the value of an exploration opportunity. But to estimate this threshold, we need to know about the value in the first place. In other words, we are dealing with a dynamic frame of decisions; as we increase our understanding of the exploration problem, we need to go back and re-define the frame. As we update the decision frame, we come to better appreciate the key underlying uncertainties. We estimate recoverable volumes by further refining our understanding of inplace volume. We use a “recovery factor” that shows the producible proportion by considering rock-characteristics and production technology. Indeed, to gain insight about the recoverable proportion of hydrocarbons we need to think about the details of the production plan (calling for thinking about the development and drainage strategy). Again, we face a cascade of stages arranged in a circle—reminding of the symbol of a snake eating its own tale! The circle is complete when we consider the best development scheme for a range of possible recoverable volumes. Commercial factors like hydrocarbon prices and the fiscal regime would affect the financial outcomes of such possibilities. We will need to iterate through the stages to better understand and estimate the key factors. While this circular nature of analysis may be unusual, it needs not be debilitating.

2.3.5 Development Scheme: From Reservoir to Market An oil or gas reservoir with known features (like its outline, productivity, and fluid characteristics) calls for a fit-for-purpose drainage strategy. Often such features are rarely known early on. They hardly ever will be known. We would have many possibilities and therefore, host of possibilities for development strategies. Yet, no company has the resources to examine the infinite possibilities. They look for a few representative scenarios. Again, even with limited scenarios, a fit-for-purpose development would depend on myriads of factors: water-depth for offshore installations, the physical extent

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Fig. 2.11 Representative scenarios for development and their discrete probability distribution

of the reservoir, arrangement of well, and access to nearby facilities—Multitude of competing designs. Similarly, our analysis could only accommodate a few representative cases. At the heart of this analysis is proper simplification—simple enough to support decision making but still not simplistic. We want simple representative scenarios that still reflect a reasonable range of possibilities. This is a trade-off between simplicity and verisimilitude: A too simple model could misrepresent the uncertain investment decision while a too realistic model could be too detailed to be useful. A good strategy to reach a balance between “realistic” and “solvable” is to start simple and gradually add details. The simplest analysis would be to consider a single development solution— assuming we develop an average size discovery with its average (expected) features. Yet sometimes this may not a proper level of details. The development schemes are good only for a narrow range of possibilities. For example, if a discovery is close enough to existing infrastructure, a single subsea tieback would be practical, and inexpensive, solution. But this development solution could only accommodate “small” or “medium” size discoveries. It may not entirely unlock the potentials of a “large” discovery. Consider another example: For a “small” or “medium” discovery, a stand-alone platform development may be an expensive overkill. If we opt for an expensive design but the discovery turns out to be smaller than expected, then the project will be doomed. The limited production cash flow could never pay for the excessive cost of development. If a single development solution is too simplistic, then we could divide the range of possibilities to sub-intervals and assign a development solution to each. For example, in Fig. 2.11, we divided the range of recoverable volumes into three sub-intervals.4 If the discovery turns out to be “small”, then we plan for a subsea development. For 4

In the oil and gas industry, the common approach to discretize a continuous probability distribution is “Swanson’s method”. Given a continuous distribution, this method assigns 30% of the probability to P10, 30% to P90, and the remaining 40% goes to the P50 of the distribution. This would replace infinite possibilities with three representative scenarios.

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a “medium” discovery, we go for a larger scheme with multiple subsea templates, and finally, for “large” discovery, we use an expensive, high-capacity, stand-alone platform. These added distinctions show more realism. However, reaching a sweet spot between simplicity and realism is for valuations to be useful. The added details are worthwhile only if they significantly affect the economics of the decisions. If we would make the same drilling decision irrespective of the envisioned development solutions, then all the efforts in performing more detailed analysis would have been in vain. Often an exploration project is part of a group of opportunities. With multiple discoveries, we would consider a regional development plan while a single discovery by itself may not justify development. To assess the value of such a group of opportunities, considering the intrinsic (isolated) value of a prospect is often not enough. We need to think beyond and consider the value of the prospect in combination with other prospects—the synergies between neighboring opportunities. This wider frame of decisions could reveal value-adding relationships. For example, we may learn that a potential discovery could be part a joint development plan along with its neighboring discoveries. We may even reconsider the ordering of drilling wells based on the revealed relationships.

2.3.6 Joint Development Solutions Often the economy of scale drives the regional exploration and development strategies. Development is expensive. It would be a huge savings in expenditures if two or more projects could share facilities. This synergy between discoveries means that the cost of joint development would be lower than the sum of the costs for individual developments. The joint value would be higher than the sum of their individual development values. Scallop oil considers drilling two nearby prospects. Prospect α has 30% chance of success, costs USD 10 million to drill, and if successful, could have an FPSO development in total worth USD 20 million. Prospect β has 20% chance of success and at USD 15 million is more costly to drill. However, if successful, its larger expected volume justifies a standalone development the total worth of USD 80 million. A preliminary analysis as Fig. 2.12 shows that the expected value of drilling

Fig. 2.12 Individual and isolated analyses lead to discarding prospect α

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prospect α is USD -4 million—a negative value. It would mean that Scallop should walk away from drilling this prospect. On the other hand, because prospect β has a positive expected value, Scallop should drill in this location. The engineers at Scallop have also devised a plan for a joint development. The chance of two discoveries is low. Assuming the wells are geologically independent, the probability of discovery in both α and β is only 6%. But if they have two discoveries then Scallop could build a common platform and produce from the discoveries at a lower overall cost. Current estimates say that such a joint solution is in total worth USD 120 million, more than the sum of values for individual developments. In our valuation, we would like to reflect the synergy between the prospects. Because of their joint value, we need consider the order of drilling wells. In Fig. 2.13 we show a decision tree for this drilling campaign. The first decision would be which well (if any) to drill. The outcome of the first well reveals if we would ever have a joint development solution. It turns out that if we drill β first and then drill α only if β was a discovery, the expected value of the campaign would become positive. We do not recommend drilling α first, that course of action would have a negative expected value. The synergy between the wells affects the recommended course of action. The best strategy has higher expected value than any of wells considered in isolation. Here, we first drill β. If this is a discovery, then we move on to drill α. If β turns out to be dry, then we stop drilling. We considered the added information about

Fig. 2.13 Optimal drilling order when prospects could lead to discoveries with a joint development solution

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joint development and made an informed decision model. Note that our short-sighted preliminary analysis recommended discarding prospect α altogether. The technical and geological uncertainties are crucial to exploration and development decisions, yet still no business decision could ignore the markets. Engineering decisions are often at their heart, business decisions. In petroleum industry, projects are large scale bets on geology, but in the end, they are bets to make money. It is no wonder that in the global oil and gas business, the exploration activities pick up where (and when) the business is good. We might think of exploration as a geology game. But such activities often happen in regions with the best tax regimes (highest value potentials) than regions with the best geology. The exploration activities also tend to sway with changing economic conditions. When the average crude oil prices dropped from above one hundred dollar per barrel in the beginning of the year 2014 to less than fifty by the end of that year, global exploration activities also dwindled. At low prices, most companies scrapped their already approved drilling plans or shortened their exploration campaigns.

2.3.7 The Market Determines the Fate of the Projects After decades of transformation, the crude oil market has now become the largest commodity market. Its sphere of influence shapes the global business landscape. It comes as no surprise when news media report the price of West Texas Intermediate or Brent (major crude oil benchmarks) as part of their routine reports on energy, politics, and society. Such a “per-barrel-price” is relevant to any energy project, say from Indonesia to Alaska. The prices particularly influence exploration and development decisions, although at first sight the revenue from the discoveries would reach the markets years or even decades in the future. Being global in scale, the crude oil markets reflect a holistic picture of the energy landscape. The producers and consumers of petroleum (and its products) practically display their capabilities and limitations, along with their hopes and fears, in their buy and sell behavior. In theory, the prices show the balance of buy and sell effects, or at least the bulk of the equilibrium in supply and demand. The market is liquid. It has many participants. A large fleet of tankers have also eased global transportation. Even though the price of benchmark West Texas Intermediate or Brent crudes are for a specific grade of crude oil in specific locations, they reveal expectations about the entire market. With efficient trades, any other type of oil in any other location in the planet would reflect the price of these benchmarks. For example, the market prices went down once the tight oil technology in the United States matured. This technology increased the total crude oil production and turned the country from an energy dependent to a net energy exporter. The global markets displayed their opinion in prices. As if they say: “too many barrels of oil now, more than what we need, therefore not as sought-after as before”. This led to a decrease in prices.

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Prices depict a wholistic picture of supply and demand, yet they also reflect a variety of effects. For an investor in the upstream petroleum industry, not all such effects are relevant. Because of projects’ long lead times (the time between discovery of hydrocarbons to the first oil in the market), the investors should be interested in long-term price trend, not in the transitory price shocks. In April 2020, a month after the introduction of widespread lockdown measures to combat the spread of COVID-19, travel and transportation dramatically reduced. The demand for crude oil and its products suddenly declined. The producers were slow to react to this change and continued to supply crude oil to an already saturated US market. Soon, the inventories were full, making it impossible for the market participants to buy any more oil. The natural reaction was a sudden price drop. Yet, continued supply reached a painful level that prices in the United States temporarily went down in the negative territory—a sign of desperate attempts to stop the flow of oil. This ordeal lasted only briefly and the European benchmark, Brent, never experienced negative prices. Overall, this was a frightening but transitory effect that is less relevant to the petroleum explorers who have a long-term scope in their drilling decisions. In conclusion, the prices are uncertain. They are one of the most influential sources of uncertainty affecting the value of petroleum projects. Yet, although we may be able to understand the dynamics of prices using supply and demand arguments, we are less successful in predicting them for the future. The best we can do is to understand this uncertainty, assess its impact on decisions, and make informed decisions. With more understanding come more informed models. Yet, there are practical limits to what we could include in a valuation and decision model. Investment decisions are multi-dimensional. A combination of geological, technical, and commercial factors affects the values. Even though the geology might be right, a prospect might have poor value for lack of export solutions. In addition, tax regimes hostile to foreign investment could hinder the drilling of even the most appealing prospects. To make useful models, we should consider the available information, appreciate the key uncertainties, understand the goals, and iteratively refine our decision models.

2.4 “All Models Are Wrong, Some Are Useful”5 The logical precept about models being “abstractions of reality” especially applies to the upstream petroleum business. To make any decision, we first need a description of the real situation. We need a definition of the problem and an understanding of the uncertainty. In the end, the quality of our decision-making cannot exceed the quality of our models. In making models, we need to simplify. Our models display only aspects of the reality but could not (and should not) reflect everything. The goal is to build useful 5

This memorable expression appeared in the works of the statistician George Box. However, the concept is not new. It is a signature of good scientific thinking through the history.

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(not realistic) models. Therefore, we select only the key aspects to show. As such, all models are simplifications. Our earlier discussion on the key aspects of hydrocarbon value chain is a good start, but by no means an exhaustive checklist. Each investment opportunity is unique and calls for its own purpose-built decision model. The investments may even depend on an entirely dissimilar set of factors and justify their own detailed study. But we hope that a general understanding helps. For example, time constraints in a project could cut the possibility of further data acquisition. Then the drilling decision should be based on the current state of knowledge and not on the possibility of further resolution. Unlike an unconstrained project, here we work with a different frame of decision. Even though projects have comparable patterns, for analysis of any project we recommend defining a frame and finding the key uncertainties. As we learn about the value drivers, we should revert to the frame and revise.

2.4.1 The Time Constraints Most petroleum exploration and development are within licenses. These are government-issued allowances giving a client the right to explore and develop. The terms of the licenses usually specify data acquisition (like seismic surveys and drillings) during a specific period. The client (oil company) also pays an area fee per square kilometers and per year. In a specific project, depending on how far we are within the license, the terms and conditions could impose a constraint on exploration schedule. Other than these regulatory constraints, availability of resources (including budget, labor, or technology hardware) could also impose a time constraint. The period from discovery to first oil, called the lead time, is particularly important. During this period, companies select a development concept and execute it. In almost any project, this period needs the largest capital. With large outlay and no income, it is no surprise that all companies aim to shorten their projects’ lead time. At the time of exploration drilling, the lead time and project schedule are just assumptions. Yet, because of the effect of time on discounting, such assumptions deeply influence the value. Even with the known track-record of oil industry’s underestimating cost and completion time [1] still a good guide to realistic assumptions is the experts’ insight and past experiences—the only sources of insight ever available. With the improved understanding, the frame of our decision models could either shrink or expand. The limiting time constraints was an example of shrinkage. With limited time, we learned that we would have a limited decision space. The frame of a drilling decision could expand to include opportunities beyond. For example, drilling a well could bring to light further options, including the joint development scheme with a nearby prospect. We could also think of the “upside potentials” of drilling a well as it may open a new play with further exploration targets.

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2.4.2 Geologically Correlated Opportunities Petroleum exploration is a game of large bets. These bets may even be interdependent. We earlier discussed the synergy in joint development solutions. In addition, the prospects could also be geologically correlated. Nearby prospects could share geological features. We may think of them as bets within a larger gameboard. For example, we think of migration paths, geological structures, and potential traps, leading to a group of prospects in a neighborhood. Drilling into one of these prospects could give us information about the chance of success in the rest. Here, one bet could open or close the door to the other bets. Scallop oil considers drilling two nearby exploration prospects γ and δ. The chance of success in prospect γ is 30% and costs USD 25 million to drill. If successful, the value of the discovery will be USD 50 million. The expected value of drilling γ is negative, and we recommend discarding this opportunity. Prospect δ has 22% chance of success and costs USD 15 million to drill. If successful, the net value of its discovery will be USD 40 million. Prospect δ also has a negative expected value and is not favorable. We show the analysis in decision trees in Fig. 2.14. However, geologists at Scallop believe that the information from drilling one prospect could help in assessing the chance of success in the other. A discovery in γ could increase the chance of success in the nearby δ. A dry hole will on the other hand, make δ look dim. The geologists express these correlation as: Discovery in γ increases the chance of success in δ to 50% but a dry hole would decrease the chance of success in prospect δ to 10%. In this example, the prospects are related. We are evaluating a game of interrelated bets. The geologists describe the conditional probability of δ given the outcome of γ . This is an informational relationship (it does not necessarily mean that we should drill γ first). Our earlier analysis ignored these relationships. Based on the geologists’ expressions, we can calculate the reverse conditional probabilities. Figure 2.15 shows the probability trees describing the conditional probabilities of success or failure in γ and δ. We show both the original tree—with the geologists’ conditional probabilities—and its reverse. Now, with two inter-dependent prospects, what is the value of the game? put another way, what course of action best unlocks the value potential? The answers depend on how well we use the available information in drilling decisions. The decision tree in Fig. 2.15 reflects a model that considers such details. The best course

Fig. 2.14 Preliminary analysis recommending that we discard both prospects

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Fig. 2.15 Probability trees describing the chances of success in prospects γ and δ

of action (shown in bold arrows) considers the interrelated uncertainties and will unlock the value potential. Here, we first consider which well (if any) to drill. Then, based on the results from the first well, we update the chance of success for the other well using the information from the probability trees (original or its reverse) shown in Fig. 2.15. The results are interesting. Even though the wells were individually unattractive (negative expected value for drilling either γ or δ), the game of two wells has a positive expected value and is economically attractive. The best results are when we follow the strategy shown in bold arrows in Fig. 2.16. We should first drill δ, then if it is a discovery, should move on to drill γ . Otherwise, we walk away. Following this strategy has an expected value of USD 2.8 million.

2.5 Conclusions Petroleum exploration and development decisions are multi-dimensional. They are often technically complex and have uncertain outcomes. However, with informed simplifications we could build useful decision models. We often start with the financial principle that the single goal of a firm is “shareholder value creation”. We then cascade this goal to project-level decisions. This leads to a straightforward business mindset: good decisions are those that create expected shareholder value. With this goal, we define decision frames that help us model the key uncertainties and make effective decisions. While everyone agrees with the above descriptions, the difference is in their implementation. In practice, there are never enough resources to study all the uncertainties. Collaborating with large teams of experts in corporations also has a “blinding” effect, we focus on more discussed rather than more crucial elements. Still, the analytic principle is a helpful guideline. We should select key uncertain factors based on our

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Fig. 2.16 Decision tree showing two-well exploration campaign and its best course of action

understanding of the context. We account for the remaining sources of uncertainty as the best guesses or “averages”. In the end, a useful valuation model should account for these “averages” as well as the key uncertainties and decisions. We need a comprehensive scheme to translate uncertainty to value. In addition, we need to have a simpler measure to show “shareholder value creation” as applying this concept to the project-level decisions is difficult. To tackle these challenges, we will discuss proxies like “expected present value”. The next chapter will discuss economic measurement of value.

Reference 1. Merrow EW (2012) Oil and gas industry megaprojects: our recent track record. Oil Gas Facil 1(02):38–42

Chapter 3

Economics of Decisions

Abstract All models are approximations. In this chapter we discuss key approximations for economic decision models. Finance theory shows the relationship between uncertainty and value. It tells us what we should expect in return for taking economically risky opportunities. This understanding is key to a consistent comparison of our courses of action. We will discuss that project decisions should be for the benefit of their real owners, the investors.

3.1 Introduction We think of risk and uncertainty as crucial factors in any investment. In the petroleum industry, projects have uncertain outcomes. An exploration well is a multi-milliondollar investment, and it is uncertain whether the outcome is a discovery or a dry hole. We discussed that for decisions with uncertain outcomes, the expected value should be the measure. But what is the risk of drilling a well or undertaking a project? Furthermore, is there a theory that consistently translates risk to value? We discussed that financial risk is the downside of uncertainty. With uncertainty, we could well end up with outcomes better than expected. These are the upsides. For example, instead of an average amount we could have an elephant discovery. The business mindset admits these upsides but concerns itself with the downside. Why? The general belief is that uncertainty could make or break a firm, while the risk could only break. There are no theories about what uncertainty we should face in the subsurface. Contrary to what many like to believe, we even do not have a theory about economic uncertainty. All we have are analogies, comparisons with similar events in the past that may or may not be relevant to the situation we face. Accounting for risk (and uncertainty) is a challenge for any valuation. Our understanding of the uncertainty will affect the value, but how much is its extent of influence? In the petroleum industry, we know that good news increases a company’s share prices. Adverse news reduces share prices. Yet, there is no theory on how much an increase we should expect, for example, from a hundred-million-barrel discovery of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Jafarizadeh, Economic Decision Analysis, SpringerBriefs in Petroleum Geoscience & Engineering, https://doi.org/10.1007/978-3-030-96137-4_3

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recoverable light crude oil in the North Sea, forty kilometers away from the existing infrastructure. We only have experiences from the past. This specific situation is unique.

3.1.1 Relative Valuations and the Law of One Price A basic principle of finance is that when there are many market participants, two identical items at one geographical location must have identical prices. Why? Because if they do not, then an opportunistic trader could buy cheap and sell expensive, making money out of nothing. Thanks to these opportunistic traders, in our experience such “free lunches” are rare. Therefore, the “law of one price” applies. Assume we have produced a barrel of oil in Northern Wyoming. What would be its value? We can compare our barrel with other barrels of oil in the area. If they are identical, they should sell at the same price. Obviously, not all barrels of oil are identical. They may have different chemical compositions or may be elsewhere. By accounting for these differences (adding premiums or deducting discounts) we could still use the law of one price and estimate the relative value of our barrel of oil. For example, for our barrel of oil in Northern Wyoming, we could look for comparable prices. The closest would be the price of a barrel of West Texas Intermediate (WTI) oil at Cushing, Oklahoma. This is a publicly available price. Our barrel of oil is close enough to WTI and should sell for a few dollars above or below. Up to this point, relative valuation using comparable assets works well. In most projects, we do not know how many barrels of oil we will have, or when we will have them. Finding comparable barrels of oil, having the same level of uncertainty and at the same point in time, may be difficult. Still, once we find comparable items, the valuation would be straightforward. Valuation is the art and science of estimating the worth of a course of action using comparable prices. Comparisons are across both time and levels of uncertainty. The art and science are all about consistently comparing different assets. We would make confused decisions without a good measure of worth. For any project, merely discovering and producing commodities are not per se worthy. The real worth is in the future cash flows. For example, a hydrocarbon discovery would be worthy only if it leads to development, production, and sales of hydrocarbons. Only then it would generate revenues that pay for its costs and further create value. To make good project decisions we need to know the value of those ensuing courses of action and their expected outcomes. For any project, there would be countless outcomes. One will eventually materialize, but we do not know which. How much effort should we spend on studying the alternative courses of action and their outcomes? For example, if we make small discovery, we could either develop it using tie-back solution, or a Floating Production, Storage and Offloading (FPSO) unit. Each course of action would have diverse levels of cost, production capabilities, and flexibility. In addition, our choice would

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later influence further decisions. Once we commit to a choice, “the die is cast” and it would be irreversible. A decision opens or closes the door to other sets of choices.

3.1.2 Black Swans in the Energy Industry When Europeans first set foot in Australia, they met a bird remarkably like those swans they had seen in Europe, except that it was black feathered. This observation led to updating biology books: a swan, previously thought of as white—because all earlier observations were white—could also be black. The expression “black swan” later became a symbol of rare and unexpected events that can deeply challenge the common norm and understanding [1]. The energy industry also experiences black swans. The discovery of Spindletop (1901, East Texas) brought the US oil industry to its prominence. The accident during drilling Macondo prospect (2010, Gulf of Mexico) ended in a disaster and an oil spill and caused immense losses. Black swans or not, it is unlikely that the decision makers formally assessed, or even addressed, these outcomes in their investment analysis. With all its uncertainty and complexity, project feasibility analysis hangs on a proper frame. We want to reflect all things to come. But our models could only reflect key factors. The rest we implicitly include as uncertainty-adjusted expectations. For example, the decision to drill an exploration well reflects the subsurface uncertainty, the development solution for the discovery, and the cash flows from all the costs and benefits beyond the discovery—including adjustments for the rest of the uncertainties. Too detailed or too brief? Then we should refine and reframe.

3.2 Comparisons Across Time All assessments use comparisons. We use analogies to compare factors based on their likeness. For example, when asked if you would like to exchange your pile of silver with a bar of gold, your immediate question should be: what can I get for each in the market? Here, you compare the appeal of two different items using a third comparable, money. Your decision to exchange silver with gold will depend on their market value—the prices per ounce of silver and gold times their weight. Without a meaningful analogy (here, the prices) it would have been difficult to make an exchange decision. We earlier discussed that assessments use comparisons and rely on the law of one price. Here we use these same principles to assessments across time. Assume that company X approaches you with an investment opportunity. They say: “Give us USD 1000 and we will pay you back USD 1050 in a year”. What could be the basis of comparison here? You face a comparison not across different items, but the same item (money) across different points in time.

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You need to compare the value of money today with its value one year from now. To see if the proposal is worthwhile, you could compare it with existing opportunities that take your money and promise a return in one year. The nearby bank, for example, pays back USD 1.1 in a year on every dollar invested today. It is a comparable opportunity. If they pay USD 1.1 for USD 1, then how much will they pay for USD 1000? Proportional to the investment. They will pay USD 1100—an amount higher than the USD1050 company X promised. You conclude that with the opportunities you have, investing in company X is not appealing. We can assess this investment opportunity from another point of view. You could say: if the bank gives USD 1.1 in a year for USD 1 today, then proportionally, to get USD 1050 from the bank I only need to invest USD 955 today. However, company X asks for USD 1000 today, for the same return. Their proposal is not appealing. Our comparisons across time hang on the ratio of today to future values. With this ratio, you could evaluate any investment over a year. You proportionally assess 1 1 = 1+r and r is the interest rate. Here, them. In economic analysis, this ratio is 1.1 the bank has offered you 10% interest on your investments. So far, our analysis compares costs and benefits of today with those of the next period (next year). However, it is straightforward to extend the comparisons to multiple periods. For example, to compare costs in present time with benefits from three years in the future, we simply need to discount the future benefits three times to find its present value equivalent. In general, any cash flow with the future value (F V ) at period t, has a present value (P V ) of PV =

FV (1 + r )t

(3.1)

In all our assessments, we have implicitly considered the concept of opportunity cost. By investing in company X’s proposal, we would have let go of the opportunity to invest in the bank. Therefore, we compare company X’s proposal with the bank alternative. In economic decision analysis, the concept of the opportunity cost underlies all valuations. Any investment is good when it outperforms our best available alternative.

3.2.1 Valuing a Series of Cash Flows Most projects have several costs and benefits over many years. To assess these projects, we extend the earlier concepts to multiple cash flows. The rules of analysis are simple. First, we only compare money in one point in time (often the present time, as this point of reference is more relatable). Second, we use the opportunity cost principle. We estimate the present value equivalent of future cash flows. Scallop oil has discovered hydrocarbon resources close to an existing platform. They believe that one extended-reach well that costs USD 80 million could extract oil from this reservoir. The well will produce for four years generating USD 25

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million net income per year. Net income means that we have already subtracted the incremental taxes and costs for running the project. If Scallop’s other comparable projects generate 8% return, is this a favorable investment opportunity? Time Period (Year)

0

1

2

3

4

Cash Flow (USD Million)

−80

25

25

25

25

The present value of all cash flows would show the economic appeal of this project. If the sum of all present values is positive, then the benefits outweigh the costs, and the project is appealing. But if the sum is negative, then undertaking the project destroys shareholder’s value. 25 25 25 25 + + + 2 3 (1 + 0.08) (1 + 0.08) (1 + 0.08) (1 + 0.08)4 = USD 2.8 Million

Project Value = −80 +

We apply 8% as the opportunity cost for this extended reach drilling project. The project value is USD 2.8 million, and it is an appealing investment opportunity. The Net Present Value (NPV) is a decision measure for investments. Because it shows the net effect of all the costs and benefits, by accepting positive NPV projects we add to shareholder value. In addition, by rejecting negative NPV projects we avoid loss. In our analysis, we already knew of a comparable project so that we could estimate the opportunity cost. In practice every project is unique. It is less straightforward to pinpoint the opportunity cost of capital (the discount rate). In addition, forecasting is not an exact science. It requires human judgement. For all our natural inability to forecast, and our behavioral tendencies to put up the appearance of professional practice, forecasting cash flows will always remain subjective and uncertain.

3.3 Comparisons Across Different Levels of Uncertainty Any corporation, including energy firms, should want to create value. Any decision they make should then play a part in achieving this goal. If a course of action leads to value creation, then we consider it, otherwise, we walk away. Yet in practice, this is easier said than done. There are myriads of intermediate goals. In theory, an intermediate goal is a stepping-stone to the final goal of value creation. Yet finding such a link in practice is difficult. For example, does employee compensation enable value creation? You may argue that higher compensations mean that less money goes to shareholders. But higher wages lead to happier and more competitive workforce that eventually

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generate more value than the low-paid, disgruntled employees. As in other intermediate goals, reaching the sweet spot for the goal of employee compensation is difficult. We believe it is helpful to know of the link between intermediate goals and value creation. Winning a series of battles wins the war, but not necessarily every battle. Knowing that, for example, employee compensation contributes to the higher goal of corporate value creation helps us in defining and assessing this task. It may be hard to show the link, but at least knowing where we want to go is helpful in formulating the smaller steps.

3.3.1 Define Goals from the Point of View of Investors A mid-manager in an energy company has the power to make decisions about, e.g., drilling exploration wells. For her, it is enough to know if the wells discover hydrocarbons. Her senior manager welcomes this, but nevertheless, wants to know if they spend the drilling budget well—whether drilling generates corporate value. The next time they attend a corporate meeting they can point out their success and ask for even larger budgets. The chief executive officer, the highest rank in management, wants to know if exploration drilling is successful. If not, they could spend the capital elsewhere. In their other lines of business that better generate value. The investors invest their capital in their company and want to know if they made the right choice. Otherwise, the investors will invest elsewhere. To define goals, we should take the point of view of the real owners—the investors in the firm. They decide where to invest and look for promising opportunities. A firm with investors must have been (or looked like) an attractive opportunity. The projects that the firm undertakes, the management skills, and the internal machinery of the company, all seemed financially appealing to the investors. All the decisions the company makes should serve the investor’s goals. They want to grow their capital. Therefore, the company’s decisions (including hydrocarbon exploration and development decisions) should enable this. Projects with positive net present value collectively grow the investors’ capital. Companies should take courses of action with a positive net present value. This contributes to the goal of value creation. However, the business world is uncertain. Promising projects could still have bad breaks. Through dumb luck, inferior decisions could create value. In an uncertain world, specific outcomes are not a measure of competency. A better indicator of success is the quality of decision making—reflecting the way of

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making decisions on the long run. The investors know these realities of the market. They like capital growth and they dislike risk.1

3.3.2 Market Data to Measure Risk and Return To assess the subsurface uncertainty in a project, we look for comparable experiences. We study seismic data, nearby drillings, and any other relevant source of information. To assess the market uncertainty, the same principle applies. We study any worthwhile source of information. Here it seems history (financial data from the past), is often the only available source. Studying financial data from the past more than anything reveals their random variations. It is almost impossible to predict the prices of crude oil or corporate stocks by studying their past. Therefore, we say prices are uncertain. Yet, uncertainty is not risk. As the adage goes, “risk is in the eye of the beholder”. The markets are always uncertain, but they are not necessarily risky for someone with no exposure to the markets—like someone with a steady income who does not invest. To assess the risk in an investment, we need to consider the point of view of the investors. Often, the investors invest their capital in multiple assets. These would be companies each with multiple projects. For the investors, their aggregate risk matters. Hence, to assess the risk of an investment, we need to study its contribution on the investors’ aggregate risk. We can assess the investors’ risk using the historical trend of prices. For good assets prices on average increase but for bad assets they fluctuate and decline. Investing in good assets make money grow. Bad assets, being risky, make money decline. Of course, history is but one realization of what could have happened. There were other possibilities that did not happen. Historical data at best paint a partial picture. Like in geology, economics also deals with insufficient data and relies on human judgement. Assuming the investors start with USD 1 in the year 2004, Fig. 3.1 shows the historical performance of several assets: a small petroleum stock, a large and integrated petroleum stock, a portfolio of major petroleum stocks, and the market index. These assets’ rate of return—what is relevant to a long-term investor buying assets and holding on to them—shows that by 2017 the small petroleum stock over-performed, but the market index under-performed. Yet, compared to the steady path of the market index, the small petroleum stock wildly fluctuates. Its price at end of the study period is higher, but the investor has experienced an uncomfortable bumpy journey. To a long-term investor, this is risk. High return comes with a higher chance of loss.

1

To assume the investors dislike risk (risk-averse investors) is a tenet of corporate finance. Given a choice between receiving a sure USD 1000 and receiving a deal of USD 0 or USD 2000 with equal chances, the risk-averse investor always prefers the sure USD 1000.

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Fig. 3.1 Compound rates of return for market and petroleum shares (monthly returns from COMPUSTAT)

The graph reveals several aspects of risk and return. First, all prices vary, reflecting inherent variability of markets and their economic dynamics. We can even see the footsteps of major economic events (like the crisis of 2007–2008) on the prices of all assets. Second, some asset prices fluctuate more than others. They are more likely to cause financial ruin to their investors. Some asset prices vary less and are less risky. Third, due to an averaging effect called “diversification”, usually a portfolio of stocks is less risky than the individual stocks. Diversification is a key effect in studying risk and return. Even companies with larger portfolio of projects experience this effect. For them, many projects (ranging from exploration and production to downstream) average out the overall financial return. Superior results of a project could compensate for the inferior performance of another. On the other hand, companies with less diverse projects enjoy less of this effect. With the inherent uncertainties, success or failure in any individual project directly reflects in their financial performance. Price variations cause the risk of loss. The risk-averse investor uses all means available to avoid loss. Yet, when the economy is in trouble, most assets will be in trouble. When the economy is booming, most assets perform well. This general correlation causes co-movements in the markets. Most investors become rich or poor in somehow a parallel direction. Only if an investment has a reverse behavior (increasing in value when everything else decreases), then the investors could shield themselves from downturns. The sad news is, such an asset will have poor return when others are doing well. We can study the risk of assets using beta, a measure of co-movement with the general direction of the market. Some investments have strong links with the market while others show less dependence. This will show in their beta measure. By plotting the returns from a stock across returns from the market portfolio (a portfolio of major stocks like S&P 500 is a good proxy for market portfolio), the slope of the fitted line would reflect the co-movements between that stock and the market. For example, a slope close to one would mean that the price of an asset moves in tandem with the market. Such a stock will perform well when the market is doing well and badly when the market is in trouble. A slope above one means the stock tends to

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49

magnify market behavior. It will perform very well in good times and disastrously in troubled times. Finally, a negative slope means the price of the asset tends to move in the opposite direction with the market. If included in the investments, it will shield us against market downturns.2

3.3.3 Beta and the Capital Asset Pricing Model In April 2020, at the onset of the coronavirus pandemic, the price of West Texas Intermediate (WTI) crude oil suddenly turned negative. This sent shockwaves around the world. Shares of petroleum companies plummeted, and their employees’ job securities eroded. Crude oil prices reflect a global picture of supply and demand. Changes to this picture prompt changes in the value of petroleum companies. Still, some independent ticks in the companies’ stock prices could be in any direction. For example, the news about an oil discovery, an accident, or a license award, affects the price of a specific stock but is unlikely to affect the entire market. Unlike the coronavirus pandemic, some good or bad outcomes are independent from the market. For most companies, only a proportion of their stock price ticks are along the general market direction. Investors could reduce their exposure to the independent movements through “diversification”. They place bets on multiple assets, hoping that the good outcomes in some companies compensate the bad outcomes in others. This diversification effect follows the law of large numbers. The more stocks in a portfolio, the more cancelling out of independent movements. In the limit, diversification removes the independent price ticks and leaves only the co-movements with the general economy. How much diversification is ideal? A truly diversified portfolio includes all assets in existence in all markets. It is an impractical concept. Yet, we could get most of the benefits of diversification by “diversifying enough”. For example, a proxy like the S&P 500 includes major assets from major industries. In Fig. 3.2 we show diversification in practice. We combined stocks of companies in the upstream petroleum industry. But this could only take us so far. The market index holds stocks of companies in varied sizes from multiple industries. For practical purposes, the market index is devoid of independent risk. It only reflects the market risk. We simply added stocks to our investment portfolio and reduced our exposure to independent risk. It costed us nothing. If we can easily do this, so can any other investor. This means companies should not compensate the investors for taking independent risks. The investors can themselves remove the independent risk by investing in a diversified portfolio. It is easy and free.

2

Only a few assets—gold, and some other rare earth minerals—have slightly negative betas. This explains why historically everyone rushed to buy gold when economies were in disarray.

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Fig. 3.2 Diversification Removes the unsystematic (independent) risk (data from COMPUSTAT)

Fig. 3.3 Returns on Concho Resources (CXO) and Chevron (CVX) against returns from S&P 500. The slope of the fitted lines shows their beta (data from COMPUSTAT)

Any reward or compensation should be for taking the market risk. A company should compensate its investors for the amount that it adds to their total market exposure. The capital asset pricing model builds on this principle. The beta of a stocks measures the co-movement of the stock price with the market. It is a measure of the market risk exposure. Therefore, the compensation for investing in a stock should merely be in proportion to its beta. As we show in Fig. 3.3, two assets with diverse levels of overall risk could still have similar market risks. For example, the stocks of Concho Resources (beta = 1) and Chevron (beta = 0.9) expose the investors to almost similar amounts of market risk. The added risk that an investor assumes when investing in Concho Resources is diversifiable—therefore, not rewarded. The Capital Asset Pricing Model (CAPM) builds on this principle. Risky assets should have a return higher than a risk-free asset.3 The excess return should be proportional to the excess exposure of the asset to the market risk. The expected return E(r ) of a stock with beta β should be   E(r ) = r f + β E(rm ) − r f

(3.2)

where r f is the risk-free rate—showing the time value of money—and E(rm ) is the expected return on market portfolio. 3

A risk-free asset could be a certain asset, like the US government’s treasury bonds.

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In simple terms, the CAPM states that the excess return on any asset should be proportional to its beta and the market risk premium E(rm ) − r f . An asset with beta = 1 should have the expected return of the market. Investing in an asset with high beta should have a higher expected return than a low-beta asset. With the CAPM reasoning, we now know how much return an investor should expect from a company. The more market risk, the higher the expected return. This is like viewing a company from the lens of the market participants. They do not need to worry about the subsurface, drilling, development, production, and the technicalities of the operations. They merely need to worry about the firm’s co-movement with the market.

3.4 Company’s WACC and the Project Discount Rate Investors buying Chevron stocks (beta = 0.9) should expect 8% annual return. This is assuming a risk-free interest rate of 3.5%, a market risk premium of 5%, and that the CAPM applies. Does this mean the investors will receive 8% return? No. The average or the expected return is 8%. In practice, the investors may get a return higher or lower than 8%. With uncertainty, we should never expect the expected. For an investor, the capital asset pricing model shows how much they should expect to receive on a company’s stocks. It simply shows the reward for the risks. Because the investor could buy a variety of other stocks, this rate reflects the opportunity cost of owning a stock. For example, if the investor could buy stocks of Chevron (CVX) but instead buys the stocks of ExxonMobil (XOM), then because the risk of these two stocks is almost comparable, the return on Chevron stocks reflects the opportunity cost of owning ExxonMobil’s stocks. With this view, we can assess the value of firms. If a firm raises money by merely issuing stocks, then the CAPM equation tells us the opportunity cost of owning this company. By discounting the total cash flows of the company using this rate, we would have the market value of the company. A company that raises its entire capital using debt would have a slightly different valuation. For example, if the loan interest rate is 5%, then we could use our capital to pay back the debt. The loan interest rate would be the opportunity cost of owning the company. Any cash flow from the firm should first pay back the loan instalments. The rest is profit. Yet, because we do not pay tax on the interest of a loan, we will experience a lower rate than the loan rate. To evaluate this company, we should discount the cash flows with 5%(1 − T ) where the corporate tax rate is T (the bank will still receive 5%). This shows why debt financing—at least in low proportions4 — is appealing. 4

Paying debt back is a contractual obligation, but paying return on equity is optional. This means companies must pay debt installments all the time, during both good and troubled times. In tough times, a company with too much debt may not be able to cover the instalments and risks bankruptcy. Yet, under the same conditions a company with less debt would face less difficulty. In summary, debt is good because it offers tax savings but too much debt also increases the risk of default.

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Not all companies raise their money through just equity (public stock offerings). It is expensive. They also rarely debt-finance their entire operations. The penalty for overdue payment and the higher risk of bankruptcy are the major deterrents. Instead, most companies use a mix of debt and equity. With both debt and equity, we should assess the companies’ cash flows using a discount rate that accounts for the joint effects of sources of financing. This rate would be the Weighted Average Cost of Capital (WACC). It accounts for the aggregate effect of all opportunity costs: any source of finance that the company pays interest on, and their importance (market weights). For a company with equity and debt, WACC would be WACC = r E

E D + r D (1 − T ) E+D E+D

(3.3)

Here,E and D are the market values of debt and equity. Their expected interest rates are respectively r E and r D . The market value of all claims on the company, both equity and debt, is E + D. The ratios show the importance weights for the sources of financing in the mix. For example, the opportunity cost for a company with more debt than equity would be closer to its cost of debt.

3.4.1 WACC for Company Valuation Scallop Oil, a mid-sized upstream company, is up for sale. An investment-banking analyst has done a “back of the envelop” calculation for this opportunity as part of a screening process. This shows which opportunity to leave and which to pursue. The estimated annual financial elements are as follows. Annual Revenue

USD 10 million

Minus: Operating Costs (OPEX)

USD 5 million

Minus: Depreciation

USD 1 million

Earnings Before Interest and Taxes (EBIT)

USD 4 million

Minus: Taxes (25%)

USD 1 million

Earnings After Tax

USD 3 million

Plus: Depreciation

USD 1 million

Minus: Capital Costs (CAPEX)

USD 1 million

Scallop Oil’s Free Cash Flow

USD 3 million

Scallop Oil uses a loan with 7% interest to finance 50% of its operations. The rest of finances are from Scallop’s shares in the market that have beta = 2. They are riskier than the market portfolio. For 3% risk-free interest rate and 8% return on the market portfolio, the equity investors should expect 3% + 2(8% − 3%) = 13%

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return (using the CAPM equation). The weighted average cost of capital for Scallop Oil, using Eq. (3.3), would be 7% × 21 + 13% × 21 = 10%. The assess the value of Scallop Oil—including the debt and equity claims on the company—we assume the annual free cash flow is constant every year and continues for eternity. Then the present value of USD 3 million constant annuity with 10% 3 million = USD 30 million. discount rate would be USD10% Most energy companies invest in a variety of assets with diverse levels of risk. Their assets may include highly uncertain exploration opportunities, the capitalintensive development projects, or safer bets like pipeline projects. The valuations should make them all comparable. Most companies use the WACC to account for the risk in their projects’ cash flows. They are often well-aware that the WACC is an average measure. It accounts for the aggregate risk. It may or may not apply to specific projects. Valuing all projects with WACC would in practice mean favoring riskier projects over safer bets. Why? Because we discount the cash flows of riskier projects with a low discount rate and those of the safer projects with an unnecessarily high rate. The mismatch could cause trouble. There are however practical benefits to this one-size-fits-all choice of discount rate.

3.4.2 Project Discount Rate “This pipeline project has only 9% rate of return. How could I approve this when our financiers expect at least an overall 10% return?” said the CEO of Scallop Oil to her business development manager. For years, Scallop had owned the production rights in an area that they knew had a logistical importance. It was on a strategic corner—on the path, some would say the only path, of the upcoming production in the east, streaming to the processing facilities in the west. But with the unfavorable economic analysis of the pipeline project, it seemed that they had misplaced all their hopes. The analysts estimated that the expensive pipeline from east to west, collecting tariff income from passing production over many future years, had only 9% return. “Admittedly, this is a new type of project for us. In the past, we had exploration, development, and production projects. They all had similar risk patterns. But even though we have the technology, this is the first time we want to invest in a pipeline.” Said the business development manager. The pipeline project simply is not their core business area. Yet, the engineers are confident that they can apply their knowledge and skills to a pipeline project as the technologies are comparable. The risks, however, are not comparable. The risk and return from a pipeline project are markedly different from exploration or development. Here, the project income is in the form of tariffs. By letting others use the spare capacity in Scallop’s pipeline, they collect a fee for transporting fluids. So long as the neighbors continue producing, tariff income will not be risky.

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The analysts argue that: “once we build a pipeline, the income for the next twenty years or so is practically guaranteed”. “Well, perhaps not entirely guaranteed as the production in adjacent areas still depend on oil and gas prices.” The business development manager replied. The analysts asked for economic measures from pipeline industry—with an average beta = 0.8—to use in this project. At 3% risk free rate and 5% market risk premium (and assuming 50% financing with 7% debt), the least acceptable rate of return for the pipeline project would have been 7%. Had Scallop been in the pipeline business, this project could have been favorable. Should the CEO accept a project with a return lower than the company’s WACC? The answer depends on many factors. For this pipeline project the answer is: yes. To make sound investment decisions, we need to assess a project with its proper WACC. With the discount rate of 7%, the project has a positive net present value (as the internal rate of return is 9%). In addition, the new pipeline project will become part of Scallop’s internal portfolio. This project will transform Scallop into a company with both upstream and midstream assets. For example, if Scallop invests 50% in the pipeline business (beta = 0.8) and 50% in upstream business (beta = 2), then the new assets will decrease the company’s WACC. If the debt is still at 50% financing, the new WACC would be the weighted average of WACC in pipeline and upstream businesses, 50% × 7% + 50% × 10% = 8.5%. Assessing the pipeline project using the new WACC leads to favorable results. If they approve the pipeline project, it will increase Scallop’s shareholder value. The firm’s WACC is an overall rate—a blanket rate treating all projects as if they are average. It applies to the assessment of the whole. It also applies to the assessment of average projects. However, to assess a project with a distinctively different risk profile than the average, we should apply a project specific WACC. In practice, every project is unique. Every project has a risk pattern unlike any venture ever taken. Otherwise, we would not call it a project. Hence, every project justifies using a different WACC. This makes perfect sense in theory, but as we discuss next, it may not be a clever idea in practice.

3.4.3 Influence Costs, Hurdle Rates, and Experience “If we leave employees’ powers unchecked, we will end up with a few hostile kingdoms within our organization.” once said an analyst. In any organization, those with more authority gradually influence decisions in their favor. Even employees of limited influence could still lobby for preferred projects—helping them or their circle of peers get power, eminence, or reputation. They may not necessarily pursue personal benefits. They could even have noble intentions. Yet, they often act against shareholder value creation. For example, the employees lobby for a project because they seek the experience that would increase their overall utility. Or the experience may put them on a wall

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of fame. Whatever the reason, lobbying for projects is costly and is (generally) against shareholders’ value maximization. Research in organization theory has also confirmed the existence of such “influence costs”. In this context, the freedom to choose the discount rate for a project could be both liberating and dangerous. In the hands of lobbying employees, the project-specific discount rate could be a bargaining chip, giving them more degrees of freedom to force their favorite courses of action. On the other hand, in an unbiased environment, the freedom to select a project’s discount rate leads to consistent valuations and superior decisions. We believe the project-specific discount rate is a worthy idea. But only if the firms build the proper work process. We also believe that firms do not necessarily cut the influence costs by assuming a constant discount rate. Instead of adjusting the discount rate to their benefit, the lobbying employees could still adjust the (rarely reviewed) risky cash flows and achieve the same intentions.

3.5 Making Valuation Models Every valuation is about the future, and the future is uncertain. Valuations, and the decisions based on them, are as good as the logic and assessments that goes into them. There are also elements of intuition, experience, and art that make some valuations stand out. Unfortunately, no easy guideline exists for good practice. The experts’ judgement makes up the basis of corporate decisions. In addition, the corporate analytical capabilities, their decision-making process, and luck, lead to the failure or success of the business. In this section, we combine all the components from earlier sections to devise a consistent valuation and decision model. Unlike the earlier components—estimating the discount rate, assessing subsurface probabilities, or calculating the expected production—forecasting a project’s cash flows is much less structured. The reason is that every opportunity is unique. The ingredients make up the whole, but there is no universal guideline on how to combine them in a new project. It is up to the discretion of the analysts, engineers, and managers to devise a sensible valuation story for projects, and then to assess its economic consequences.

3.5.1 The Cash Flow Profile Estimating the expected costs and incomes, also known as pro forma cash flows, is not trivial. First, the story should make sense. Every project has a start and an end, there would be turn of events and possibilities that affect the project. Even though it is impossible to consider everything, we still need to show a reasonable story from the beginning to the end. Second, the context of the story and its characters should

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Fig. 3.4 Cash flow profile telling the expected story of development, production, and termination

also make sense. The context includes: the geographical location of the project, the firms undertaking the project, and their capabilities. For example, to forecast the cash flows for a deep-water development project in eastern Canada by a mid-sized company, the analysts need to consider a myriad of issues. These include the development technology, experience with such a technology, the possibility of mobilizing the workforce, and access to the working capital. These issues would help in making the project schedule. The project schedule would show the lead time (a period of investment and no income) and the production plan. In addition, depending on the products (crude oil, natural gas, or a mixture) the analysts would need to think up customized production and export solutions. Finally, they could show a cash flow profile telling this expected story, as in Fig. 3.4. Note that the analysts have already dialed their technical and economic judgement into this story. For example, the decision about the size of the production facilities depends on the expected reservoir size and prices. It would also depend on what is available. The cash flow profile in the figure includes these provisions. The figure tells a story as: we expect the production to start in five years and ramp up to a plateau—this is the maximum production capability of the facilities. During this plateau period, the production potentials of the reservoir exceed those of our facility. Therefore, we could only produce at maximum production rate. We could have designed a larger facility, but this would be wasteful as it would be under-used for the rest of the project life. As the reservoir depletes, its pressure drops, and the production gradually declines. In this story, the analysts and engineers have decided on the “right” size of the facilities. Both over-sized and under-sized designs would have their own economic drawbacks.5 5

An over-sized development plan would shorten or even avert the plateau period. The firm could produce hydrocarbons as fast as possible, not hampered by the limitations of the equipment. Yet, such a facility would take longer to build, and inevitably, more expensive. The resulting cash flow profile with larger capital costs and more positive cash flows but further in the future, would lead to a lower net present value. Similarly, an under-sized development would be less expensive but

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The first principle for building valuation models is that projects should only reflect the incremental costs and benefits. Only those costs and benefits that are the result of undertaking the project should enter our analysis. Any other item that independently appears (or disappears) is irrelevant. The incremental costs and benefits should show the contribution of the project to the firm. The second principle is to ignore the sunk costs—the costs that we have already paid for or will pay for anyway. Such costs are not incremental and are hence, irrelevant. All project decisions have a forward-looking scope. Things that happened in the past and things that will happen in the future but do not have a bearing on the decisions are “sunk items”. They should not enter our economic analysis.

3.5.2 The Sunk Cost of an Exploration License To explore a region, companies first need to get the exploration right. They often send a closed (or sometimes open) envelop of their bid to the host government. The government then decides which of the bidders should get the right to explore. The winner gets to run exploration activities (like running seismic surveys and drilling wells) in a specified area within a specified period. Scallop oil bid USD 100 million and won an offshore exploration tract in a recent bidding round of US Gulph of Mexico. While the available information shows a promising tract, executives in Scallop think that they have bid too much. They suffer from the winner’s curse. They also believe that a mere average discovery is not enough to cover these excessive bid costs. They think they should not drill unless the expected discovery is large enough. They ask the geologist and analysts to only look for large accumulations. Should they adjust the minimum economic volume in this license based on the excessive bid cost? No. Here, the cost of getting the license is irrelevant to the drilling decision. They already paid the cost. Irrespective of whether they drill, the cost is still there! By adjusting the minimum economic volume for the bidding cost, they will fall into the sunk cost trap. But if Medallion oil (a rival oil company) proposes to buy the exploration rights on this tract, then the incremental cost of drilling for Scallop suddenly changes. For example, if Medallion offers a one-time amount of USD 50 million for this tract, then drilling for Scallop now means forgoing the opportunity to sell. The USD 50 million offer from Medallion oil becomes the opportunity cost of drilling for Scallop. Some projects are straightforward. We could readily to them apply our experiences of working in similar environments or with similar technologies. Still, even with similarities every project is unique. Experiences are not directly transferable. Often projects have “first times”.

will lead to a much longer plateau period and an overall longer production. This will also be a less economically favorable scenario.

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This means, estimating project variables and the future cash flows for every project (including the routine) has challenges. It is more challenging for unfamiliar projects. With uncertainty, predictions would turn out to be wrong. Earlier we discussed that project outcomes are uncertain and suggested modeling the key uncertainties. We use the language of probability to describe these uncertainties. For example, we say: “We assign 30% chance of success for drilling this prospect”. We then assume a successful project would have expected future cash flows. To assess the value of the successful project, we discount these cash flows with a rate that accounts for the time value of money and their risk. Again, we are using an embedded probability language. For example, if the project succeeds, with 50% chance it will have high production and will generate USD 30 million revenue for four years. Also, with 50% chance it will have low production generating USD 20 million revenue. Then the expected revenue would have been USD 25 million per year, for four years. In other words, the expected revenue is the probability weighted average of the possible revenues. Probabilities and expectations are common in valuation models. Yet, unbiased expectations are rare. For a variety of conscious or unconscious reasons, instead of expected cash flows we could end up with optimistic or pessimistic estimates. For example, the geologist who is looking for petroleum prospects and the manager who decides to drill, often have a natural tendency to show a hoped-for (not the expected) view of their project. There are usually more prospects than is possible to drill. Such an optimism increases their chance of project approval. On the other hand, if bonuses depend on performance, then for approved projects the managers tend to have pessimistic views. This will later create the illusion of overperformance.

3.5.3 Valuation and Behavioral Issues The aggregate effect of the early optimism and later pessimism, leads to biases in investment. We would have managerial enthusiasm for drilling prospects. But later a reluctance to develop the discoveries. This means, we will end up with many undeveloped marginal discoveries waiting for favorable conditions in the future. For some of those discoveries, the favorable conditions never come. Not all ideas are business worthy. It is normal to start with many prospects and end up with few value-creating projects. However, such behavioral issues (like estimation optimism, pessimism, overconfidence, or employees’ influence) could make a dent in the normative decision-making process. The biases increase the rate of failure. Hydrocarbon exploration is a game of chance. Uncertainty leads to surprising results. There will always be dry holes and lost value. But companies do not want to lose value for behavioral biases in addition to the odds of the business. Most companies are aware of their optimistically enlarged cash flow estimates. They politically prefer not to declare their experts as biased and try to compensate their optimism through other measures. For example, companies assuming a high discount rate to cancel out their experts’ optimism.

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We agree with these real-world limitations. We also understand the firms’ efforts to offset the biases. Yet, we still believe that understanding the explicit effects of behavioral biases and their impact on decisions goes a long way in improving investment decision making. Even with its cognitive biases, human judgement is currently the only source of decision insight. It would be years, even decades, before any non-human form of intelligence could generate significant project decision insight. Until then, the decision makers should make the best of human judgement and be wary of its biases and limitations. A consistent valuation should account for key issues. There are many uncertain factors, value drivers, and decisions. But for practical reasons we must only focus on those that affect the value the most. This calls for careful model building.

3.5.4 The Dilemma of Energy Projects Once we undertake a project, we start learning about it. This learning is often “insufficient” to know the outcome for sure. We would be uncertain about the outcome up until the very last moment. Only then we would completely “know”. For example, the Original Oil in Place (OOIP) of an oil accumulation is key in making decisions. It is most uncertain at exploration and development stages, the time we make major project decisions. With discovery, development, and later production, we will learn about the OOIP, but we will not completely “know” up until the end of the project. By then, the information is worthless. It will not do any good for the project. This is the dilemma for most energy projects. In the beginning, when making major project decisions, information is scarcely available. In the end, information will be abundant, but it will be of little use. In most projects, information availability increases but its usefulness decreases. To deal with the dilemma, we must understand the uncertainty and to reason under uncertainty. A good decision model is not the most comprehensive. Too much detail would make models so large and complex that defy their primary purpose of clarity and communication. The decision of which factor to model (and which to leave) is the main decision in building a decision model.

3.5.5 Making Models: Granularity To make a wildcat drilling decision, we may choose to explicitly model the chance of success and the uncertain recoverable volumes. We think of several representative outcomes. We then show each outcome as a project with expected cash flows. Yet, discoveries could be of any size—not just the few outcomes we consider. We could find as little as few drops of oil to as large as elephant formations. In addition,

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Fig. 3.5 Decision tree model for drilling a wildcat well (if we had the time and energy to include many relevant factors)

there are myriads of decisions we could make for a discovery to make money. We could consider several development schemes, production strategies, and oil prices. This means that we ideally want to model the project with a decision tree as in Fig. 3.5. Because a decision tree cannot show continuous uncertain variables, we used a shorthand convention for the variables “discovered volume” and “prices”. The decision tree shows the uncertainty and the managerial flexibility. It depicts the decisions the managers make as they react to the unfolding events. The decision to drill a wildcat well has an uncertain outcome. It could either be a dry hole or a discovery. With a discovery, we learn that the hydrocarbons exist. Yet, we would still be uncertain about its size. In addition, we would not know of the future prices. Still, we should decide which (if any) development solution to take. Each end node of the tree stands for a scenario. It would show a development and production project with specific cost structure and production capabilities under a realization of future prices—bringing about an expected cash flow stream. The Net Present Value (NPV) of the cash flows then shows the economic worth of each scenario. The challenge is to show several representative scenarios—out of infinite scenarios. While it is difficult to universally agree on the term “representative”, most decision makers intuitively agree that if we are to select only one scenario from a range of possibilities, it should be the average (also referred to as “mean” or “expected value”). If we have the luxury to consider more than one scenario, then perhaps we would settle for guesstimates from low, middle, and high regions of the spectrum. The industry has learned to discretize continuous distributions, replacing them with approximate discrete distributions. For example, Fig. 3.6 shows the Swanson’s discretization approach—a common method of approximating a continuous distribution with a three-point discrete distribution. Here, for low, medium, and high representative scenarios we select respectively the tenth, fiftieth, and ninetieth percentiles of the continuous distribution. We respectively assign them the probabilities of 30%, 40%, and 30%. The resulting threepoint discrete distribution keeps some of the statistical properties of the continuous distribution but is much easier to work with.

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Fig. 3.6 Swanson’s approach to approximate a continuous distribution with a three-point discrete probability distribution

This means we can replace a continuous probability distribution with its approximate discrete equivalent. In the earlier example, we can apply the Swanson’s method to the continuous variables “discovered volume” and “future prices” to replace them with three representative scenarios. The result would be the simpler decision tree in Fig. 3.7. Still, this decision tree may seem un-manageably large. We could not show the complete tree in one page, so we used three dots in some places to implying that the trend continues. There would be sub-branches where we left three dots.

Fig. 3.7 The decision tree with discrete probability assessments

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Higher model granularity means more details, the higher the better. Within our resources, we could consider as much detail. But this would not be an efficient use of our resources. We could end up with too detailed models, leading to the same decisions that we could make with simpler models. Model design is itself a major decision. Our models should be useful, material, and altogether economical. If we make the same decision irrespective of the level of details, then the time and effort we spend on adding features to the model would be in vain. A model should show the right level of detail so that it supports decision making.

3.5.6 Making Models: Flaw of Averages If you have one foot in the fridge and the other in the oven, then your average temperature would be fine. Yet, it would be an uncomfortable situation. Another anecdote is about a statistician who drowned as he tried to cross a river that was on average three feet deep [2]. The lesson we learn from these stories is that: the “average” could be a poor decision measure. Thinking only in terms of averages could be harmful or dangerous. However, without using averages, the decision models would become un-manageably large. Especially for energy projects, we would never have enough time and resources to study every scenario. As a practical solution, we have learned to replace the infinite outcomes with a few “average” scenarios. The average is an approximation tool. It replaces a spectrum (or a range) with one representative number. Good modelling needs judicious use of this simplification tool. The trick would be to use the right balance of averages versus the explicit enumeration of scenarios. Assume Scallop oil wants to drill a wildcat well that costs USD 15 million and has 30% chance of success. The term “success” includes expected or “average” assumptions. It includes expectations of in-place volume, recovery factor, production, and even future oil prices. The expected discovery (with many averages) would use an FPSO development. It would export its production through an offshore buoy. It takes years (and costs millions of dollars) to install the equipment. Then we expect years of production revenue. The net present value of such an expected project is USD 30 million. The decision tree in Fig. 3.8 depicts this investment decision. With these figures, the expected net present value of drilling is negative—Scallop should not drill this wildcat well. However, the Scallops president believes that the assumption of “expected discovery” does not do justice to this opportunity. The average discovery includes, for example, the average reservoir thickness. If the geometry of the trap is such that even with the tenth percentile of reservoir thickness, they would still end up with a decent hydrocarbon accumulation, then this opportunity would have asymmetrical value. Large value for optimistic cases, and still decent value for pessimistic cases.

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Fig. 3.8 Decision tree model of exploration drilling considering an average discovered volume. The arrows in bold show the best course of action

The president suggests considering high- and low- reservoir thicknesses in their analysis. Instead of an average discovery they could have two possibilities. They will develop the low-volume discovery (80% chance) with an FPSO and buoy solution, leading to the net present value of USD 25 million. The high-volume discovery (20% chance) justifies a stand-alone permanent platform and export solution, resulting in the net present value of USD 100 million. The decision tree in Fig. 3.9 depicts the president’s view. With the increased level of detail, the model shows that it is worthwhile to drill the wildcat well. This is mainly because previously the “average” assumption was hiding significant asymmetries. With added detail, we better reflect the insights about trap geometry and its value potentials. We discussed that models should have the right level of details. But how much is the right level? There are no simple answers. The most logical way is to start with an acceptable frame, understand the relevant sources of uncertainty, and then revert and revise. We model key factors for decision making. The decision models cannot be impartial. Models that describe the uncertainty should inform the decisions. The decisions should also inform the modeling of uncertainty. It is a two-way street. The best workflow would be to assess and then to refine the models within the confines of our analysis resources.

Fig. 3.9 Decision tree model of exploration drilling when we consider high- and low-volume development scenarios. The arrows in bold show the best course of action

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3.6 Commodity Markets and Their Valuation Insights To forecast a project’s cash flows, we need price forecasts. Yet forecasting is not easy. Changes in economic conditions lead to changes in forecasts. This, ceteris paribus, leads to changes in cash flow forecast. We do not have a crystal ball. We know that our forecasts never materialize. There would be endless eventualities, and a price forecast is but one of them. If our goal is to just to forecast, we will inevitably have surprises. We forecast not just to forecast, but to support investment decision making. Therefore, we should not judge the quality of forecasts based on how “true” they came to be, or how close to the actual prices they were. Forecasts should support valuations. They are a statement of uncertainty. We reflect our best understanding in (uncertain) price forecasts. Bad forecasts confuse and mislead the investment decisions. Good forecasts are the best manifestation of information and are useful in decision making.

3.6.1 Uncertain Prices in Corporate Culture “Your analysis is on flimsy foundations, change one assumption, like price forecasts, and the whole rationale breaks down” said the manager dryly. Her team had presented a month-worth of analysis, and suggested investing in an expensive development solution. The sensitivity analysis that usually go with such reports revealed a hyper-sensitive project. A slight change in price assumption would change everything. “We didn’t make anything up, we received price forecasts from the office of the chief financial officer” said the analyst, with both palms up, as if to avoid the blame for not-so-robust analysis. “I understand it is uncertain, prices could still change tomorrow, your guess is as good as mine” the manager replied. She then added “such strong assumption about the future price trend overshadows all other factors”. “Respectfully, our guess is not as good as the experts’ opinion.” said the analyst. The analyst then continued: “Project parameters are uncertain, but we rely on experts to model the uncertainty, a geologist’s assessment of the chance of success, and a market analyst’s assessment of future prices are not that different in essence”. This fictional conversation could be a recurring theme in real investment meetings. Most often, energy companies are technological enterprises. Their managers are veteran engineers and analysts, with decades of experience in project management and decision making. However, project appraisals always have a leg on economic dynamics—factors that influence decisions but usually come from outside the managers’ domain of knowledge.

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3.6.2 Price Forecasts One of the oldest economic innovations is the idea of a commodity market. A medium where buyers and sellers of a commodity come together and exchange goods—based on their preference. Mike (the buyer) comes to the market to get food, raw material, or energy while Mia (the sellers) wants money for her products—A solution that helps everyone. With many Mikes and Mias buying and selling, the market reaches an equilibrium. Then the prices would be a fair reflection of the total supply and demand. In crude oil market, the balance is between global production and consumption. Mias produce millions of barrels of crude oil across the planet and Mikes buy them. Sometimes unexpected things temporarily upset the flow of crude oil between Mikes and Mias. Local imbalances and price jumps are common—e.g., the storage shortages in 2020 led to negative West Texas Intermediate (WTI) crude oil prices. Yet soon the overshadowing forces of global production and consumption will take over and prices would be fair—once again reflecting the balance of supply and demand. In the natural gas context, Mikes and Mias so far have only formed regional markets. This is mainly because they need a pipeline network to exchange (and that the Liquid Natural Gas, LNG, technology is still too expensive to make a global natural gas market). The result: prices in regional markets are fundamentally independent—e.g., we have different natural gas prices in Northwest Europe vs. North America. Still, within the market boundaries, the balance of production and consumption primarily shapes the markets and their price dynamics. This leads to an interesting proposition: if we know about the supply and demand and where they are heading in the future, then we will be able to predict the prices. If demand rises but supply does not, then prices will rise. If supply rises and demand does not, then prices will fall. As simple as it may seem, the devil (especially in this case) is in the details. The system is truly immense, we would not be able to understand (and model) its every aspect. In addition, new developments would always change the game—pandemics, financial crashes, technological breakthroughs, just to name a few. Still, good macroeconomic models that reflect key aspects of the supply and demand trends go a long way in generating useful price forecasts. Another aspect of commodity markets is their close ties with production and storage. Because producing commodities takes time, Mia (a seller) often prefers to have an assurance for sales before her product is ready. If Mia is a farmer, she would even prefer to receive the money upfront so that she could invest in land or fertilizers in the beginning of the season and sell the harvest at the end of the season. If Mia is an oil producer, she would again be happy to receive payments early on so that she could care for production machinery. Such a time mismatch between when the producer needs payment and when the commodity is ready creates a need for cross-time-deals. A deal that promises a specific price for delivering a commodity in a specific time would satisfy this need. This has led to the creation of derivative markets, where producers and consumers

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(and the traders) can exchange promises of purchase and delivery in the future. If there are many participants, we will have fair promises and prices. Such promises would also show a market-based forecast of prices. Overall, the commodity exchanges and their derivative market counterparts show useful clues for price forecasts. The information from these markets is useful but may not be sufficient. We may need experts’ judgement to further inform forecasts. Because forecasts are key to valuations and decisions, we would always need to use all available insights. Forecasting is not a pure science. It is an expression of available information. Because of our narrow view of the future, forecasting is always going to be a mix of science, skills, and economic intuition. With the common liberal interpretations of data, in practice we will always have differing forecasts and dissimilar valuations. But dissimilar valuations do not necessarily lead to wrong decisions. In an uncertain world, all deterministic forecasts will turn out to be wrong. They will never materialize. Instead of right or wrong, we better think of them as useful versus confusing forecasts. But useful in what sense? We earlier discussed that forecast should be consistent with our knowledge and understanding. Here we also discuss that forecasts should be context specific. They should be purpose-built. Forecasting for corporate decisions should be different from, e.g., forecasting for government policy making. Like pieces of puzzle that only fit together in a specific way, the elements of valuation (like discount rate, cost estimates and price forecasts) should also fit together. The price forecasts depend on their intended purpose. When forecasting, we cannot disregard other project assumptions like discount rate or cost estimates. Next, we discuss the role of commodity exchanges in forecasting prices—specifically for project appraisals. Through such exchanges, the traders use instruments to remove price risk and replace it with certainty. Like paying for insurance, the traders also pay to remove risk in a process called hedging. We will discuss that if traders could hedge price risk, so could the industrial firm. In a project that produces e.g., crude oil, we could learn from the process of hedging and understand project risk. This would lead to a better project appraisals and more informed decisions.

3.6.3 Hedging Instruments During the eighteenth and nineteenth centuries, potato farmers in Maine sold their produce in advance. They sold potatoes at the beginning of the season when planting the seeds. They would use the money to cultivate the land and deliver the potatoes at harvest. This farmers’ innovation gave rise to a contract for forward selling of their potato. By signing a forward contract, the seller promises delivery at a time in the future. The buyer would also know that the product will be ready by then. Such forward

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Fig. 3.10 Prices for forward contracts and the fitted risk-neutral curve

contracts are today prevalent in commodity trades. The contract specifies the price and delivery time. It also guarantees a set price. How do the forward contracts relate to a project’s cash flows? Forward contracts let a firm sell barrels of oil at a guaranteed price. They remove the risk of price change. The contracts show a roadmap not the complete picture. Yet, we can still “connect the dots” and gain some understanding about uncertain prices. As in Fig. 3.10, putting together the prices of crude oil forward contracts (as maturities increase) shows a trend. Forward prices for short-term delivery are closer to the spot price. For delivery dates further in the future, the forward prices tend to a long-term trend. The fitted line to the forward prices shows a forecast. It would be a certainequivalent price forecast—also referred to as a risk-neutral curve. It is a forecast of promised prices. If we could sell our production at prices of the curve, then we would have the certain-equivalent production revenue. By selling at these prices, we nearly remove the risk of price change. Most energy projects extend over decades. They produce beyond the longest maturity market contracts. For example, the longest forward contract for crude oil is currently for delivery in twelve years while an average petroleum development project takes decades to complete. The markets for metals and mineral have even shorter horizons. Does the certain-equivalent forecast still apply? We may extrapolate the “fitted curve forecasts” to horizons beyond the view of the markets. Yet, we should take our forecasts with a grain of salt. In general, no method is the ultimate solution to forecasting. The certain-equivalent forecasts are but one of the tools in our arsenal to generate valuation insight. In our valuations so far, the discount rate accounted for the time value of money and the systematic risk. To estimate such a risk-adjusted the discount rate we used the Capital Asset Pricing models. For any project, the higher the systematic risk, the higher the discount rate. Yet, this is not the only method of valuation. In an alternative approach, we can account for risk in the cash flows instead of in the discount rate (for example by using a certain-equivalent price forecast to estimate cash flows). If our adjustments

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to cash flows are fair, then it does not matter which approach we take. With consistent assessments, we would achieve identical results. Earlier we estimated the overall risk of projects using the Capital Asset Pricing Model. But what if we specifically studied the price risk? The cost of hedging is the cost of removing price risk from the cash flows. In other words, we could risk-adjust the cash flows for price risk using the insights from the forward contracts in the crude oil market.

3.6.4 Alternative to Project WACC: Certain-Equivalent Cash Flows If the certain-equivalent approach is much like the risk-adjusted discounting, then why bother? The answer is in the versatility of the certain-equivalent approach. As a project matures, the riskiness often changes. Using a single discount rate to stand for the total riskiness is possible but challenging. In the certain-equivalent approach we could individually assess the factors. It shows another view of assessing a project. For example, in a development project the cash flows in ten years would be riskier than those of the next year. Next year’s cash flow would be more about spending and constructing, but in ten years, we would have an uncertain production and would have to sell at uncertain prices. The risk-adjusted discount rate approach treats cash flows as if they are of the same riskiness. We hope that we estimate “ballpark” values. Yet still have no way of knowing about the biases that have crept into their valuations. In addition to the “ballpark” analysis, we could estimate the certain equivalent of the project cash flows. By discounting them with the risk-free rate (because they are not risky) we will have another view on project value. The comparison would be between a tailor-made versus a one-size-fits-all solution. Consider a simple and short-term project. Scallop oil company considers renovating and then selling an aging FPSO vessel for the expected price of USD 500 million. The renovation takes a year. The risk-free rate r f is 3%, the expected return on market portfolio rm is 8%, and Scallop estimates that the uncertain cash flow has the same risk as the market (leading to β = 1) for the FPSO renovation project. formula, the risk-adjusted discount rate for this project is r f +   Using CAPM β rm − r f = 8% and results in project’s present value of 500/(1 + 8%) = 463 million. Yet, as the news of renovation spreads, another company in need of an FPSO approaches Scallop and offers to pay USD 477 million for the vessel once renovation is complete. Their guaranteed payment is like a certain-equivalent, it entails no risk, therefore it is reasonable that they offer less than the expected price. Scallop calculates the present value of this offer by discounting this sure amount with the risk-free rate. The present value of this certain offer is 477/(1 + 3%) = 463 million—same as the earlier valuation. The two valuations are equivalent—whether Scallop accepts the offer or not.

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We see different views on the premium for taking risks. In risk-adjusted discounting, we have 8% − 3% = 5% risk premium embedded in the rate. In certain equivalent valuation, we have 500 − 477 = USD 23 million risk premium deducted from the future payment. The approaches are fundamentally the same and would lead to the same decision. They just differ in the way they account for the cost of taking risk. The principles of risk-neutral valuation similarly apply to larger and more complex projects. Instead of a single guaranteed payment, we could use the futures (or forward) markets. The insights from these markets ease the work. For example, we use the forward prices of crude oil futures in the New York Mercantile Exchange (NYMEX) to estimate the certain equivalent of risky cash flows. Traders commonly use market instruments to hedge price risk. If companies do the same, then their production revenue would be immune to the risk of price changes. Note that we do not say companies should follow such selling methods. Doing so may not always be the best. We just say if they did, then they would have achieved the guaranteed revenue. We pretend that companies use hedging to estimate certainequivalent cash flows. In other words, we use an analytic shorthand to simplify our analysis.

3.6.5 Market vs. Technical Risk In another shorthand, we divide project risk factors into two groups: the “market” risks that we can hedge, and the “technical” risks, that we cannot. For example, in a project we could protect ourselves from oil price downturns or currency drops by hedging. These would be market risk factors in our view. But we cannot hedge against the risk of dry hole or the risk of unexpectedly small discovery. These would be technical risks. The certain-equivalent valuation approach is all about assessing cash flows whenever hedging is possible. Therefore, those technical un-hedge-able risks enter our valuation intact. The investors will have no way to avoid the technical risks. To estimate present value of a series of cash flows, we discount the risk-adjusted cash flows with the risk-free rate. This is akin to the spirit of risk-adjusted discount rate. The key is in understanding and compensating for risk. In day-to-day life, people consider risk as any bad outcome. A manager that worries about the risk of a dry hole, may wrongly ask for increasing the discount rate to cover the added risk. Yet, markets (and the investors) do not compensate for the outcome of dry hole. Markets and subsurface uncertainty are independent. In CAPM language, getting a dry hole is a zero-beta event—an unsystematic risk and does not qualify for compensation because investors can diversify it away. In certain-equivalent language, we cannot hedge the risk of getting dry hole in the markets—a technical risk that we do not adjust cash flows for. Either way we do not offset against the risk of dry hole (or any other technical risk).

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3.7 Conclusions In this chapter, we discussed the use of the net present value as an economic measure of decisions. A positive net present value means that a course of action (like undertaking a project) would add to the firm’s value. A negative net present value destroys firm’s value. We discussed several ways of estimating the net present value. Yet in general, we should account for the time value of money and the uncertainty. To estimate the net present value of a safe project, we assess its cash flows and discount them with the risk-free rate—currently around 3%. But most projects are not safe. They have uncertain outcomes. The uncertainty leads to unwanted outcomes that we label as “risk”. To estimate the net present value of an uncertain project, in addition to the time value of money we should also account for its risks. The capital asset pricing models show a way of accounting for risk. We add a premium for riskiness to the risk-free rate and will have a project discount rate. The riskier a project, the higher its discount rate. Alternatively, we can adjust the project cash flows for risk. The certain-equivalent cash flows would be like assessing a twin project that is similar in every aspect to our project, except that it is safe. To assess the net present value of this twin project, we discount its equivalent safe cash flows with the risk-free rate. Both approaches should yield same results. Our valuation should be from the point of view of the investors. Because they are free to invest anywhere, they can diversify and remove the unsystematic risk. But they expect compensation once they expose to market risk. If this is the concern of the investors, so should be the concern of the firms that undertake the projects.

References 1. Taleb NN (2007) Black swans and the domains of statistics. Am Stat 61(3):198–200 2. Savage S (2002) The flaw of averages. Harv Bus Rev 80(11):20–21

Chapter 4

Applications

Abstract This chapter applies the key concepts from earlier chapters to real-world problems. Often these applications have subtleties that traditional models ignore. Therefore, we discuss how to reflect a further aspect of the real world in our decision models. The common theme in our discussions would be consistency of implementation and clear understanding. We use concepts of real options and dynamic decision making to enhance our models of project appraisal.

4.1 Introduction We undertake projects (or a specific course of action within a project) with the proviso that it has a positive net present value. Yet the real world is complex and uncertain. The decision makers do not just sit and watch the secrets unfold. They tend to actively manage their projects. As a result, using the net present value as a decision measure may seem oversimplistic. Our formulation so far ignores what the decision makers could do in response to the changing conditions. To improve our net present value estimate, we could include the effect of active management as “what-if” functions. Our models could further reflect the decision makers’ action. But this often creates a complicated situation. We need to show the evolving uncertainty—changes in belief by the arrival of information or changing conditions. We also need to model the acts of managers. To have these added features, we need to use more advanced mathematics. In this chapter we discuss the sophistication and subtleties of real investments. We discuss more advanced decision models. However, we still use the same theme in our discussions: decision models should be useful. Reflecting the real-world sophistications in our models should be useful. It should improve decision making and further create value. Otherwise, they would be a waste of time and effort.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Jafarizadeh, Economic Decision Analysis, SpringerBriefs in Petroleum Geoscience & Engineering, https://doi.org/10.1007/978-3-030-96137-4_4

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4.1.1 Non-linearity from Capacity Constraints It may strike as odd, but blockbuster hydrocarbon discoveries have repeatedly led to poor value estimates. For example, a large discovery in West Shetlands back in 2017 met with enthusiasm at first but led to not-so-much appealing value later. Often there is a non-linear relationship between “volume” and “value”. Scallop Oil considers drilling an oil prospect in a remote region. The wildcat well costs USD 10 million and would have 33% chance of success. Because of its distant location, the management has concerns about the commerciality of a potential discovery. In the past, there had been discoveries in this region that led to successful developments “but legacy of when oil prices were soaring”, as the commercial advisor put it. Recently, the crude oil prices dropped. The expectation of future prices is bleak. The engineers in Scallop devised a development plan for the expected discovery. They would use a subsea wellhead and manifold along with an FPSO vessel for production, storage, and offloading. As in Fig. 4.1, this development project would be a moderate financial success. Using the decision model in Fig. 4.2, the analysts in Scallop concluded that the expected value of drilling, assuming it could lead to the expected (average) discovery, is slightly positive. Then, based on the current view, drilling the wildcat is a “go” decision. However, they also raised concerns that changes in assumptions could affect

Fig. 4.1 Cash flow profile for average discovery

Fig. 4.2 Decision tree model when we consider “average” discovery

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Fig. 4.3 Probability distribution of volume given discovery and three outcomes selected for further analysis

the net present value of the average discovery and easily tip the scale against the “go” decision. On the other hand, Scallop’s chief geologist pointed out the potentials for a large discovery. “We could be having an elephant discovery for all we know”, showing Fig. 4.3 in a heated debate with the analysis team. With the potential to make a large discovery, drilling the wildcat well seems to the geologist as more than justified. To show these further potentials, the engineers drew up multiple plans for development. They selected three scenarios for further scrutiny: a marginal discovery showing the tenth percentile, a mid-size discovery for the fiftieth percentile, and a large discovery for the ninetieth percentile of volume distribution. It turned out that (because of the technical constraints of this project) the development solution in all scenarios would still be using subsea facilities and FPSO. The capital costs in all three scenarios would be the same. The engineers pointed out that the development scheme has a limited production throughput. It constrains the maximum production, leading to a “plateau” in the early years. For a large discovery, such a constraint would mean longer production years—a drawback, as distributing production over longer periods means less value. As Fig. 4.4 shows, a large discovery has more hydrocarbons to produce and to sell. Yet, spreading the cash flows over longer periods would hurt the economy of the project. The decision tree in Fig. 4.5 shows the non-linear relationship between size and value. Earlier analysis based on an average discovery led to the recommendation “drill”—the expected value was slightly positive. But a more detailed analysis led to the added understanding about production constraints. With three discovery scenarios and the improved understanding about constraints, the expected value of drilling is slightly negative. This leads to the recommendation “walk away”. Which course of action should Scallop take? Our analysis merely shows the importance of clear thinking. The model recommends a course of action based on our understanding. It shows the result pivots on our understanding.

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Fig. 4.4 From the top, cash flow profiles for marginal, mid-size, and large discoveries. In each scenario the production cessation date is when the revenue declines to a level barely able to cover the expenses. We assumed no abandonment cost and used 7% discount rate

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Fig. 4.5 Decision tree model for drilling the wildcat well assuming the discovery could be marginal, mid-size, or large. The analysts used Swanson’s discretization method to select representative scenarios and assign probabilities to these outcomes

For example, the engineers could consider a purpose-built stand-alone platform as an alternative to the FPSO development. It would be more expensive but would also have higher throughput. It could also lead to future expansion opportunities. In conclusion, non-linearities are prevalent in the real world. Ignoring their effects could lead to poor decisions and loss of value. Managers often actively manage their projects—either to mitigate losses or to capitalize on the upsides. For example, drilling an appraisal well after discovering hydrocarbons is a form of management. By drilling an appraisal well, the manager would gain more information about the extent and the properties of the discovery. This would be a major help before committing to an expensive development decision. The ability to use the managerial flexibilities and react to the resolving uncertain conditions are the “real options” of the business. Their value could be substantial— the effect of active management versus doing nothing. Decision makers often know of the value of flexibilities. But could our decision models also reflect this? We discuss real option valuation models to enhance our decision models.

4.1.2 Real Options (Value of Flexibility) Some projects are inherently flexible. Only an incompetent manager ignores their flexibility. In some other project, managers could buy flexibility at the outset to tackle the uncertain conditions later. For their appraisal, our valuation models should answer one question: do the potentials outweigh the added upfront costs? A flexible plan is naturally more expensive. The added flexibilities make it costly, lengthy, or effortful. The potentials, on the other hand, are uncertain. They may not even materialize. The answer to the earlier question then rests on a consistent comparison between upfront costs and the expected future benefits. Assume Scallop oil wants to develop an offshore oil discovery. The engineers originally conceived a development plan to accommodate the best drainage strategy. But then geological studies pointed out another oil prospect nearby. With a potential expansion in mind, Scallop’s engineers have now devised three development solutions:

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• Proceed with the original development plan. If future drilling in the nearby oil prospect turns out to be a discovery, then they could “tie it back” to the existing facilities. However, changing to the built facilities will be very costly. The production from the new discovery will have to wait. It could start only after the main field’s production drops to a level admitting the added flow. The engineers suspect that such deferred production could have a negative impact on project economy. • Design a development scheme with enough slack capacity so that it could accommodate production from the potential discovery anytime. Such a design would be naturally more expensive but will not defer any production. It would, however, be a colossal waste of money and resources if exploration in the nearby prospect is unsuccessful. • Delay the development until they know of the outcome of the nearby drilling. By then, the engineers will be able to design the best solution for the discovered resources. This would mean that they defer positive cash flows and would sustain losses—depending on how long the delay is. The proponents of the delay option argue that the forecast for hydrocarbon price is a decisive factor. In general, price forecasts are influential in any investment decision. Here, if price expectation increases, then the delay option is much more valuable than the case that price expectation decreases. • Incorporate flexibility into the design so that when (and if) the nearby field comes online, they could expand the scope of their operation. It would be costly to dial in the flexibility into the design—yet still not as costly as constructing a large facility. If the nearby exploration is unsuccessful, Scallop will not lose much. All they could lose is the added cost for a flexible design. Comparing the value of these courses of action is challenging. Their values depend on a myriad of factors—including price forecasts, the incremental costs, and the chance of success in exploration. If it was certain that drilling leads to success, or if Scallop had a guarantee for prices, then the comparison would be easy. The uncertain factors make the comparisons challenging. Scallops’ decision would depend on the value of the nearby discovery. A highly valuable discovery is worth adjusting current plans for, while a less valuable project is easier to let go. As Fig. 4.6 shows, the best course of action depends on the expected value of the courses of action. Assume the chance of success in the nearby prospect is 45%, it costs USD 15 million to drill, and that Scallop uses 7% discount rate for this investment decision. The accompanying spreadsheet has more detailed information about the rest of the parameters, including the cost of development in each scenario, the operating costs, and price forecast. To keep the discussions brief, in Figs. 4.7, 4.8, 4.9 and 4.10 we show the cash flow profiles for each course of action. As earlier discussed, changing the future price expectation (price forecast) sways the relative appeal of courses of action. Forecasts especially affect the economics of the “delay” scenario. In this example, we assume an upward sloping price forecast.

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Fig. 4.6 Decision tree showing the courses of action and uncertainty about the outcome of nearby exploration

The decision makers in Scallop expect the prices to increase in the future. This leads to the best course of action as in Fig. 4.11. On the contrary, if we had a downward sloping price forecast, then the economics of the projects, and the best course of action, would have been different. This shows how decisive the price forecasts are in this project. In general, we recommend analysis of the effect of prices on project value. This could be through e.g., sensitivity analysis of project values with respect to changing prices. This example reveals an important relationship between flexibility (an upfront cost) and value creation from managerial decisions. Because of the ever-changing conditions, our valuations would always depend on the expectation of dynamics— such as price forecasts.

4.2 Prices Dynamics The future price of any energy commodity is uncertain. The uncertain prices lead to uncertain project economics. Could we predict prices? In theory, yes. The prices

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Fig. 4.7 Cash flow profiles showing the outcomes for the course of action “proceed with original development”

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Fig. 4.8 Cash flow profiles showing the outcomes for the course of action “build flexibility into the design”

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Fig. 4.9 Cash flow profiles showing the outcomes for the course of action “delay development project”

reflect the balance point of supply and demand. Once we know of the future of supply and demand, we would know about the prices. In practice, the complex energy economy makes price predictions impossible. We often think of prices as unpredictable and fluctuating. In fact, Early attempts in modeling commodity prices also followed these thoughts. They used a random walk process to describe the dynamics of the prices. Comparable to a drunk person that tries to walk on a straight line but inadvertently steps to the left or right, the prices would also go up or down. Such random walk models successfully describe the price moves of financial assets (like stocks). But commodity prices (like crude oil and natural gas prices) are not like financial assets. Humans physically produce and consume the commodities. The prices should then more closely reflect the supply and demand. Statistical Studies

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Fig. 4.10 Cash flow profiles showing the outcomes for the course of action “build a large platform”

of commodity prices confirms randomness. These studies also show mean reversion [1].

4.2.1 Randomly Walking or Mean-Reverting Prices We describe the behavior of uncertain prices as stochastic process. Assume the spot price St at time t follows a random walk process. We define the logarithm of spot price as lnSt = ξt where ξt follows a simple Brownian motion dξt = μdt + σ dz

(4.1)

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Fig. 4.11 Decision tree model of the investment project showing “delay” as the best course of action

In this equation,μ is the trend and σ is the standard deviation (volatility) of the Brownian process. We show the increment of the standard Brownian motion process as dz. To visualize this process, assume the drunk is walking randomly in a general direction defined by the trend μ. They randomly step to the right or to left of the general direction—σ shows the average size of these random missteps. A random walk process is not the best description of commodity price behavior. When prices are high, we expect existing projects to produce more, and higher cost producers to come online. This results in more products in the market pushing the prices down towards an equilibrium. On the other hand, when prices are low the existing production ceases faster and earlier, expensive producers go offline, and there will be less oil in the market. This pushes the price up towards the equilibrium. Such pattern of price behavior is in line with the “mean-reverting price” assumption. We expect to see random deviation from the mean that tend to dissipate and disappear—revert towards the average. Mean-reversion is a common feature in most commodity prices, including in crude oil and natural gas. We define mean-reverting prices by assuming the spot price consists of a constant “average” element and a short-term random deviation. The deviations fluctuate

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randomly around zero, sometimes above zero, describing spot prices larger than average, and sometimes below zero, for below average prices. In mathematical terms, lnSt = χt + ξ where ξ is the average price (constant), and the deviations,χt , follow the stochastic process dχt = −κχt dt + σ dz

(4.2)

In this equation, σ is the standard deviation (volatility), dz is the increment of the standard Brownian motion process, and κ is the coefficient of mean-reversion. A larger coefficient means that deviations fade away faster—the term ln(2)/κ is the “half-life” of deviations. To visualize the process, the drunk now follows a rope that connects the bar and home. Once they deviate from the path, the rope pulls and corrects the direction. Fig. 4.12 describes the uncertainty in crude oil prices using a geometric Brownian motion and a mean-reverting process. We have simulated many price paths in a spreadsheet and have calculated the confidence intervals for future prices. The confidence bands show distinct profiles of price uncertainty. Depending on the model we use, we would have distinct investment decision insights. Which model should we use? There are arguments for and against each model. Mean-reversion is

Fig. 4.12 Describing price behavior using a geometric Brownian motion and a mean-reverting process

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a common aspect of commodity prices because of their production-consumption relationship. Prices also have a random walk aspect. The factors that randomly affect supply and demand (like changes in technology) follow a geometric Brownian motion process. Furthermore, statistical studies of historical prices reveal both mean-reversion and random permanent changes.1 The markets reveal yet another clue about the behavior of prices, especially, the prices for forward and futures. These are contracts for delivery of crude oil or natural gas in a specific date in the future. They show the markets’ expectation about prices in the future. If these expectations agree with the expectations from our model, then we would know that our model is consistent with the market. Studies show that the prices of forward and futures better agree with the mean-reverting model. In general, the future is not like the past. The future is uncertain—more scenarios could have happened than what we have experienced. The past is but one realization of many possibilities. Therefore, in addition to studying the past, we should also see whether our perceived behavior of prices agrees with the general belief about the commodity markets. The random walk assumption is at odds with the general understanding about commodity markets.2 Once we think of prices as stochastic variables, we can set up a frame for further thoughts and acts. Still our price model depends on the context—the decision frame. Like pieces of puzzle that should match to collectively form a picture, the price model and decision context should also match.

4.2.2 Dynamics of Price Forecasts For most energy investments, the prices now—spot prices—are irrelevant. Often it takes years for the projects to come to fruition. By then, prices would be entirely different. To estimate project values, we need price forecasts. Still, price forecasts should be project specific. Why? To invest in a short-term project (like shale oil) we would be more interested in the forecast of the near future— We could capitalize on short term price jumps. But to invest in a long-term project (like a large offshore project) short-term price moves do not matter. We would be interested in the long-term forecasts. Assume Scallop oil company wants to drill an oil prospect. The spot prices at the time of drilling hardly matter. It would take years for the hoped-for discovery to produce hydrocarbons and the prices at that time have no relationship with prices at 1

We believe that these processes reflect distinct aspects of price behavior and they both apply. Prices manifest both mean-reverting and random walk behaviors. This idea has led to the development of multi-factor processes (e.g., the two-factor process in [2]) that better represent price dynamics. Here, we confine our discussions to simpler single-factor models. 2 Similarly, we make subsurface models based on limited information. The information shows a blurry profile of the subsurface. We further need to rely on general geological principles (e.g., deeper depositions are older, lower density liquids migrate upwards). The models should then be consistent with both information and such general beliefs.

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the time of drilling. Yet, when prices drop, oil and gas activities diminish and when prices soar, exploration accelerates. Are the drillers (including Scallop) irrational? or do they see beyond spot prices? They see a trend in prices. If oil and gas prices plummet, the drillers think “what if this trend continues and make my business miserable?” Or, when the prices soar, they think “world is ripe for exploration, as prices seem to stay favorable”. In other words, so long as changes in spot prices inform about changes in price trend—price expectation—they would be relevant to the decision makers. Our price forecasts would depend on either we believe in a random walk, meanreverting, or any other behavior of spot prices. Nevertheless, the spot prices hint at a doomed or a bright future. It is a murky picture, but as spot price changes, so do the forecasts. In our mathematical models, spot price is the “initial condition”. A change in this initial condition leads to a change in the expectation.

4.2.3 Price Forecasts in Mean-Reverting Framework In commodity markets, prices of forward contracts are sort of price forecasts. The market participants collectively believe the spot prices at time of delivery would be close to the prices of forward contracts. We can also show this in a mean-reverting process. The price of a forward contract on a barrel of oil at time t for delivery at T , denoted by Ft,T , is σ2  ln Ft,T = e−κ(T −t) χt + ξ + 1 − e−2κ(T −t) 4κ

(4.3)

For t = 0 (the present time) and T ≥ 0 (deliveries in the future), the equation shows a curve —a series of forward prices for various delivery times. They form a price forecast. Depending on the parameters, the curve could be upward or downward sloping. The solid curve in Fig. 4.13 shows a downward sloping price forecast. Here, we expect the short-term positive deviation to wear off and that prices to approach the long-term equilibrium level. How about the dynamics of forecasts later in time? For example, what would be the “high” or “low” forecasts if we undertake a project next year instead of now? We can still use the equation and generate price forecasts for t > 0. Because there could be countless initial conditions at t > 0, we can use the binomial lattice representation of a mean-reverting process [3]. In this representation, during a period t, the variable χ could only move up, to χt+ with probability pu , or down, to χt− with probability pd = 1 − pu , √ tσ

(4.4)

√ χt− = χt − tσ

(4.5)

χt+ = χt +

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Fig. 4.13 Dynamics of mean-reverting price forecasts in a binomial lattice

   √   pu =max 0, min 1,1/2− t κχt +1/2σ 2 /2σ

(4.6)

The probabilities for up and down move change with periods. They also depend on χt , the deviation from the mean. Note that the market prices could take any value while in the binomial lattice we only think of price ticks of “up” and “down”. This is not problematic per se. The binomial lattice is an abstraction of the real world. It approximates price movements through simple moves. With short intervals and many time steps, the binomial portrayal of prices approaches a continuous representation. Often, we do not need to depict the binomial price ticks for the entire duration of a project. With long project durations, it would make an unnecessary large binomial tree. We only need to depict binomial moves for the decision epoch. For example, to save on computation time, for a project that starts at t = 0 and the decision window only opens later at for t = 4 or t = 5, we show a binomial lattice as in Fig. 4.13. At the outset of the binomial lattice t = τ , χτ is a function of time τ and its initial value is χ0 . We can calculate the value of χτ using the fact that any changes in lnF0,τ is due to changes in short-run deviations [4] χτ − χ0 = ln F0,τ − ln F0,0 Note that the mean level ξ , is not time dependent. We conclude that  σ2 − χ 0 − ξ + χ0 χτ = e−κτ χ0 + ξ + 1 − e−2κτ 4κ σ2  χτ = e−κτ χ0 + 1 − e−2κτ 4κ

(4.7)

In summary, Fig. 41 shows that the spot price of 70 USD/barrel and the meanreverting parameters lead to the primary price forecast—the downward sloping solid

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line. A few periods in the future, the forecast changes. With two periods to depict the binomial dynamics, we show five price forecasts. Positive or negative deviation from the mean respectively lead to downward or upward sloping forecasts. This book is not a primer on price forecasting. We only discuss forecasts (and their dynamics) as they relate to project appraisals. In our discussions, the stochastic process is the primary idea. All forecasts originate from the stochastic process that we use to describe prices. It is crucial that the stochastic process stand for the underlying phenomenon. The mean-reverting process is consistent with market characteristics. Still, we should adjust the process to the available information. What should be our estimate for the volatility, speed of mean-reversion, or the starting point of the process? We need a systematic method of parameter estimation.

4.2.4 Parameter Estimation There are several ways to calibrate a stochastic process. The traditional approach is the use historical prices and estimate the process parameters so that it conforms to the past. Once the process agrees with the past, some researchers claim, it should be good in describing the future. The past is but one realization of a series of values that the uncertain variable has taken. There could be many other realizations, but our world view confines to only one experience. This is a natural limitation. An alternative approach to parameter calibration uses the collective ideas of market participants. It assumes the markets are efficient (prices reflect the state of information) and made up of rational participants. Then the participants’ collective expectation would be a useful forecast.3 Like any other parameter calibration method, the implied approach based on market information also has its limitations. First, the data from market instruments (derivative contracts like option and futures) reflect a risk-free outlook of the prices. They do not hint at estimation of risk premiums. In this regard, the implied approach is no worse than the alternative approach of using historical prices. Second, the market data is also noisy. It has patterns that the mean-reverting process could not explain or accommodate.

4.3 Real Options and Price Dynamics Besides their technical flexibilities, managers can also react to the changing economic conditions. For example, the managers ask: what if we undertake the project but the forecasts changed two years from now? By then, if the economic outlook were 3

A general discussion of parameter estimation is available in: [5].

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promising, they could expand the scale of the project. If, on the other hand the conditions were bleak, they could shrink the scope. The acts of the managers affect project value. But do we also show these effects in our models? We can show only the key aspects. Our “what-if” models show an abstraction of what could really happen.

4.3.1 The Option to Wait In most projects, we have flexible timing. We can wait and delay the start. What could change if we wait? The geology is constant. It has been unvarying for millions of years. Changes could be only in our understanding and in the commodity prices. We wait to learn more about the geology, or to get a better economic outlook. Scallop oil company holds a drilling license that expires in three years. Having only one opportunity in the license—a hydrocarbon prospect with 30% chance of success and slightly negative economics—Scallop considers letting it go. But believers in the license would like to wait. They argue that the marginal economics of drilling could easily turn positive with higher price forecasts. “In a dynamic economy”, the believers say, “The outlook of hydrocarbon prices is promising, let us wait for favorable times.” In a mean-reverting process with parameters shown in Fig. 4.14, we can think of binomial price moves leading to five different price forecasts (the dashed lines). The proponents of waiting say: “If any of the favorable price outlooks materializes, then we are in for a good project. If not, we will then let go of the license”. Once Scallop finds hydrocarbon, it would take years to develop the field and to produce hydrocarbons. Their engineers estimate two years of tie-back development

Fig. 4.14 Binomial lattice showing the risk-neutral price forecasts for a mean-reverting process

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and eight years of production—in total, a ten-year project. Note that the proponents of delay merely highlight the fact that the economics of project would be different if we use any of the different price forecasts from Fig. 4.14. The value of the development project depends on its start date and the price forecast that applies. In the binomial framework, Fig. 4.15 shows the value dynamics for the development project. The decision to drill is inherently a waiting option. The license expires in three years. Therefore, Scallop could drill now, next year, or two years from now—assuming the organizational restrictions only allows annual decision making. Often the waiting option is not free of charge. In this case, Scallop should pay area fee for holding on to the license. In other cases, the area fee may be a sunk cost as sometimes companies pay fees for the duration of the license upfront. Still, to compare with the status quo (the “as is” alternative) Scallop needs to know the value of waiting. The decision tree in Fig. 4.16 shows the decisions, uncertain factors, and the value of best course of action. It would be best to wait and drill the wildcat next year if conditions improve. Otherwise, they should quit. Note that drilling now has a negative expected value, it is not a value-adding course of action. Waiting and capitalizing on a price spike leads to a positive expected value. Our analysis used a simple mean-reverting process in a binomial lattice to describe the price dynamics. We then used a decision tree to describe the decisions and uncertainties. In many ways, we have simplified our model. For example, we assumed the decisions have an annual frequency, while in practice firms may be able to mobilize their resources faster and decide in a shorter period. Adding further features to our model would make it more detailed. A way to tackle a complex model would be to use other analytical tools like dynamic programming and simulation models.4 Naturally, we always need to balance the benefits of a simple model that is effective and useful with those of a detailed and more realistic model.

4.3.2 Sequential Exploration and Uncertain Prices From its early days, the right to explore has been for a specific area—first inland and later for offshore tracts. Companies get to explore hydrocarbons in a tract, during a set period, and for a fee. At the end, they may discover resources or return the tract to the original owner empty handed. The decision to buy the exploration rights always ties to the question: What is the value of the tract? The answer depends on what we expect to find. Yet, for many tracts, the value is not necessarily the sum of the values of the prospects. Companies usually find multiple prospects within a tract—a portfolio of opportunities. The traditional approach to valuation of tracts has been to assess the value of each opportunity and then sum them up. This works fine if the opportunities are 4

For example, the simulation method in: [6].

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Fig. 4.15 Cash flow profiles of the development project for the binomial price forecast

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Fig. 4.16 Decision tree model of the waiting option showing the best course of action in bold arrows

independent or have insignificant inter-relationships. However, in most tracts this does not work. Nearby opportunities are often interdependent. The interdependence could be from several aspects. prospects may be geologically or operationally correlated. Drilling a prospect may give information about neighboring opportunities. Multiple development projects could join and make a whole with better economics. With these interrelationships we should ask: “Where should we drill first and what to do next?” so that the strategy yields the best value. Assume Scallop Oil is exploring an offshore tract with two prospects α and β. The chance of success is 30% for α and 22% for β. It would take a year to drill

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Fig. 4.17 Cash flow profiles for developing the discoveries in α or β

wildcat wells into these prospects and costs (respectively) twelve and fifteen million dollars. If successful, the discoveries would lead to development projects with cash flow profiles shown in Fig. 4.17. If we consider the wells independent, then their economics are not favorable. The expected value of drilling prospect α is 30% × 34.4 + 70% × (−12) = −1.7 and for drilling prospect β is 22% × 35.3 + 78% × (−15) = −7.2. With negative expected values, Scallop should walk away from this tract. However, the prospects are inter-dependent. The chief geologist in Scallop says they share common geological features and information from drilling one prospect would affect their assessment of chance of success in the other. These correlations lead to the conditional probabilities for success: P(β success|α success )=50% P(β success|α failure )=10% P(α success|β success )= 68% P(α success|β failure )= 19%. In addition, a twin development project (for twin discoveries) could use common features and save costs. It is less likely to have twin discoveries, but once they are there, the value potentials are significant. Instead of waiting to have twin discoveries, the engineers have devised a flexible plan. They start laying the foundation common features once they know of the first discovery. They can then expand for twin discoveries or shrink the scales to accommodate a single discovery. Scallop’s economist also mentions that there is value in waiting. The outlook of hydrocarbon prices is promising. “By drilling in sequence” the economist says, “we not only learn from geology, but also could capitalize on higher prices. The price forecast may go south, but then we would not be much worse off from where we are today”. To spell out the economist’s view, in Fig. 4.18 we show the dynamics of price forecasts. Scallop believes that prices follow a mean-reverting stochastic process. Based on the parameters (shown in the figure), the price outlook could go “up” or “down” in a year when Scallop wants to drill the second well.

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Fig. 4.18 The dynamics of price forecast in the mean-reverting context

Any single or twin discovery would lead to a distinct cash flow profile. We consider two possibilities for each development project: a high- and a low-price forecast. As we show in Fig. 4.19, this leads to six cash flow profiles. To assess the net present value of these projects, we used the certain-equivalent approach. The dynamics of price forecasts are risk adjusted, leading to risk-adjusted

Fig. 4.19 Cash flow profiles for the development of single or twin discoveries with high- or lowprice forecasts

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estimates for production revenue. The rest of the cash flow elements are technical assessments and risk adjusted. This leads to certain-equivalent cash flows that we discount with 4% risk-free rate. It is a simplified view. But it is consistent and useful in our analysis. Finally, we can show our view of the drilling strategy in the decision tree model of Fig. 4.20. We show the value effects of (1) geological correlations and learning from drilling. (2) the economy of scale in developing twin discoveries. (3) the option to wait (for a year) and capitalize on positive changes in price forecasts. While our earlier myopic analysis led to poor value estimates, this decision tree model shows a different valuation. The two-prospect opportunity (with added value effects) is economically attractive. We gain positive expected value by following the strategy of: “drilling α first and drilling β next only if α has been success and price forecasts have gone up”. We still show a simplified (and useful) view of the real project. But nevertheless, the decision tree could grow unmanageably large for three or more inter–dependent prospects. Studies have used dynamic programming and approximate methods to mitigate this curse of dimensionality and make useful models.5 Again, we compare simplicity versus sophistication. All models should aim at creating useful decision insights. Note that the decision tree model is not an operational guideline. It does not show how managers should manage their investments. It is a model for project valuation. For example, here it shows that a project consisting of two opportunities would have a positive expected net present value because the decision makers would actively manage the investment. With a positive expected value, the tract is economically attractive.

4.4 Environmental Considerations Environment is the silent, and often neglected, stakeholder in energy ventures. It has become clear only in recent decades that misusing and misallocating fossil resources, while bringing utility in the short run, could eventually leave us with a desolate economy. Examples of environmental neglect are abundant. Oil spills, contaminations, and emissions are usually the result of errors and neglect—often occurring because of lack of foresight. As we better understand our global economic ecosystem, we better realize the importance of the environment as a major stakeholder in energy projects. Tighter environmental measures mean higher immediate costs. For example, carbon tax—or emission allowances—and double-hull oil tankers increase the immediate costs and eventually deteriorate projects’ value. However, the policy makers 5

Sequential exploration of geologically correlated prospects: [7]. Adding dynamics of prices: [8]. Using approximate dynamic programming for larger problems: [9].

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Fig. 4.20 Decision tree model showing the sequential drilling strategy

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believe that such measures decrease the likelihood of future harm and are therefore, investments for the future. Policy makers devise these added measures to eventually avert costly environmental calamities. In this context, the sacrifices are worthwhile. The rationale is to pay a reasonable upfront price to avoid sky-high costs in the future. It is an economic decision on a planetary—and inter-generational—scale. For energy projects, paying the added environmental premiums leads to less worth. The decline in value depends on the rate (and the design) of these premiums. Ideally, the environmental taxes should penalize the most polluting projects while encouraging cleaner initiatives. For example, with the introduction of carbon tax, we expect the coal-fired power stations to gradually phase out. Similarly, the carbon intensive Steam-Assisted Gravity Drainage (SAGD) technology used in the heavy oil sand projects should also lose its appeal. On the contrary, cleaner projects that were expensive before the introduction of emission taxes should now become comparably more favorable—this is tax system encouraging a specific pattern of investment.

4.4.1 Emission Taxes and Project Feasibility Because of limited exploration budget, Scallop oil company considers drilling either prospect ψ or prospect ω. These opportunities are in almost identical. The chance of success in either prospect is comparable (20% for ψ and 22% for ω). The engineers estimate the cost of drilling for each prospect would be USD 10 million. If they make a discovery, the development projects in each case would be similar. It is likely that the host government would introduce a tax of USD 150 per tonne carbon dioxide to reduce the emissions from exploration and development projects. Since prospects ψ and ω have similar hydrocarbon content and planned development scheme, at least under the current provisions such a carbon tax would equally affect their economics. Under the current standard development provisions, both projects would emit on average thirty-five kilogram of carbon dioxide per barrel of produced oil—a number considered high by industry experts. The emission tax each project would pay decreases their net present value by tens of millions of dollars. Fig. 4.21 shows these expected cash flow profiles for standard development projects. Amid the growing environmental concerns and the loss of value due to emission tax, the analysts in Scallop consider alternative development scenarios. They devise a “green” development solution besides the standard scheme. Such a green solution would have added features that makes it costly. It involves a different approach to platform electrification. By removing the carbon-emitting gas turbines and instead, supplying electricity through subsea cables, the engineers expect the emissions to reduce to only five kilogram of carbon dioxide per barrel. The added upfront cost however makes a more expensive development. The company faces a dilemma. Should they pay upfront and pay less taxes later, or should they go with standard development and pay higher emission taxes later? The answer depends on the incremental present value of these courses of action.

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Fig. 4.21 Cash flow profiles for development projects following success in drilling prospect ψ or ω

This is also where the two prospects differ. Prospect ψ is closer to existing infrastructure and if they make a discovery there, the incremental cost of subsea cable compared to a discovery in ω would be lower. The prospects are similar in most aspects, yet the green development solution would be much less costly for a discovery in prospect ψ than in prospect ω. Figure 4.22 shows the cash flow profiles for the green development projects. Proponents of these green solutions point out the benefits of reducing the net carbon dioxide emissions against its costs. They say: “Yes, the subsea cable is expensive, but it brings electricity from a clean source and cuts the emissions from the otherwise carbon emitting gas turbines in the standard development scheme”. However, in the absence of carbon tax, as Fig. 4.23 shows, such arguments do not effectively change the decisions. Scallop answers to the shareholders. They prefer to drill prospect ω because of its higher expected value.

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Fig. 4.22 Cash flow profiles for green development projects, assuming there are no emission taxes

Even though prospect ψ is closer to the existing installations and the cost of electrification through a subsea cable is lower, the host government’s fiscal policy does not currently encourage such a plan. However, if the carbon tax of USD 150 per tonne of carbon dioxide takes effect, then the proximity to the existing installations could give an edge to prospect ψ, as in Fig. 4.24. The savings on emission tax better justifies the added cost of electrification through a subsea cable. With the introduction of emission tax, the value creation goals will automatically lead Scallop to select a course of action that is environmentally cleaner, as in Fig. 4.25. By drilling prospect ψ, we still have a positive expected value and in addition would honor the environmental concerns. This is an example of emission tax shaping the investment decision pattern towards more environmentally conscious choices. The authorities implement measures to encourage a general pattern of investments, but the details are up to the ingenuity of the investors. They detect or design value-adding opportunities within the constraints and put them to their advantage. In

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Fig. 4.23 In the absence of environmental taxes, the decision tree model shows drilling ω as the best course of action

our earlier example, the emission tax affected the relative feasibility of the development solutions and led to the design and implementation of the green electrification solution. Without the emission tax, engineers would never set about designing a low emission solution on their own. Such is the fate of other environmentally friendly projects. They all depend on their value-adding property within the new set of constraints. For example, the discussions about a central power hub that electrifies nearby facilities and draws on the economy of scale, windmills that electrify platforms and reduce dependence on gas-burning generators, or even the concept of electricity-from-shore, they all draw on their value-creation potentials. Without the carrots-and-sticks of the regulatory system, such ideas would not show promise and would never take root in industry.

4.4.2 Carbon Accounting and the Single Objective of a Firm Most environmental models show that the average global temperatures are increasing because of the excessive release of greenhouse gases (including carbon dioxide) in the atmosphere. Their narrative is that we humans, all contribute to the slow cooking of our planet. Who is to blame? They answer it is the fault of the carbon emitters from producers and consumers. In fact, the excessive emissions are just a symptom of a disease—humanity’s excessive consumption. By management of emissions, we have been treating the symptoms, while the real culprit has been the needlessly abusive lifestyle. With such a description, the policy makers should broaden their horizon. Instead of direct emissions, they should consider net incremental emissions—the total effect

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Fig. 4.24 Cash flow profiles for green development projects assuming the project pays emission taxes

of undertaking a project from production to the indirect effect of its technology on operations and the acts of the society. However, this introduces new preferences into the process of decision making so that we may need to rethink and redefine the economics of a project and a corporation. We believe finance theory and the single objective of a firm—shareholder value creation—is still applicable in this context. We do not need to redefine the economics; we just need to design effective environmental constraints. Value creation is the goal, but to maximize shareholders’ value, the firm needs to first satisfy other stakeholders—e.g., the creditors, the government, and environmental regulators. They all stand in a line ahead of the shareholders. The firm should meet their expectations first, before tending the shareholders. Shareholders’ value creation still applies. We believe saving the environment is an all-encompassing effort. We need to develop technologies and make systematic changes to business, society, and the economy. We still do not have the ultimate solution, but we have taken the promising

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Fig. 4.25 With the introduction of emission tax, the decision tree model shows that the best course of action is to drill ψ prospect

first steps. As the societies gradually gain a collective understanding about key issues, they vote for policies that are more effective in preserving the long-term wellbeing of all.

4.4.3 Market Mechanism for Emission Control Instead of a command-and-control mechanism that punishes emissions, progressive societies have favored market mechanisms that implicitly rewards environmentallyfriendly solutions—preferring incentives over deterrents. From the 1970s, pioneering emission markets in the United States effectively managed the level of Sulphur dioxide and Nitrogen oxides emissions under their acid rain program. Later, these learnings inspired the development of the European Union’s Emission Trading Scheme (EU-ETS)—the first market scheme to control Carbon dioxide emissions. Within these cap-and-trade markets, regulators distribute trade-able emission allowances to corporations. These allowances are the “cap”, they show the maximum allowed emission for the owner. If their emissions exceed the cap, then they should pay excessive penalties. To avoid penalties, they could visit the emission markets and buy any unused allowances from other sellers at the market price. As the authorities gradually reduce the total emission allowances over time, they implicitly encourage technological innovations and meanwhile they reduce total emissions. This setup thus encourages smart solutions that save on emissions and makes money by selling any unused allowances to those in need—innovative emission-reduction measures that create value.

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For energy companies, such a cap-and-trade market means that their emission allowance is a tradeable security. Like commodities in efficient markets, it has a fair market price. In a comparable way that trends of hydrocarbon prices generate insight about the outlook of projects, emission price fluctuations and their market trends could also generate general insights about the economic outlook of emission-saving projects. For example, in an upward trending price scenario for emission allowances, environmental solutions with long-term payoffs would have economic appeal. Here, the collective market participants hint on a future that rewards cleaner solution, even though the low current prices do not say so.6 Richard Feynman once said: “For a successful technology, reality must take precedence over public relations, for nature cannot be fooled”.7 This is true for any technology, and especially for the politically heated debates of the environment-saving technologies. We believe the emission markets as a social technology, and the technical emission-reducing technologies, all need to work hand in hand and according to the science-informed policies. In this context, the economic decision analysis would be a useful framework promoting long-term and sustainable value creation.

4.5 Conclusions: Useful Analyses and Common Pitfalls With the inherent challenges of the energy business, it is easy to make valuation mistakes. Even though it is difficult to measure their true extent, we believe that inferior analytics and wrong valuations have so far profoundly affected the investment decisions. We also believe that faulty valuations will not disappear overnight. It takes time for the learnings to take effect. Below are the common mistakes we came across in practice. • Wrong decision models: Oil and gas professionals heavily use decision trees to model and communicate their project understanding. This has been both beneficial and harmful. The benefits are in the ease of communications and clear thinking. But harms also arise from fitting general models to new and different opportunities, without paying enough attention to framing and understanding of the decisions, uncertainties, and value drivers. • Wrong uncertainty models: In any energy project, we should look for economically extractable resources. But it is easy to lose sight and look for any-size resources, recalling of the misleading adage “buy land, they do not make it anymore” from the real estate business. Buyers of property have long realized

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Note that carbon taxes do not convey such a collective message. A government-enforced tax on emissions, assuming it will stay the same, shows the unchanging will of the regulators. Through this lens, we also understand the potential rewards and penalties of a project, they will be as certain as paying corporate taxes. 7 https://www.goodreads.com/quotes/81867-for-a-successful-technology-reality-must-take-preced ence-over-public.

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that not all pieces of land have value. Similarly, not all opportunities are worth the shot! We discussed that the project decisions are inevitably multi-faceted, reflecting the geological, technical, and economical sources of uncertainty. The uncertainty models should therefore be about the chances of economically extractable volumes. A particularly troublesome mistake in practice is confusing the chance of geological success with the chance of commercial success. The latter is a more specific term reflecting the chance of discovering commercial amounts of hydrocarbons. Ignoring key inter-dependencies: Modeling is to make approximations. It is therefore natural to omit some real-world aspects in our decision models. Often, we omit the inter-dependencies in favor of modeling the overshadowing factors such as chance of success or the extent of resources. But sometimes inter-dependencies are crucial. For example, in multi-prospect exploration campaigns, or in drilling multisegment exploration prospects, inter-dependencies are key factors. Drilling a well that informs the chance of success about neighboring prospects, or, penetrating a specific segment of a multi-segment prospect, are not isolated decisions. They often involve learning in addition to earning. Sometimes the inter-dependencies control the fate of projects. Ignoring the human element: Embedded in all valuations is the active role of decision makers. When the conditions are ripe for value creation, they seize the opportunity. Their conscious decisions either avoids losses or capitalizes on the upsides. Yet because of their other motives, sometimes the acts of the decision makers do not conform to theory. For example, in our multi-prospect exploration example we assumed that the decision to drill each well depends on the value of the information that the well potentially reveals and the value of hydrocarbons that it potentially discovers. With these considerations, our analysis led to the best order of drilling. In practice, managers do not necessarily follow this recommended course of action and consider office politics, loyalty to their superiors, or morale of their employees. Hence, valuations should always be wary of the limitations of the decision makers. Disregarding the upsides and double counting the risks: The elements of valuations and decision models are like pieces of puzzle. They should fit one another to depict the big picture. This means the forecasts, the cash flow estimates, the probability estimates, and discounting should all be consistent. The “Flying Carpet” Assumption: A man walks in a carpet store. The seller welcomes him and says: “How can I help you sir?” “I am looking for a carpet to tie my apartment together” the man says. “We have this nice silk carpet. We also have this bright and beautiful wool carpet.” The seller then points to an ordinary carpet on the wall and says “Or, for one million dollars, you could have this flying carpet”. The man is surprised, he says: “What do you mean? Does it really fly?”

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The seller says “I guess you have heard of the stories. This is the famous carpet that flies. It has not flown yet. But the person who brought it last week said it needs to fully charge before it could fly. It has been charging ever since.” The seller then adds: “I am sure you are doubtful. It could be a fraud. I agree. But think about it. What if it really flies? Isn’t such a possibility worth a million bucks?” The man says: “It sure is worth a million dollars. But I just come from the broom store where they also tried to sell me a flying broom”. Like the “flying carpet and broom”, there are many far-fetched assumptions flying around in the analysis of energy projects. Often, they serve purposes other than to support investment decisions. To avoid these traps, we recommend a sense check for any analysis. The story of the analysis should make sense. Otherwise, it could be a flying carpet. Valuation is the application of commonsense and logical thinking in mathematical models. The complexities of the approaches should not deter us from clear thinking. The goal in any analysis should be clarity of thought for making good decisions.8

References 1. Pindyck, RS (1999) The long-run evolutions of energy prices. The energy journal 20(2) 2. Schwartz E, Smith JE (2000) Short-term variations and long-term dynamics in commodity prices. Manage Sci 46(7):893–911 3. Hahn WJ, Dyer JS (2008) Discrete time modeling of mean-reverting stochastic processes for real option valuation. Eur J Oper Res 184(2):534–548 4. Jafarizadeh B, Bratvold RB (2019) The option value of refracturing oil wells: implementation in a Binomial Lattice. SPE J 24(04):1903–1911 5. Jafarizadeh B, Bratvold RB (2012) Two-factor oil-price model and real option valuation: an example of oilfield abandonment. SPE Economics & Management 4(03):158–170 6. Jafarizadeh B, Bratvold RB (2015) Oil and gas exploration valuation and the value of waiting. Eng Econ 60(4):245–262 7. Bickel JE, Smith JE (2006) Optimal sequential exploration: a binary learning model. Decis Anal 3(1):16–32 8. Jafarizadeh, B, Bratvold, R (2020) The two-factor price process in optimal sequential exploration. Journal of the Operational Research Society, 1–11 9. Brown DB, Smith JE (2013) Optimal sequential exploration: bandits, clairvoyants, and wildcats. Oper Res 61(3):644–665

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I am grateful for the comments and suggestions provided by Reidar Bratvold (University of Stavanger), Farid Tayari (Penn State University), and the anonymous reviewers. Their comments have improved my work and has helped me gain clarity of thought. I am thankful to Asghar Shams (Heriot-Watt University) for his support. In addition, I appreciate the comments and feedback provided by participants in seminars, workshops, and lectures.