Risk, Uncertainty and Investment Decision-

Making in the Upstream Oil and Gas Industry

Fiona Macmillan

MA Hons (University of Aberdeen)

October 2000

A thesis presented for the degree of Ph.D. at the University of Aberdeen

DECLARATION

This thesis, and the research underpinning it, is entirely my own work. It has not been

submitted in any previous application for a degree. All quotations in the thesis have

been distinguished by quotation marks, and the sources of information specifically

acknowledged.

Signed:..

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ACKNOWLEDGEMENTS

Whilst this thesis is entirely my own work, many others have contributed to it and

shaped the end result in their own unique way and I would like to take this

opportunity to recognise them.

First, thanks must go to my sponsor, CSIRO Australia (Commonwealth Scientific and

Industrial Research Organisation), and supervisors for their enthusiasm, patience and

commitment, especially Professor Graeme Simpson, whose confidence in me and in

the value of my study never wavered even when I doubted it myself. Second, I would

like to acknowledge the respondents and the companies that they represented. For

their time, honesty and encouragement I am extremely grateful. Special thanks must

go to Steven McColl of Conoco, Jon Gluyas of Lasmo, Pat Mackintosh of DNV (Det

Norske Veritas) and Gillian Doyle of Wood Mackenzie for all their assistance. Last

and most importantly, I would like to thank my husband, Mike, my parents and my

friends - especially Gill, Vanessa and Natalie, for their love, support and unshakeable

belief in me and in all that I do. Without them, there would be no thesis.

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ABSTRACT

The research presented in this thesis is rooted within the existing decision theory and

oil industry literatures. It contributes to one of the current debates in these literatures

by providing evidence that in the operators in the U.K. upstream oil and gas industry

there is a link between the use of decision analysis in investment appraisal decision-

making by organisations and good business performance.

It is commonly acknowledged that decision analysis is not as widely used by

organisations as was predicted at its conception (for example, Schuyler, 1997). One

reason for this is that no study to date has shown that use of decision analysis

techniques and concepts can actually help individuals or organisations to fulfil their

objectives. Despite over four decades of research undertaken developing decision

analysis tools, understanding the behavioural and psychological aspects of decision-

making, and applying decision analysis in practice, no research has been able to show

conclusively what works and what does not (Clemen, 1999).

The current study begins to fill this gap by using qualitative methods to establish the

following. Firstly, the research identifies which decision analysis techniques are

applicable for investment decision-making in the oil industry, and thereby produces a

description of current capability. Secondly, the study ascertains which decision

analysis tools oil and gas companies actually choose to use for investment appraisal,

and through this develops a model of current practice of capital investment decision-

making. Lastly, using statistical analysis, it provides evidence that there is an

association between the use of decision analysis in investment decision-making by

companies and good organisational performance in the upstream oil and gas industry.

Such research not only contributes to the current theoretical debate in the oil industry

and decision theory literatures but also provides valuable insights to practitioners.

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CONTENTS PAGE Declaration i Acknowledgements ii Abstract iii List of figures vii List of tables viii

Chapter 1: Introduction 1

1.1 Introduction 2

1.2 Background to the thesis 2

1.3 Research questions 4

1.4 Outline of thesis 8

Chapter 2: Literature Review 10

2.1 Introduction 11

2.2 Risk and uncertainty 11

2.3 Current practice in investment appraisal decision-making 18

2.4 The evolution of decision theory 20

2.5 Decision analysis and organisational performance 31

2.6 Conclusion 37

Chapter 3: The Oil Industry in the U.K. 39

3.1 Introduction 40

3.2 Current challenges in the global oil industry 40

3.3 The oil industry in the U.K. 47

3.4 Investment appraisal decision-making in the oil industry 52

3.5 Conclusion 54

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Chapter 4: Methodology 55

4.1 Introduction 56

4.2 Adopting an appropriate methodological framework 57

4.3 Evaluating the effectiveness of the research methodology 69

4.4 Conclusion 71

Chapter 5: Current capability in investment appraisal in the upstream oil

and gas industry 72

5.1 Introduction 73

5.2 The concepts of expected monetary value and decision tree analysis 74

5.3 Preference theory 86

5.4 Risk analysis 98

5.5 Portfolio theory 105

5.6 Option theory 111

5.7 Current capability 118

5.8 Conclusion 125

Chapter 6: Current practice in investment appraisal in the upstream oil

and gas industry 126

6.1 Introduction 127

6.2 The use of decision analysis by organisations 128

6.3 The investment appraisal decision-making process 140

6.4 A model of current practice 152

6.5 Conclusion 155

Chapter 7: The relationship between the use of decision analysis in investment

appraisal decision-making and business success: a non-parametric

analysis 157

7.1 Introduction 158

v

7.2 The type of study 159

7.3 Ranking companies by use of decision analysis tools and concepts 161

7.4 Ranking companies by organisational performance 169

7.5 Proposing the hypotheses and selecting the statistical tests 175

7.6 Results 177

7.7 Discussion 179

7.8 Conclusion 180

Chapter 8: Conclusion: between extinction by instinct and paralysis by

analysis 182

8.1 Introduction 183

8.2 The research questions revisited 183

8.3 Theoretical contribution 189

8.4 Implications of the study to practitioners 193

8.5 Future research 194

8.6 Conclusion 196

Appendix 1: Interview Schedule 199 Appendix 2: Presentations and Papers 206 Appendix 3: The Spearman Correlation Test 208 Appendix 4: The Kruskal Wallis and Wilcoxon Rank Sum tests 209 Appendix 5: Critical values of for Spearman tests 214 Appendix 6: Critical Values of K for Kruskal Wallis test with 3

independent samples 215 Appendix 7: Critical Values of Chi-Square at the 0.05 and

0.01 level of significance 216 Appendix 8: Critical Values of S for the Wilcoxon Rank Sum Test 217 Bibliography 218

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LIST OF FIGURES

PAGE 3.1 Worldwide giant fields 42

3.2 Campbells prediction of world oil production after 1996 43

3.3 Distribution of remaining (Yet-to-Produce) oil (in Billions of Bbls)

by country 44

3.4 Distribution of remaining (Yet-to-Produce) oil (in Billions of Bbls)

by region 44

3.6 Actual spot Brent oil price over time 46

3.7 The average size of U.K. fields by discovery year 48

3.8 Discoveries by field-size class in the North Sea 49

3.9 Worldwide operating costs 50

5.1 The upstream oil and gas industry: a multi-stage decision process 75

5.2 Cumulative cash position curve 76

5.3 Typical spider diagram 79

5.4 An example of a decision tree 82

5.5 A preference curve 88

5.6 Typical preference curves 89

5.7 Analysis using EMV 92

5.8 Test results eliminated 93

5.9 The decision-makers preference curve 93

5.10 Analysis using preferences 94

5.11 Reducing risk through diversification 106

5.12 A 9-step approach to investment appraisal in the upstream oil

and gas industry 119

5.13 Choke model 124

6.1 A model of current practice 155

7.1 A decision tree 166

8.1 The relationship between decision analysis and behavioural decision

theory 192

8.2 Best practices in organisations use of decision analysis 193

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LIST OF TABLES

PAGE

2.1 Conceptualisations of risk and uncertainty 12

5.1 Discounted cash flow concept 78

5.2 Hypothetical field data 99

5.3 Hypothetical field data for Monte Carlo simulation 99

5.4 Results from the Monte Carlo simulation 100

5.5 Base value data and probability distribution assigned to each of the

reservoir parameters 104

5.6 Table of the output generated using the base value data and input

distributions specified in table 5.5. 104

5.7 Safe and risky projects 109

5.8 All possible outcomes of investing 50% in each