Thesis Template (single-sided) - NERC

CRANFIELD UNIVERSITY

SASAENIA PAUL OLUWABUNMI

COMPARATIVE RISK ASSESSMENT OF OFFSHORE OIL AND

GAS RISKS AND OFFSHORE RENEWABLES INSTALLATIONS

SCHOOL OF ENGINEERING

ENERGY SYSTEMS AND THERMAL PROCESSES

MSc

Academic Year: 2013 - 2014

Supervisor: Athanasios Kolios

August 2014

CRANFIELD UNIVERSITY

SCHOOL OF ENGINEERING

ENERGY SYSTEMS AND THERMAL PROCESSES

MSc

Academic Year 2013 - 2014

SASAENIA PAUL OLUWABUNMI

COMPARATIVE RISK ASSESSMENT OF OFFSHORE OIL AND

GAS RISKS AND OFFSHORE RENEWABLES INSTALLATIONS

Supervisor: Athanasios Kolios

August 2014

This thesis is submitted in partial fulfilment of the requirements for

the degree of Master in Science

© Cranfield University 2014. All rights reserved. No part of this

publication may be reproduced without the written permission of the

copyright owner.

i

ABSTRACT

The development of adequate energy sources to satisfy the ever increasing

energy demand in the world has led to the development of several offshore

energy sources. Offshore oil and gas industries and offshore renewables

installations have had a lot of growth in recent years, this growth has led to an

increase in accompanying risks and challenges in these industries. Thus, it is

pertinent to assess all the risks in the offshore energy industry, to create a ‘feed-

in base’ for both offshore renewables and offshore oil and gas industries to use.

This thesis critically identifies and analyses all the risks in the offshore energy

industry through a thorough and extensive literature review creating a

comprehensive risk register. Electronic surveys were created for both the

offshore oil and gas and offshore renewables industries; based on a total of eight

responses from professionals and Cranfield University doctoral researchers with

research interests peculiar to each of the offshore industries. The first stage risk

prioritization using FMEA was done. The second stage risk prioritization was

achieved through the TOPSIS methodology by the use of six criteria and the risk

priority numbers of each risk from the first risk prioritization stage.

The risks were ranked and subdivided into three groups namely: high, medium

and low risk scenarios based on results from FMEA and TOPSIS mathematical

models developed. The outcome of the risk assessment identified the critical risks

in the offshore oil and gas industry as: exploration risks, political instabilities,

fluctuating fiscal terms, geological risks, expropriation risks, health and safety

risks. The critical risks in the offshore renewables industries were identified as:

reduction of subsidies, supply chain instabilities, investment risks, structural

failure risks, compliance risks and fluctuating standards. Critical risks common to

both industries were identified as: market uncertainties, fluctuating policies,

environmental risks and price volatility.

Keywords: PESTLE analysis, multi criteria analysis, analytic hierarchy process,

weighted sum method, weighted product method, TOPSIS, failure mode and

effect analysis.

ii

ACKNOWLEDGEMENTS

First of all, I want to appreciate my supervisor, Athanasios Kolios, for his help,

guidance and advice during the course of my thesis work. His input was key in

the preparation of this dissertation and his motivation gave strength to my passion

when the sheer volume of work I had to do overwhelmed me.

I would also like to thank my academic sponsors, Esso Exploration Production

Nigeria Limited (Exxon-Mobil) for providing me with the financial leverage and for

covering all the expenses of my MSc program in Cranfield University. My sterling

academic performance would not have been possible without Mobil’s support.

My sincere gratitude also goes to my family back in Nigeria especially my parents,

Pastor and Pastor (Mrs) Oluwabunmi for their constant words of advice, support

and prayers that have kept me through the crest and troughs of life. A deep

reflection has made me realize how privileged I am to have you as my parents

and I’m forever grateful.

Lastly, but most importantly, I’d like to express my gratitude to God Almighty.

Many times, I faltered; many times, I stumbled; many times, I almost gave up;

but, I drew inspiration from God’s strength in me. I would eternally be grateful to

God’s help in my academics because my track record of excellence would not

have been possible without God’s grace.

iii

TABLE OF CONTENTS

ABSTRACT ......................................................................................................... i

ACKNOWLEDGEMENTS.................................................................................... ii

LIST OF FIGURES ............................................................................................. v

LIST OF TABLES .............................................................................................. vii

LIST OF EQUATIONS ........................................................................................ ix

LIST OF ABBREVIATIONS ................................................................................ xi

1 INTRODUCTION ............................................................................................. 1

1.1 BACKGROUND ........................................................................................ 1

1.2 AIMS AND OBJECTIVES ......................................................................... 3

1.3 THESIS STRUCTURE .............................................................................. 4

2 RISK IDENTIFICATION AND ANALYSIS........................................................ 5

2.1 OFFSHORE OIL AND GAS INDUSTRY RISKS ....................................... 5

2.1.1 POLITICAL RISKS ............................................................................. 7

2.1.2 OPERATIONAL RISKS ...................................................................... 9

2.1.3 ECONOMIC RISKS .......................................................................... 12

2.1.4 SUPPLY AND DEMAND RISKS ...................................................... 14

2.1.5 HEALTH AND SAFETY RISKS ........................................................ 16

2.1.6 ENVIRONMENTAL RISKS ............................................................... 17

2.1.7 STRATEGIC RISKS ......................................................................... 19

2.2 OFFSHORE RENEWABLE INDUSTRY RISKS (PESTLE ANALYSIS) .. 21

2.2.1 POLITICAL ANALYSIS .................................................................... 23

2.2.2 ECONOMIC ANALYSIS ................................................................... 25

2.2.3 SOCIAL ANALYSIS.......................................................................... 28

2.2.4 TECHNICAL ANALYSIS .................................................................. 29

2.2.5 LEGAL ANALYSIS ........................................................................... 33

2.2.6 ENVIRONMENTAL ANALYSIS ........................................................ 35

2.3 COMBINED RISK REGISTER ................................................................ 37

3 MULTI CRITERIA DECISION MAKING ........................................................ 39

3.1 INTRODUCTION .................................................................................... 39

3.2 MULTI CRITERIA ANALYSIS ................................................................. 42

3.3 AHP ........................................................................................................ 46

3.3.1 AHP IN ENERGY PLANNING .......................................................... 47

3.3.2 AHP PROCESS OVERVIEW ........................................................... 48

3.3.3 FUZZY AHP (FAHP) ........................................................................ 51

3.3.4 SPECIFIC APPLICATION OF THE AHP METHOD ......................... 52

3.4 WEIGHTED SUM AND WEIGHTED PRODUCT METHODS ................. 59

3.4.1 WEIGHTED SUM METHOD (WSM) ................................................ 60

3.4.2 WEIGHTED PRODUCT METHOD (WPM) ....................................... 60

3.4.3 SPECIFIC APPLICATION OF THE WSM AND WPM ...................... 61

3.4.4 WSM AND WPM IN ENERGY PLANNING ...................................... 64

iv

3.5 TOPSIS ................................................................................................... 64

3.5.1 INTRODUCTION .............................................................................. 64

3.5.2 TOPSIS PROCESS OVERVIEW ..................................................... 66

3.5.3 SPECIFIC APPLICATION OF THE TOPSIS METHOD ................... 69

4 FAILURE AND EFFECT MODE ANALYSIS (FMEA) .................................... 70

4.1 INTRODUCTION .................................................................................... 70

4.2 FMEA PROCESS MECHANISMS .......................................................... 70

4.3 RISK PRIORITY NUMBER (RPN) .......................................................... 71

4.4 MAJOR SHORTCOMINGS OF THE FMEA METHOD ........................... 75

5 OVERALL RISK PRIORITIZATION ............................................................... 76

5.1 METHODOLOGY.................................................................................... 76

5.2 FIRST STAGE RISK PRIORITIZATION ................................................. 84

5.3 SECOND STAGE RISK PRIORITIZATION ............................................ 88

6 RESULTS AND DISCUSSION ...................................................................... 92

6.1 OFFSHORE OIL AND GAS INSTALLATIONS ....................................... 92

6.2 OFFSHORE RENEWABLES INSTALLATIONS ..................................... 95

6.3 COMPARATIVE RISK ANALYSIS .......................................................... 97

7 CONCLUSIONS AND RECOMMENDATIONS ............................................. 98

7.1 CONCLUSIONS...................................................................................... 98

7.2 RECOMMENDATIONS FOR FURTHER WORK .................................... 99

REFERENCES ............................................................................................... 101

APPENDICES ................................................................................................ 123

Appendix A PAIRWISE COMPARISON MATRIX ........................................ 123

Appendix B FMEA STANDARD (BS EN 60812:2006) ................................ 125

Appendix C FMEA ELECTRONIC SURVEY SCREENSHOTS ................... 132

Appendix D EXCEL SPREADSHEETS ....................................................... 144

REFERENCES ............................................................................................... 161

v

LIST OF FIGURES

Figure 2-1 Oil and gas sector risk radar [14] ...................................................... 6

Figure 3-1 General MCDA process flow chart .................................................. 40

Figure 3-2 MCDA flow chart for sustainable energy applications [111] ............ 41

Figure 3-3 Complex interactions of sustainable energy systems [111] ............. 42

Figure 3-4 AHP process flow diagram .............................................................. 50

Figure 3-5 ANP process flow diagram [136] ..................................................... 51

Figure 3-6 Proposed hierarchical AHP model……………………………………..55

Figure 3-7 TOPSIS methodology stepwise analysis [163]………………………..67

Figure 4-1 FMEA implementation cycle…………………………………………....72

Figure 4-2 FMEA process flow chart based on survey results………………...….74

Figure 5-1 Methodology work flow chart……………………………………………77

Figure 5-2 Offshore oil and gas FMEA survey outcome…………………………..82

Figure 5-3 Offshore oil and gas risk prioritization…….………………………..…..82

Figure 5-4 Offshore renewables FMEA survey outcome………………………….83

Figure 5-5 Offshore renewables risk prioritization…….…………………………..83

Figure 6-1 Overlapping risks in offshore energy industries…………………...…..97

Figure A1-1 Typical AHP hierarchy [1]…………………………………………….123

Figure B4-1 Relationship between failure modes and failure effects [5]…….…128

Figure B5-1 FMEA analysis flow chart [5]…………………………………….…..130

Figure B5-2 FMEA criticality matrix [5]………………………………………..…..130

Figure C1-1 Electronic survey – Screenshot 1…………………………………...132








vi


Figure C2-4 Electronic survey – Screenshot 10…………..……………………...141

Figure C2-5 Electronic survey – Screenshot 11………………………………….142

Figure C2-6 Electronic survey – Screenshot 12….……………………………...143

vii

LIST OF TABLES

Table 2-1 Combined risk register ..................................................................... 37

Table 3-1 Pairwise comparison scale [131] ...................................................... 47

Table 3-2 Criteria and sub-criteria used for the AHP model ............................. 53

Table 3-3 Average investment costs of offshore energy technologies [149]……56

Table 3-4 Average operational life and construction time [153]…………………..56

Table 3-5 Life cycle CO2 emissions of offshore energy technologies [154]……...56

Table 3-6 Efficiency, land requirements and job potential [155]………………….57

Table 3-7 WSM and WPM evaluation matrix [149], [150], [151], [152]…………..62

Table 3-8 Weight of importance (wj) of considered criteria……………………….62

Table 4-1 Severity rating scale for online survey FMEA…………………………..73

Table 4-2 Occurrence rating scale for online survey FMEA………….…………...73

Table 4-3 Detectability rating scale for online survey FMEA……………………...74

Table 5-1 Offshore oil and gas industry RPN calculations…….........……………78

Table 5-2 Offshore oil and gas industry RPN calculations…….........……………80

Table 5-3 Offshore oil and gas low risk scenarios………………………………....85

Table 5-4 Offshore renewables low risk scenarios…………………..…………....85

Table 5-5 Offshore oil and gas medium risk scenarios…………………………....86

Table 5-6 Offshore renewables medium risk scenarios………………………......86

Table 5-7 Offshore oil and gas high risk scenarios………………..……………....87

Table 5-8 Offshore renewables high risk scenarios………………………............87

Table 5-9 Weights for TOPSIS criteria………………………………………….…..90

Table 5-10 Offshore oil and gas TOPSIS rank values………………………….…90

Table 5-11 Offshore renewables TOPSIS rank values……………………………91

Table 6-1 FMEA/TOPSIS offshore oil and gas risk rank comparison……………92

Table 6-2 FMEA/TOPSIS offshore oil and gas risk rank comparison……………95

Table A1-1 Fundamental ratio scale in pairwise comparison [2]……………….123

Table A2-1 RI vales [3]…………………………………………………………..….124

Table A2-2 The pairwise comparison matrix………………………………….….125

viii

Table A2-3 Modified RI values [4]…………………………………………..….….125

Table B4-1 Example set of general failure modes [5]…………………………....128

Table B5-1 Severity classification for end effects [5]………………………...…..129

Table B5-2 Modified criticality matrix…………………………………………...…131

Table B5-3 Failure mode evaluation criteria…………………………………...…131

Table D1-1 AHP mathematical model – Spreadsheet 1………………………....144



Table D2-1 WSM mathematical model……………………………………………147

Table D2-2 WPM mathematical model……………………………………………148

Table D3-1 FMEA mathematical model – Spreadsheet 1…………………….…149




Table D4-1 TOPSIS mathematical model – Spreadsheet 1…………………….153








ix

LIST OF EQUATIONS

(3-1) .................................................................................................................. 48

(3-2) .................................................................................................................. 49

(3-3)……………………………………………………………………………………49

(3-4)……………………………………………………………………………………57

(3-5)…………………………………………………………………………………....58

(3-6)…………………………………………………………………………………....60

(3-7)…………………………………………………………………………………....60

(3-8)…………………………………………………………………………………....61

(3-9)…………………………………………………………………………………....63

(3-10)…………………………………………………………………………………..63

(3-11)…………………………………………………………………………………..63

(3-12)…………………………………………………………………………………..65

(3-13)…………………………………………………………………………………..65

(3-14)…………………………………………………………………………………..65

(3-15)…………………………………………………………………………………..67

(3-16)…………………………………………………………………………………..67

(3-17)…………………………………………………………………………………..68

(3-18)…………………………………………………………………………………..68

(3-19)…………………………………………………………………………………..68

(3-20)…………………………………………………………………………………..68

(3-21)…………………………………………………………………………………..68

(4-1)…………………………………………………………………………………....71

(5-1)…………………………………………………………………………………....88

(5-2)…………………………………………………………………………………....88

(5-3)…………………………………………………………………………………....89

(5-4)…………………………………………………………………………………....89

(5-5)…………………………………………………………………………………....89

(5-6)…………………………………………………………………………………....89

x

(5-7)…………………………………………………………………………………....89

(5-8)…………………………………………………………………………………....89

(5-9)…………………………………………………………………………………....89

(6-1)…………………………………………………………………………………....92

(A2-1)………………………………………………………………..……………….124

xi

LIST OF ABBREVIATIONS

ACER Agency for the Cooperation of Energy Regulators

AHP Analytic Hierarchy Process

ALARP As Low As Reasonably Practicable

ANP Analytic Network Process

BCR Benefit Cost Ratio

BP British Petroleum

CAPEX Capital Expenditure

CEFAS Centre for Environment, Fisheries and Aquaculture Sciences

CO2 Carbon (iv) Oxide

CORDIS Community Research and Development Information Service

CR Consistency Ratio

CSB Chemical Safety Board

DECC Department of Energy and Climate Change

DEFRA Department for Environment, Food and Rural Affairs

DMO Domestic Marketing Obligation

DNV Det Norske Veritas

DOE Department of Environment

EIA Environmental Impact Assessment

ELECTREE ELimination and Choice Expressing Reality

ES Environmental Statement

E&P Exploration and Production

EM Electro Magnetic

EMEC European Marine Energy Centre

ETI Energy Technologies Institute

EU European Union

EU-OEA European Union - Ocean Energy Association

FAHP Fuzzy Analytic Hierarchy Process

FCM Fuzzy Cognitive Map

FDM Fuzzy Decision Map

FMEA Failure Mode and Effect Analysis

FMCEA Failure Mode Cause and Effects Analysis

FMECA Failure Mode Effects and Criticality Analysis

xii

FPSO Floating Production Storage and Offloading

FTA Fault Tree Analysis

GHG Green House Gases

GHz Giga Hertz

GPS Global Positioning System

GW Giga Watt

HAZOP Hazard and Operability Study

HFLS Hesitant Fuzzy Linguistic Set

HFLTS Hesitant Fuzzy Linguistic Term Set

HPC High Performance Computing

HSE Health, Safety and Environment

HVAC High Voltage Alternating Current

HVDC High Voltage Direct Current

IAEE International Association for Energy Economics

IALA International Association of Aids to Lighthouse Authorities

ICEPT Imperial College Centre for Energy Policy and Technology

IEA International Energy Agency

IOC International Oil Company

IPO International Property Office

IRENA International Renewable Energy Association

IT Information Technology

JNCC Joint Nature Conservation Committee

KWh Kilo Watt Hour

LCI Local Content Initiative

LFDN Linguistic Fuzzy Decision Network

LNG Liquefied Natural Gas

MADM Multi Attribute Decision Making

MCA Maritime and Coastguard Agency

MCDA Multi Criteria Decision Analysis

MCDM Multi Criteria Decision Making

MCGDM Multi Criteria Group Decision Making

MEAD Maritime Energy Array Demonstrator

MMO Maritime Management Organization

xiii

MODM Multi Objective Decision Making

MOLP Multiple Objective Linear Programming

MW Mega Watt

NGO Non-Governmental Organization

NIMBY Not In My Backyard

NREL National Renewable Energy Laboratory

NTSB National Transportation Safety Board

OPEC Organization of Petroleum Exporting Companies

OPEX Operating Expense

OR Operations Research

OREI Offshore Renewable Energy Installations

ORJIP Offshore Renewables Joint Industry Program

OWA Ordered Weighting Average

PESTLE Political, Economic, Social, Technical, Legal and Environmental

PHMSA Pipeline and Hazardous Materials Safety Administration

RAFTS Rivers and Fisheries Trust of Scotland

REZ Renewable Energy Zone

RI Random Index

RSPB Royal Society for the Protection of Birds

RNLI Royal National Lifeboat Institution

ROC Renewables Obligation Certificate

ROI Return On Investment

RPN Risk Priority Number

RYA Royal Yachting

SEA Strategic Environmental Assessment

SOP Standard Operating Procedure

SPE Society of Petroleum Engineers

SWIFT Structured What If Technique

TEC Tidal Energy Converter

TFN Trapezoidal Fuzzy Number

TOPSIS Technique for Order Preference by Similarity to Ideal Solution

UNFCCC United Nations Framework Convention on Climate Change

UK United Kingdom

xiv

UKERC United Kingdom Energy Research Centre

UN United Nations

UN-DESA United Nations – Department of Economic and Social Affairs

UNDP United Nations Development Program

UNESCO United Nations Educational, Scientific and Cultural Organization

URL Uniform Resource Locator

US United States

USAEE United States Association of Energy Economics

VHF Very High Frequency

VTS Vessel Traffic Services

WEC Wave Energy Converter

WPM Weighted Product Method

WSM Weighted Sum Method

1

1 INTRODUCTION

1.1 BACKGROUND

The offshore oil and gas sector generates around £20 billion of revenue per

annum and £12.8 billion of Gross Value Added (GVA) whilst supporting induced,

indirect and direct employment of more than 190,000 people; thus, making it one

of the key sectors of the UK economy [1].

This scenario is true not only for the West but also for major emerging economies;

for example in Nigeria, offshore oil and gas earnings of over £18 billion per annum

account for more than 98% of the country’s export earnings, 83% of the federal

government revenue, 14% of the country’s Gross Domestic Product (GDP), 95%

of foreign exchange earnings and 65% of the government’s budgetary revenues

also making this sector the mainstay of the burgeoning economy [2].

Offshore oil and gas installations and processes around the world are

‘converging’ and becoming increasingly similar with the major international oil

companies participating actively in most exploration activities [3]. However, this

cannot be said of the offshore renewables industry.

The offshore renewables industry is relatively ‘young’ compared to the offshore

oil and gas industry and still has a lot of potential for growth especially in the

West. The UK is estimated to have about a quarter of Europe’s potential wind

and tidal energy capacity and a tenth of its wave resource [4]. Most offshore

renewable installations in the UK are in the North Sea region where a vast

majority of the country’s offshore oil and gas assets are located.

A huge number of elements such as offshore installations, risk management,

personnel transfer, offshore operation and maintenance activities required to

develop offshore renewable projects have already been developed by the oil and

gas sector [5]. This has made many oil and gas firms to start operating offshore

renewable projects especially offshore wind, putting their expertise to use; using

the same protocols and standards of the offshore oil and gas industry [4].

2

Most developing countries especially in ‘resource-rich’ Africa on the other hand

presently have no offshore renewable energy installation, despite the fact that

there are huge potentials for these countries to develop such.

Most oil producing countries in these economies have the capacity for developing

large scale offshore renewables projects especially in the shorelines and the

water bodies which are presently dotted with a large number of offshore oil and

gas structures [6]. However, with growing energy concerns in Africa and the push

for diversification of the economy from huge reliance on the oil and gas sector

due to growing sustainability concerns, offshore renewable projects would gain

leverage and more popularity [7].

When this happens, the major oil and gas players would want to take the lead to

keep up the upper hand they currently have in the energy sector; and similar

protocols of the oil and gas industry would be applied to the offshore renewables

industry like is happening in the West.

Skills transference from the oil and gas sector and incorporation of the oil and

gas supply chain has the potential to reduce the cost of offshore renewable

operations by 20% based on significant areas of crossover, application of similar

knowledge and skills and favourable economies of scale [1]. However, despite

these advantages; there are a lot of risks to be considered in shifting from

offshore oil and gas to offshore renewables.

Hence, this thesis does a comparative analysis of the risks in the offshore oil and

gas installations and offshore renewables industry using various methods with

the view of identifying the risks with the highest probability of occurrences. The

risks after analysis and assessment are prioritized and grouped as high risk

scenarios, medium risk scenarios and low risk scenarios to enable the afore

mentioned industries adequately monitor these risks and employ relevant risk

mitigation procedures where necessary.

3

1.2 AIMS AND OBJECTIVES

This thesis aims to exhaustively consider all the risks inherent in the offshore oil

and gas industry and the offshore renewables industry using both qualitative and

quantitative risk analysis methods.

Practices in both industries would be correlated using the PESTLE approach; the

overall aim being the assignment of requisite scores to each of the outcomes of

risk analysis using FMEA and MCDM methods.

In order to achieve this aim, the following key project objectives would be

considered:

Extensive literature review to identify all possible risks in the offshore oil

and gas and offshore renewables industries.

Development of a risk register synonymous to each project stage for both

offshore renewables and offshore oil and gas installations.

HSE considerations and impact analysis of both installations.

Review of possible risk registers and matrices for risk classification and

analysis.

Comprehensive risk assessment of all possible risks involved in shifting

from offshore oil and gas installations to offshore renewable energy

projects.

Systematic classification of risks using the PESTLE approach, FMEA and

MCDM methods of analyses.

Identification of possible risk mitigation processes for identified risks.

Assignment of risk priority numbers based on outcome technical survey

conducted by industry and academic experts.

Inference deduction from comparative analysis outcome.

Identification of possible further research work areas.

4

1.3 THESIS STRUCTURE

The second chapter considers all possible risks involved in the offshore

renewables and petroleum industries by an extensive review of literature.

Essential considerations and impacts of such installations are discussed and

analysed in depth, with relevant ‘high level’ risk classifications applied.

The third chapter contains a review of MCDM methods used for risk analyses

with a core focus on AHP, WSM, WPM and TOPSIS methods. FMEA and FMCEA

methods of analyses are introduced in chapter four and the assignment of risk

priority numbers, which is subsequently applied to the risk register developed for

a technical survey conducted.

Technical survey analyses results and statistics from industry and academic

experts from survey results are done in chapter five. Based on results of the initial

survey conducted and the first stage risk prioritization done using the FMEA

method, second stage risk prioritization is done using the TOPSIS method. A

scale down of risks from the high level to the low level is populated according to

the risk prioritization numbers. This chapter contains requisite calculations and

models; necessary inferences are also deduced and discussed.

Chapter six presents the outcome of the comprehensive risk assessment done

and a discussion of the results presented. Comparative analysis outcomes based

on the assignment of risks are also presented and explained.

The last chapter of the thesis shows relevant conclusions deduced from the

overall thesis findings and suggestions for future research work.

5

2 RISK IDENTIFICATION AND ANALYSIS

2.1 OFFSHORE OIL AND GAS INDUSTRY RISKS

An extensive literature review on possible risks affecting the offshore oil and gas

industry shows risks peculiar to any offshore installation and added risks due to

the characteristics of oil and gas exploration and production. In accordance with

the popular maxim by Edmund Burke: ‘those who don’t know history are destined

to repeat history’ [8]; oil and gas companies involved in offshore installations keep

a record of past project risks in order to create a better framework for properly

identifying risks which can occur in similar future projects [9].

Increasing concerns about overall project ‘life cycle cost’ and ‘safe operations’

has made risk identification a very key aspect of decision making with regards to

embarking on offshore oil and gas projects [9].

Proper risk identification also affects the feasibility of an adequate ROI of most

offshore projects, which is increasingly becoming a major aspect of project

investment decisions [9], [10]. An extensive analysis of the possible risks in both

the offshore oil and gas industries show a set of risks which recur the most in all

literature considered.

These risks include: engineering, commercial, geological, operational,

commercial, climate, social, supply-demand, socioeconomic, HSE, asset

damage, business interruption, pollution, accidents, compliance/non-compliance,

cost overruns and security risks (including cyber threats)1 [8], [9], [11].

The oil and gas industry faces a lot of uncertainty and risk [12]; however, due to

the world’s increasing thirst for energy, companies have to contend with these

risks to have financial, operational and strategic success [13].

Ernst and Young in its business review on the energy sector highlights the major

risks which oil and gas companies (as a result of both onshore and offshore

1 The Stuxnet computer virus targeted at oil companies in the Middle East (Saudi Aramco in

Saudi-Arabia and RasGas in Qatar) is a classic example of increasing cyber-security risks that oil and gas companies are currently facing [11].

6

activities) are prone to; which are: access to reserves and competition arising

from political constraints; uncertain energy policies; cost containment; worsening

fiscal times; HSE risks; human capital deficit; operational challenges; climate

change concerns; price volatility and competition for new technologies [14].

An exhaustive analysis of the risk types identified from various literature

considered shows that they can be broadly classified into seven main groups

which are: political; operational; economical; supply and demand; health and

safety; environmental and strategic risk groups. These risk groups would be

subsequently analysed further in the following sections.

Ernst and Young further created a risk radar model to act as a snapshot of the

top ten risks for global oil and gas industries in 2013 as shown in Figure 2-1.

Figure 2-1 Oil and gas sector risk radar [14]

7

2.1.1 POLITICAL RISKS

The political climate has a significant impact on oil and gas companies, various

regulatory and statutory obligations imposed on these companies by the

government goes a long way in affecting how business is done [13].

The level of local and national uncertainty in the political sphere in areas of

offshore installations also poses a serious risk in strategic operations [11]. Geo-

political regulations limit the location and time that offshore extractions can be

carried out and the severity of these laws vary from location to location [12].

The continued state of flux of energy policies and expanding government roles

such as debates and calls for more stringent regulations in offshore oil and gas

activities following various oil spills such as that in the Mexican Gulf [14] has

increased political concerns for IOC’s.

These political risks increase in intensity when extraction is carried out beyond

the local shores of these oil and gas companies. Countries with a stable political

system and ‘friendly’ offshore laws attract a lot of oil and gas companies.

However; with increasing demands for energy, there is an increasing propensity

to prospect and extract oil and gas offshore in unstable political regions [11], [12].

This might lead to expropriation or nationalization2 of the oil and gas companies

and a subsequent loss of investment capital [9]. Nationalization of resource

companies occurs mostly in the ‘oil-rich’ Middle-Eastern countries such as: Saudi-

Arabia, Iran, Iraq, Kuwait and Russia [15].

Other ‘mildly’ and ‘extreme’ left-wing countries such as Bolivia, Venezuela and

Brazil have also successfully nationalized offshore oil and gas operations in their

countries sometimes forcefully [16]. It is popularly reiterated by ‘oil and gas’ policy

makers that “the decisions that a company ends with, regarding government

deals are not necessarily synonymous to the decisions that the company begins

2 Expropriation is defined as the process whereby a government takes over private property for a

purpose deemed to be for public interest; nationalization occurs when a government does an expropriation and hands over the ‘seized assets’ to a national company [19].

8

with” [13]. A shift in government can occur in unstable regions thereby overturning

policies and striking deals that give the government more profit.

Political risks are rife in countries with a dictatorship track compared with

developed countries having stable governments; and unified legal systems using

the Hull Doctrine3 where oil and gas companies can effectively sue the

government for any wrongdoing against them or breach of contract [15], [16].

The government can also enact policies that favour domestic companies with little

or no expertise having a large share in the local offshore operations of IOC’s; this

is called the ‘local content initiative’ and various LCIs are widespread in the oil-

rich regions of the developing world especially China, Nigeria, Ecuador, Libya,

Algeria, Argentina, Mexico, Indonesia, Ghana, Equatorial Guinea, Kazakhstan

and Uganda [9], [16], [18]. Governments in these countries also use tax claims

(both real and fictitious) to ‘meddle’ with operations of oil and gas companies in

their deep waters; these acts as a major source of concern to be considered

during offshore projects [14], [16].

Violence and terrorism acts as a result of political unrest are carried out by

locales; and usually target offshore oil and gas installations for rebuttal against

the government such as the Niger-delta militants in Nigeria [18]. This makes

recruiting of employees and posting of expatriates to these regions very risky.

Government regulations regarding currency exchange also incur risks to offshore

oil and gas operations because revenue generated by offshore projects might not

be convertible to hard currency [20]; due to restrictions and inadequate access to

needed ‘hard currency’.

For example, Venezuela restricts sales of foreign currency to its central bank

only; because of increasing political tensions between Venezuela and the United

States, the dollar which is required by most IOCs for transaction is difficult to

access in Venezuela [21]. Political tensions also cause fluctuating currency

3 The ‘Hull Doctrine’ was advocated by the United States of America and functions as the

international standard for expropriation where: ‘the expropriating country has to provide effective, adequate and prompt compensation [9].

9

exchange rates; thereby, affecting the internal accounting procedures of offshore

oil and gas companies making projected values of ongoing transactions highly

unpredictable thereby indirectly leading to fiscal risks [22].

Additionally, offshore oil and gas operations that hugely affect the local populace

and the labour market have a likelihood to face more risk than those not

generating huge local populace concerns; especially regarding environmental

and health impacts [10].

Thus, a lot of analysis is required by oil and gas companies in assessing the

political risks involved in their operations and considerations need to be given to

long-term views of the government regarding foreign development of its offshore

oil and gas resources [9].

2.1.2 OPERATIONAL RISKS

Offshore oil and gas operations involve difficult engineering tasks which occur

most times in deep water; and as such are prone to a lot of geological risks. As a

result of the world’s heavy demand for petroleum products, most shallow water

oil and gas resources are depleted [13].

Hence, managing offshore exploration projects are increasingly becoming risky

due to the proliferation of the drilling of multiple small wells in deep water

compared to the former era of larger reserves offshore [11]. Offshore oil and gas

explorations are thus carried out in the farthest regions of water bodies and

oceans with undulating structures [11], [14].

Increasingly dangerous and unconventional techniques have been developed for

offshore oil and gas extraction to surmount difficulties in reaching these resources

[13] leading to new operational challenges in these ‘unfamiliar environments’ [14].

Although, international collaborations between IOCs on E&P projects to

overcome and minimize possible risks involved in assessing the hydro-carbons

in these extreme regions is popular, a high level of risk still occurs in tactical and

10

strategic operations because of the complexity of the offshore projects and

collaborative efforts [11].

Several geological risks and geo-hazards due to offshore oil and gas operations

have been identified as either man-made or natural [10]. Natural geological risks

include [23], [24]: reservoir structure, hydrocarbon charge and seal, slope

instability, shallow gas, natural gas hydrates and their climate-controlled

dissociation, shallow water flows, mud diaprism, mud volcanism, active fluid

seepage, sea floor pockmark formation, seismicity4, excess pore pressure

development, fluid migration, sediment accumulation, seabed sediments.

Man-made geological risks [23], [24], [25] are: existing offshore oil and gas

installations, existing offshore renewables installations, pipeline and cable works,

ship wrecks, naval operations and military dumping grounds.

Several marine conditions such as: lightning strikes, thunder storms, winds,

tropical storms, loop and eddy currents, turbidity flows, waves and tides are

erratic and might not be easily predictable [23]; these conditions might also

exceed the inbuilt operational capabilities of offshore vessels [25]. Thus, they

cause a high level of risk to day-to-day offshore operations and may lead to

temporary or permanent shut-down of operations [26], [27].

Apart from extraction difficulties, there are also huge risks that can arise due to

oil and gas deposits being smaller than estimated leading to sizeable investment

losses. Geoscientists and geologists; thus, embark on a series of testing to

ascertain the level of oil and gas reserves in an offshore site to reduce the risk to

ALARP; this forms a major input to offshore project decisions [11], [13].

Also, according to Ernst and Young’s 2013 petroleum sector survey report:

‘offshore oil and gas operations are limited because of a growing human capital

deficit’ [14]. The survey showed that twenty-two percent of IOC respondents

claimed that the lack of adequately skilled personnel such as geophysicists,

4 Seismicity refers to earthquakes and tremors affected by human activities which cause strains,

stresses and pressures on the earth’s crust. These strains and stresses trigger slides which cause tsunamis and an agitation of subsurface geological conditions [10].

11

geologists, engineers and IT personnel (skilled in HPC) was causing a negative

impact on their operations. Critical material resources such as steel, cement and

vessels [10] required for offshore oil and gas development might be unavailable,

thereby causing delays, substantial cost increases, and reduction in work quality;

thus, adding to operational risk [9].

Day-to day operational risks such as possibilities of material supply delays,

breakages, faults etc. are deemed to be more prominent in offshore oil and gas

installations in developing countries compared to developed countries because

the market environment in these countries are not fully capable of providing real-

time solutions to these concerns [9].

Hence, operational risks due to day-to-day operations are increased in this regard

for developing countries. Also, SOPs for major petroleum countries operating in

foreign countries may be superseded by government regulations thereby

reducing operational efficiency in these regions [9], [11].

Increased competition in the offshore industry especially with new technologies

developed for offshore renewables also increase operational risk for the offshore

oil and gas industry [11], [14].

Engineering risks can be classified as operational and the major engineering

areas of concern include [28]: exploration risks (mobile drilling units and drilling

operations), construction and installation risks, processing and separation risks,

transmission and decommissioning risks. Infrastructure conditions such as: port

and pipeline access, facility capabilities, transport conditions (roads, rails and

bridges) and market access. These also impose additional operational risks to

offshore oil and gas installations [29].

A lot of transportation risks occur during offshore production because operators

might need to construct new pipelines, transfer stations, refineries, LNG terminals

and tankers (when using a FPSO vessel) to transfer oil and gas to the market [9].

Port congestions and inadequate access to pipeline services for the

transportation of oil and natural gas from offshore installations to a broader

market can pose significant problems during operations [30].

12

2.1.3 ECONOMIC RISKS

The fluctuating price of oil and gas as a result of policies made by both OPEC

and non-OPEC member countries adds an amount of risk which should be

considered in determining the economic feasibility of exploring proven or

suspected offshore oil and gas reserves [15].

Various cyclical factors and the end of ‘easy to get oil’ has consistently driven

E&P costs up and this is projected to increase [14]. Cases of militancy in the

Niger-Delta region of Nigeria, unrest in the Middle-East and political instability in

North Africa increases price volatility; thereby, posing a significant economic risk

to be considered [14], [15].

From, a concise analysis of facts and literature, it can be stipulated that the level

of price risk faced by offshore oil and gas projects is directly proportional to the

level of geological risks. The more difficult it is to access a proven reserve, the

more unconventional the technology required would be and the more expensive

the E&P process would cost.

Proven technologies also offer less financial risks compared to new technologies

or applications of existing technologies in a new setting [31]; new technologies

attract higher risk premiums by investors because of the high level of uncertainty

such technologies are associated with [32].

The financial requirement for most companies venturing into offshore oil and gas

installation projects is provided by a combination of shareholders, investors and

banks on the basis of the company’s investments, technologies and businesses

[31]. If the offshore oil and gas project invested in is unsuccessful, the company

absorbs the losses and the costs by using other successful projects to subsidize

the venture [32]. This increases the operational risk of other company projects

since their cash flow is reduced.

13

Various factors affecting project finance such as: access to capital5, cash flow,

profitability, TVM, solvency, capital budgeting and financial market investment

equity (debt/capital) also serve as high risk prone areas from project start to

completion [10].

An increasing shift in investment perspective from offshore oil and gas to offshore

renewables by long standing investors of the oil and gas sector especially in the

Euro-zone also poses an increasing challenge for IOCs seeking third party

investment [10].

Investment in the offshore renewables sector over the past five years increased

by 35% compared to the offshore oil and gas sector which increased by less than

half of that percentage [32]; there is also a ‘stampede’ by venture capitalists to

invest into ‘clean energy’ such as offshore wind compared to offshore oil and gas

[33]. This trend is projected to increase with the enactment of more stringent laws

on ‘clean energy’; thereby, reducing the ease at which offshore oil and gas

companies can get funds and subsequently increasing financial risk [10], [33].

Thus, investment forecasts need to take into account price risks over the project

life cycle to make a suitable ‘Go/No-go decision’ and effectively manage the

expectations of stakeholders in relation to the degree of financial risk acceptable

and expected ROI .

A thorough analysis of the inter-relationship of the individual project elements to

create a concise financial risk profile of the venture is needed [31]; to optimize

risk-reward relations for all equity, debt and other investments. In assessing

financial risks for offshore oil and gas funding; financing options, project time and

financial security have to be considered.

5 ‘Access to capital’ challenges can limit growth opportunities for offshore oil and gas companies.

If a company’s cash flow is less than its capital budgeting; the company has to shed assets, increase borrowing margin or increase shares of its stock to cover the cost of capital [10].

14

2.1.4 SUPPLY AND DEMAND RISKS

Variations in energy supply and demand act as sources of uncertainty for offshore

oil and gas companies. Projected operations might not always be flexible enough

to follow unanticipated changes in market supply and demand [13].

Unstable market conditions (such as supply/demand crests or troughs) directly

lead to price volatility which can cause a possible decrease in anticipated ROI.

Although, the market regime for oil is global while that for natural gas is more

localized6 [9]; both offshore oil and gas can be subjected to a DMO which can

significantly reduce its market value [34].

Offshore oil and gas companies require a lot of material resources for offshore

development [10]; and project success depends hugely on a steady supply of

resources. The shortage of materials, vessels, and manpower can lead to project

lag which might also contribute to the risk of failure.

Offshore oil and gas companies face short and long term resource shortages due

to economic imbalances between purchasers and suppliers of resources [34].

These imbalances occur as a result of unpredictable interactions of supply and

demand for a particular resource.

Oil and gas companies try to lower these supply and demand risks caused by

imbalances by shifting operations to low cost centres within the company,

restructuring of the supply chain and outsourcing some supply and demand

functions [35].

Supply and demand risks are also dependent on the market in which the offshore

oil and gas project would operate in and possible economic effects [34].

Economic effects that affect projects can be sub-divided into macro-economic

effects and micro-economic effects [10].

6 The localized nature of the natural gas market means that the host country may require a

significant proportion of the gas produced to be sold in the local market. In an environment in which competition is less than robust, the value the local market offers for the gas might be lower than the gas quality; thereby reducing market value and reducing profit [9].

15

The relevant micro-economic effects to the offshore oil and gas industry are:

competitors, price determination, availability of alternatives and utilization level

while the corresponding macro-economic effects are trade, economic growth,

exchange rates, commodity prices and interest [10], [35].

These economic scenarios affect supply and demand collectively and individually

and impose additional risks for potential investors in the offshore oil and gas

industry.

It has been discovered that the level of complexity of the project increases supply

and demand risks with highly complex offshore oil and gas projects such as field

development projects7 having more supply and demand risks compared to

projects with smaller scopes [10].

The kind of technology used also causes supply and demand risks because the

use of new technologies open up new markets and improve existing markets: this

increases supply and demand risks considerably but has the potential for also

increasing competitive advantage for the offshore oil and gas companies;

compared to the use of proven technologies with a lower supply and demand risk

effect and lower possibility of increasing competitive advantage [34].

Suppliers providing services, equipment, personnel and raw materials, clients

and operators which are involved in the project development and operations

phases also add a significant amount of supply and demand risks to the overall

project which has to be considered [35].

7 A Field development project is a complex project which can be broken down into several sub-

projects. Each sub-project might have a peculiar market with directly relevant supply and demand characteristics imposing different supply and demand risks for each project phase [10].

16

2.1.5 HEALTH AND SAFETY RISKS

Health and safety risks are considered one of the most important risks in the

offshore oil and gas industry due to more complex operational challenges,

increased public awareness and pressures from various NGOs [14].

Although, oil and gas companies often consider HSE regulations as top priority

in all their E&P activities, these regulations are getting constantly more stringent

to take into account new risks as a result of technological development and more

unconventional offshore exploration techniques [11].

Series of shutdowns and fires like the ones in Chevron Richmond [36], BP Cherry

Point and Amuay in Venezuela, 2012 [37]; the California San Bruno pipeline

explosion in 2010 [38], the Deep-water horizon drilling rig accident in the Gulf of

Mexico in 2010 [39] and the Pemex pipeline explosion in 2012 [40]; have

significantly increased public awareness about the huge health and safety risks

of the offshore oil and gas industry and the corresponding huge impacts if such

risks occur.

This has led to the promulgation of new offshore drilling safety requirements and

pipeline safety regulations8; either local, national or international regarding air

quality, noise levels, confined space conditions, work time periods and working

at height conditions [10].

Incorporation of these requirements and accounting for the possible health and

safety risks increases the development costs and also the financial uncertainties

and consequently the overall project risk. High risk activities in offshore oil and

gas might be fatal or might cause serious accidents to the workers which might

lead to significant legal and other strategic risks.

Constantly changing health and safety regulations regarding inspection,

reporting, asset management, drilling operations, accidents and offshore

8 An example of a very important pipeline safety regulation is the US Pipeline Safety, Regulatory

Certainty and Job Creation Act of 2011 designed to examine and improve the state of both onshore and offshore pipelines [41].

17

reservoir analysis mean that offshore oil and gas companies increasingly face the

risk of being non-compliant [11].

Health, safety and environment risks include possible harmful occurrences due

to [42]:

Hazardous substances: biological substances, toxic and flammable

materials.

Work environment: vibration, noise, poor lighting and hot ambient

temperature.

Work activities: poor workplace design, poor visual ability and repetitive

actions.

Radiation: Ionizing or non-ionizing radiation, EM waves and laser beams.

Electrical installations: HVDC or HVAC circuits; sparks and ignitions; short

circuits.

Mechanical installations: High pressure processes, impacts from falling

objects.

2.1.6 ENVIRONMENTAL RISKS

Apart from health and safety risks; environmental risks also pose a huge

challenge for offshore oil and gas companies. Risks related to climate and

environment concerns permeate more than local regulatory and compliance

issues but affect the whole world [14].

The Copenhagen climate change conference9 and other similar global

conferences caused major stakeholders to demand a lot from oil and gas

companies exploring offshore to reduce the risks their operations expose the

environment to.

The risk of oil spillage and gas flaring causing pollution of the water bodies and

the atmosphere respectively are the major environmental risks the offshore oil

9 The Copenhagen climate change conference was held in December 2009 and facilitated climate

change policy discussions (caused by energy development) with world policy leaders [43].

18

and gas industry presents. This risk is very high and pertinent as a result of

current happenings especially in the Niger-delta region of Nigeria and the North-

Sea region of the United Kingdom [43].

Environmental risks by the offshore oil and gas industry are majorly caused by oil

spillage from exploration, production and transportation. These risks occur from:

crude carriers, offshore transport by pipeline, loading buoys, drilling and

processing [44].

Oil spillage from the offshore oil and gas industry present further environmental

risks which include: increase in biomass production and accumulation of organic

materials leading to algal production; accumulation of toxic materials causing

damage to aquatic life [45].

Major environmental risks occurring as a result of oil spillage can be categorized

based under the following: oil exploration; oil transport (through risers, flow-lines

and pipelines), on-loading and off-loading of oil based drilling mud, well drilling,

well production, well work-overs, tanker loading (offshore loading), offshore use

and storage of diesel oil, offshore storage of crude oil [46].

Apart from water pollution, the risk of air emissions which can cause harm to living

organisms, biological and anthropological sites is also present from offshore oil

and gas operations.

The release of various GHGs especially CO2 which can cause global warming

and ozone layer depletion contributes to environmental risk [45]. High profile

environmental accidents may lead to huge legal costs and possible stoppage of

operations in the affected regions; this leads to other corresponding risks such

as strategic and operational risks [46], causing an overall ‘chain effect’.

19

2.1.7 STRATEGIC RISKS

The World Energy Outlook compiled by the IEA projects that world oil and gas

demand would grow by over 33% in 2035 because of increase in standards of

living in China, India, the Middle East and Africa which would account for over

65% of that increase [47].

This projected growth poses serious risks for IOCs operating offshore in meeting

future energy demands, correspondingly affecting strategic decisions. Also;

‘unconventional plays’ such as mass production of shale gas which is available

in large quantities and has future prospects of being cheaper than oil and gas

produced from offshore installations pose huge risks for the offshore oil and gas

industry [47].

Most consumers would gravitate towards the cheaper energy sources reducing

the reliance on offshore energy; this scenario has a high probability of reducing

the asset base and profitability ratio of these companies in the future [11].

The proliferation of independent producers and a more decentralized market

system might also cause strategic risks for the offshore oil and gas industry.

Independent producers develop 90% of domestic offshore oil and gas wells

(except in most African offshore fields), produce 65% of domestic oil and 80% of

domestic natural gas [47].

According to the Independent Oil Producers Association, this trend is bound to

increase in the future years which would significantly affect the strategic growth

of offshore oil and gas companies leading to other kinds of risks.

Legal risks are also increasingly pertinent; the host country’s legal system might

favour the government and not uphold contracts if the offshore oil and gas

companies need to seek for a redress or sue for a breach of contract [48].

20

Exposure to damages and financial risks if the company is sued by the host

country need to also be critically considered because they can impose strategic

risks and they have the ability to create a disruption in strategic operations10.

Other strategic risk considerations relating to legal decisions include: contingency

fees; class actions; punitive damages; corporate philosophies; contractual11 and

regulatory requirements; operating and consortium agreements; market

conditions and civil juries [10], [11].

Risks due to insurance of oil and gas installations can also be classified as

strategic, although insurance in itself is a way by which risks can be mitigated;

the risk it covers is only minimized, it does not go away [49].

Exclusions, exposure level, notice requirements, insurance policies and

underwriting capabilities might impose a certain level of uncertainty to strategic

decisions thereby, increasing the risks therein; like the Exxon Valdez oil spill and

its corresponding ongoing legal case [50].

Uncertain fluctuations in direct12 and indirect taxation laws also impose risk

considerations to offshore oil and gas projects. The net effect of taxation on a

project is an overall increase in project cost; when taxation values are unstable,

project costs would be unpredictable [49], [50]. This has a net effect of causing

spikes in the overall risk of an offshore oil and gas project.

An example is the Hamaca project in Venezuela where the offshore oil and Gas

Company had to offset increase in production tax by the release of escrowed

funds [51].

10 The ability to enforce and uphold contracts is considered the major legal risk when dealing with

international offshore oil and gas operations [9]. 11 The major contractual concerns that can pose strategic risks are: parties to the agreement,

duration of the agreement, roles of the parties, scope of the agreement, payments, warranty, liability, insurance, taxes, ownership, confidentiality, development, operations, decommissioning, force majeure, management, governance procedures, dispute resolution, mergers and acquisitions and governing law [10]. 12 Examples of direct taxes relevant to the offshore oil and gas industry are: withholding taxes,

value added taxes, export/import duties and fees, severance taxes/royalty payments, registration fees. Corresponding indirect taxes are: corporate/income taxes, taxes on employee wages, social taxes, and property/advalorem taxes [10].

21

The size and work scope of offshore oil and gas installations are directly

proportional to the risks involved in the successful execution of projects to

specified requirements within given time constraints [50], [51].

Large projects usually involves the subdivision of work packages13 between

various parties (operators and contractors) to share the risk. This risk sharing

does not necessarily reduce the overall project risk because mistakes by one of

the parties can affect the overall project leading to a larger increase in strategic

risk than if the work packages had been handled by a single entity [10].

It has been noted that risks due to project size and execution are very strategic

and are capable of causing ‘ripples’ and ‘shock waves’ which can affect projects

in the whole region; and not only offshore oil and gas projects peculiar to the

company.

An example is ‘the thunder horse effect’ in the Gulf of Mexico where project size

and execution risks caused by a single project delayed several offshore oil and

gas projects from starting by 2 to 3 years because of consequent regulations [52].

2.2 OFFSHORE RENEWABLES INDUSTRY RISKS (PESTLE

ANALYSIS)

The world has seen an exponential growth in the offshore renewables industry.

The United Kingdom has been at the forefront of Europe’s exponential growth in

renewables with new licenses awarded by the government increasing generating

capacity to 33GW by the end of 2020 [53]. However, a corresponding increase in

risks and hazards is being associated with the growth trend in the industry and

the offshore renewables industry is usually classified as a ‘high’ risk sector [53].

The PESTLE14 (political, economic, social, technical, legal and economic)

13 This subdivision of work packages amongst various specialists or multiple companies is referred

to as a joint venture or consortium in the oil and gas industry [10]. 14 The PESTLE analysis method is often used in business and financial management and it has

the advantage of focusing on the macro-environment; thus having a broader view compared to a narrow perspective of risk identification which limits to certain aspects of a project [56]. In PESTLE

22

analysis approach is widely acclaimed as the best risk identification tool for the

offshore renewables industry; because it maps individual risks to the multiple

stakeholders involved in the offshore project development stages from design

conceptualization to decommissioning [54].

OREIs consist basically of offshore wind energy, tidal energy, marine current

turbines and wave generators [55]. Offshore wind energy is more developed than

the other three offshore renewable energy forms; thus, to reduce data gaps in the

overall PESTLE analysis, knowledge of the offshore wind sector would be

transferred to the three other ‘juvenile’ offshore industries [54].

This knowledge transfer would help (to a certain extent) to have a holistic view of

the PESTLE analysis for the offshore renewables industry [55].

The PESTLE analysis thoroughly identifies all potential possible risks present in

the offshore renewables industry based on stakeholder perception. The main

steps followed in carrying out the PESTLE analysis of the offshore renewables

industry are [57]:

Generic PESTLE review of the offshore renewables industry.

Focused PESTLE analysis of the offshore renewables industry in the UK.

Risk and stakeholder identification.

Expertise characterization.

Risk and stakeholder categorization.

Proper risk identification for the offshore renewables industry involves a thorough

literature review and proper stakeholder identification for each stage of the

PESTLE analysis [57]. Overlapping of project stakeholders would occur during

the PESTLE analysis based on the relative importance and involvement of each

stakeholder in the project development process [56].

analysis, main risks alone are identified in contrast to other analysis methods; which identifies all risks (relevant or irrelevant).

23

2.2.1 POLITICAL ANALYSIS

The major political risk that the offshore renewables industry is likely to face is

the fact that most political stakeholders are likely to prefer policies that favour

already established technologies compared to relatively new technologies such

as offshore renewables [54].

International governmental organizations currently present a low political risk to

OREIs as most of their policies favour their development; the major political risks

that the offshore renewables industry can be exposed to comes from the national

scene.

The ORJIP15 highlights political risks as ‘medium’ in the offshore renewables

industry especially ‘consenting risks from the government’. The ‘consenting risks’

include: the risk of not getting consent, risk of delay in getting consent or risk of

consent with stringent conditions [58].

Politics has a huge effect on the successful development of offshore renewables

now and in the future [54]. The major political bodies presenting various risks to

the offshore renewables industry include [59], [60]: the UN, UNESCO, UN-DESA,

UNDP, IEA, IRENA, continental organizations (such as the EU, European

Commission, ACER, EU-OEA and NATO16), local energy organizations (such as

the DECC et al).

A lot of advisory boards in the UN have been set up to promote sustainable

energy development especially renewables; thus, political risks in the offshore

renewables industry would keep on reducing year after year [54]. The United

Nations Conference on Sustainable Development summit raised the political

pressure on all major stakeholders to help reduce risks in the renewables sector

[59].

15 The ORJIP is a joint industry project involving the DECC, Carbon Trust, Marine Scotland, The

Crown Estate and offshore wind developers (Centrica, DONG Energy, E.ON, EDF Energy, EDP Renewables, Eneco, Fluor, Mainstream Renewable Power, REPSOL, RWE, Scottish Power Renewables, Siemens, SSE Renewables, Statkraft, Statoil, Vattenfall) [58]. 16 The European Commission, EU-OEA and NATO set the 2020 renewables target for Europe

[61], which has made government policies favorable towards offshore renewables compared with other regions of the world such as Africa.

24

However, unfavourable fiscal policies and worsening economic times might lead

to governments on the national (or local) level facing budget costs to reduce

spending on offshore renewables development because of its capital intensive

nature [54].

Also, the internal and uncertain political structure of specific countries might

cause an enactment of policies in line with objectives which might/might not

favour offshore renewables for the period that the party is in power [54].

This has the potential to pose a very high level of uncertainty to offshore

renewables projects that have a project execution life which is more than the

tenure duration of the ‘favourable’ government.

Political fractions in the national scene also pose a high risk to offshore

renewables development [58]. For example, in the UK; England, Wales, Scotland

and Northern Ireland have different ideologies regarding the renewables industry;

leading to a sizeable level of political risk for offshore renewables projects in the

UK based on the region in which it is sited [61].

Differences in regional political support across a country also impact on

government cutbacks in renewables spending which would in turn increase the

political risk profile of offshore renewables in such regions [57].

Possible uncertain global political ‘scenarios’ and environment related politics

might cause a reduction in support that international organizations would in the

future give the renewables industry [58]. Although; this is farfetched, it is still a

probable risk.

Thus; although, political risks to the offshore renewables industry might be

minimal from international organizations, internal political tensions and varying

national/regional ideologies have a high probability of affecting the offshore

renewables industry.

25

2.2.2 ECONOMIC ANALYSIS

Developers and organizations planning to invest in the offshore renewables

industry perform a lot of economic analysis because of the huge start-up capital

required for such projects and the lengthy period required to recoup the

investment.

Financial analysis of the offshore renewables industry shows a consistently low

ROI because of the huge investment amounts required and the project complexity

which often requires multiple contractors, stakeholders and investors [62].

Most offshore renewables projects are always completed over budget [62]; this

poses a huge economic risk which reduces the willingness of investors to invest

in these projects.

Banks are also likely to refuse investing in the high risk/long term investment

option which the offshore renewables industry portends, preferring shorter

term/low risk investments; making needed investment funds very difficult for the

offshore renewables industry to harness without significant public sector

involvement.

ROI values for offshore renewables depend largely on cost and time overruns;

according to Risktec [62]: ‘for every budget overrun and project delay, the

feasibility of a favourable ROI reduces significantly’17.

This makes developers and organizations planning to invest in the offshore

renewables industry perform a lot of economic analysis because of the huge start-

up capital required for such projects and the lengthy period required to recoup

the investment.

Funding; is thus the major economic risk to be considered in offshore renewables

development [63] because offshore wind and especially tidal and wave energy

are still in the early stages of their developmental cycle.

17 A financial assessment of offshore renewables projects shows that an expected ROI of 10%

would be reduced to 8.5% if the project is over schedule by 15 months; this has a 0.5 probability of occurrence in all offshore renewables projects [62].

26

The unstable global market; inadequate correlation between public and private

sector investments; and the fragmentation of the offshore renewables industry all

pose a lot of economic risks for the industry’s future [64].

The economic risks are also influenced by the key stakeholders made up of both

the public18 and private sector (mainly banks and private companies) [57].

The government is a major player in the economic risk category because various

financial schemes (such as Feed-in-Tariffs and Renewables Obligation

Certificates) affect the economic viability of offshore renewables projects [57],

[66]. Increased competition among the various OREI types and suppliers of fossil

electricity also suggest a level of economic risks because the competition level

affects optimal energy pricing from renewables.

This is also similar to the insurance system imposing premiums to cover for

damages with premiums which might be sufficiently high to make high-risk

offshore renewable projects not competitive, except if insurance companies are

involved in the project initiation stage [67].

On the continental scale, CORDIS funds various renewable projects through

various national governmental grants [68]; and within the UK, the DECC, Carbon

Trust, ETI and Technology Strategy Board provide funding. Examples are: the

£20 million pounds MEAD scheme provided by the DECC [69]; Marine

Renewables Proving Fund provided by The Carbon Trust [70]; £1.2 million

pounds invested in Knowledge Transfer Partnerships by the Technology Strategy

Board [71]; and the £10 million pounds Saltire prize19 administered in Scotland

[72].

18 Apart from the government, relevant organizations within Europe whose decisions have an

impact on economic risks are: The Carbon Trust, CORDIS, various DOEs, DECC and the ETI [65]. 19 The Saltire prize is a £10 million pounds cash prize created to fast-track the wide scale

development of wave and tidal energy in Scotland; it is open to individuals, teams or organizations all over the world [73].

27

These public sector investments are geared towards increasing the participation

of the private sector in the investment scheme for offshore renewables thereby

reducing the corresponding economic risks through risk sharing.

Currently, the price per KWh of energy generated from offshore renewables is

more expensive than onshore renewables or other conventional sources of

energy. This leads to a risk of investment decline in offshore renewables in the

future if the price per KWh does not reduce [54].

Although, tariffs and rebates are provided by the government to offset the

investment costs; these tariffs might not be sustainable and may still present risks

for the developers because of uncertainty in pricing.

Selection of a cost-effective technology above competing conventional

technologies, governmental policies, possible stoppage of government subsidies

and the true cost of offshore renewable projects (CAPEX and OPEX) all impose

additional economic risks. An increase in the level of ROCs20 might increase the

support garnered by the offshore renewables industry over time. However, this

might not be sustainable in the long run and might be out phased if required

investors are not attracted [74].

The uncertain global energy market does not guarantee that the present growth

that the offshore renewables industry is experiencing would continue [75].

Increasing prices of oil might cause a spike in the global yearn for renewables

thereby favouring its development [76]; however, the increase in the development

of shale gas technologies which provide energy much cheaper than renewables

might reduce the growth rate that the offshore renewables industry is currently

experiencing in the long term [77]. Despite the enormous economic risks, the

offshore renewables industry has an advantage of being able to generate jobs

since it is labour intensive; thus, helping the local, regional and national economy

significantly [78].

20 A ROC is a certificate issued by the government to operators of renewable energy generating

stations for the renewable electricity generated [74].

28

2.2.3 SOCIAL ANALYSIS

The various social stakeholders of any offshore renewables project, although

mostly overlooked can present a substantial amount of risks capable of truncating

the whole project if not properly managed. An example of social risks causing

project delay is the case of Britain’s public enquiry in Powys on the deployment

of 500 MW capacity wind turbines [79].

Social upheaval from pressure groups and communities carrying out various

petitions can hinder the deployment of offshore renewables; the argument being

that OREIs can negatively impact the landscape [54], [80].

This is commonly referred to as the NIMBY (not in my backyard) syndrome

because people might be reluctant to seeing offshore renewables close to the

shorelines of their homes [81]. This might lead to OREIs being located in very

remote places (not necessarily by design) thereby, increasing installation and

construction costs.

Although, the attendant social risks might be higher for offshore wind installations

compared to tidal or wave energy generators; social acceptance is still very

important for the success of offshore renewables [82].

Developers often neglect incorporating the level of social acceptance by the ‘host

community’ into offshore renewables and this can cause a high amount of social

risks if the project has to come to a halt midway in the project implementation

phase [83]. A major challenge for offshore renewables development especially

during social analysis is the proper identification of both the ‘direct’ and ‘indirect’

stakeholders [57].

Apart from the local communities, these stakeholders span the local, national and

international terrain and include [84]: commercial shipping communities, dredging

communities, marine emergency service associations (e.g. the RNLI), local and

national coastguards, national navy services, fishing associations, national

29

support services, tourism associations21 and surfers. All these stakeholders

impose amounts of social risks to offshore renewables in varying levels.

Specific stakeholders such as fishing communities, dredging associations and

various marine navigation communities (both for business and leisure) have to

be critically considered and communicated with early enough in the offshore

renewables development stage to encourage buy-in and reduce attendant social

risks that their neglect can cause [83], [84].

Commercial and recreational shipping activities face collision risks if offshore

renewables developers do not integrate them early enough in the project planning

phase. These collision risks can be avoided if proper underwater clearance,

correct marking, lighting and adequate route maps are provided close to offshore

energy sites in accordance with the guidelines provided by the RYA; and the

Maritime and Coastguard Agency [85], [86].

Social groups, pressure groups and other relevant NGOs which are ignored might

cause the delay or stoppage of offshore energy installations leading to an

investment loss. Opposition both on the local and national levels as a result of

lack of public acceptance of the projects probably due to inadequate awareness,

sensitization or communication; can also create huge problems for the offshore


2.2.4 TECHNICAL ANALYSIS

The development of technology for adequate foundations, turbines and the best

grid system for harnessing the energy from offshore renewables present a lot of

risks. Cabling routing, design and the development of technologies to reduce

installation impacts on marine structures and activities are both pertinent and

challenging for the offshore renewables sector because of the rapidly changing

technological landscape [57].

21 OREIs attract a lot of tourists but with these installations becoming more popular; this might

decline in the long run and with it, its attendant social risks [54].

30

The resistance of OREIs to geo-hazards, scours and accretion is dependent on

cutting-edge technologies which significantly increases the total cost of offshore

renewable projects. Cabling accidents and incidents have been adjudged the

major source of insurance claims for the offshore renewables industry and lots of

research are still being done to make cabling safer [53].

Technology development takes a considerable amount of time and incorporating

this increases the overall project duration. Large turbines (5MW – 10MW) go

through the design, manufacturing and testing phase for 2 to 3 years in the field

before their installation; and vessels for transporting turbines take about 4 to 5

years from designing to construction; all these add considerably to project costs

[53].

‘Blade throw’22 and structural failure are huge risks to offshore renewables and

these operational hazards can only be minimized through technology which

translates to cost. All the technological risks can cause fatalities, delays, budget

overruns and reputation loss for the offshore renewables industry and should be

minimized as much as possible. OREIs also impact marine radar,

communications and positioning systems [55].

For offshore wind farms; spacing, water depth, seabed changes, tidal streams,

rotor effects and electrical transformer locations need to be thoroughly

considered through the adequate use of technology for optimum performance

[55]. Offshore wave and tidal installations are mostly located close to the water

surface and so are not as visible as offshore wind installations [53].

According to EMEC, the basic technologies used for offshore wave and tidal

installations include: attenuators, point absorbers, oscillating wave surge

converters, oscillating water columns, overtopping devices, submerged pressure

differentials, WECs, TECs, horizontal axis turbines, vertical axis turbines and

oscillating hydrofoils; most of these installations are not visible; thus the ‘safety

22 An increasing number of offshore wind installations has increased the proximity of their

locations to built-up areas; this has caused a lot of HSE concerns because rotor failure can lead to the wind turbine blades (‘blade throw’) detaching at a high speed which can cause harm to people on marine vessels close to the site where this occurs [87].

31

zone’ markings should be in accordance with the IALA guidelines to reduce

collision risks [88]. OREIs can also impact maritime communication especially if

the installation sites lie within VTS operational limits.

According to research conducted by the UK government [55], offshore wind farms

presently cause a negligible impact on marine radio communications such as:

radar (between 3GHz and 9GHz), VHF and GPS. However, there are rising

concerns about these risks increasing as OREIs become more popular in years

to come [89].

Offshore renewables present a source for infrastructural development through

new supply chain channels and expertise which can help the host country’s

economy [54]. The technology behind tidal and wave power generation still in the

developmental phase has the potential to attract more developers and investors

In the future if the major risks are overcome synonymous to offshore wind

technology which is more established [90].

A considerate amount of analysis must be carried out to create reliable and not

too expensive technologies which would be more economically feasible. Thus, a

balance has to be made between having the best design and having the most

cost effective design. Power density, loading, material structure analysis, failure

analysis23, reliability, performance and availability thresholds should also be

considered to suit the developed technology to the requirements of the

environment [91]. Established principles derived from offshore wind installations

(with appropriate modifications) can also be directly applied to wave and tidal

generation schemes to reduce the cost of developing new technology from the

scratch thereby incurring new risks [57].

Technical stakeholders24 should be considered in OREIs because design failure

can impact negatively on offshore renewables development. The prototype

23 Basic industry standards for failure analyses to mitigate failure risks are the: SWIFT, HAZOP,

FTA and FMECA methods [93]; new theoretical modelling techniques and computer simulation methods are being developed by the NREL to better improve performance of OREIs [94]. 24 A large proportion of the technological stakeholders are engineers and scientists in research

institutes and universities. They partner with investors and developer teams to create new (or

32

development and testing phases which occur before the installation phase are

considered very crucial [92]; failure during these phases occur often and incur

lots of cost, but reduce risks considerably during the installation stage.

Stakeholders involved in classification, certification, installation, commissioning,

grid connection and integration, maintenance and de-commissioning should also

be considered early during the project initiation stage to reduce the effect of

possible risks at the latter end of the project life [57].

Other important technical stakeholders are [57]: government organizations such

as the National Grid, Technology Strategy Board and CORDIS; classification

organizations such as Llyod’s Registers and DNV; research institutes such as the

ICEPT and UKERC; manufacturers; marine installation and commissioning

bodies; NGOs like RenewableUK; material suppliers; test site owners such as:

EMEC and Wavehub; developers and universities. These stakeholders have to

be managed effectively and their requirements met to reduce technical risks.

Draft standards and guidelines such as those provided by EMEC [95],

OffshoreGrid [96] and The Crown Estate [97] for offshore renewables energy

development especially during part/full scale prototyping, concept/tank testing,

testing and final deployment should be adhered to reduce failure risks and

improve the probability of developing an effective and reliable structure.

Cost-effective technologies for decommissioning should be considered as

important as other aspects of the project life-cycle to reduce possible cost-

overruns and technological risks due to lack of knowledge and experience. The

overall payback period and carbon footprint of the offshore renewables

installation should also be performed to help make the adequate choice of

suitable technology [97].

Other possible factors that can cause risks and hence, require technological

analyses [57] are: technology maturity level; engineering design uncertainty;

improved) efficient and ‘workable’ designs [57]. These teams also work with other stakeholders such as manufacturers, suppliers, modelling teams and simulation experts which have a role to play in the overall project life cycle.

33

supply chain reliability; power output; industry fragmentation25 impact; support,

anchoring and mooring methods; prototype restrictions; design variability; overall

system efficiency; design and certification standards; grid connection;

maintenance; and knowledge transferability.

2.2.5 LEGAL ANALYSIS

Legal requirements stipulate that the impact of OREIs must be assessed and

appropriate measures of mitigation put in place [55] in accordance with the formal

requirements of the MCA; for navigational safety, emergency response

preparedness [98], development consent and licensing26. The necessary national

and international standards must be adhered to as this hugely affects OREI

development.

In the UK, according to the Energy Act of 2004, a regulatory regime was

established for OREIs in the United Kingdom’s REZ for installation beyond

territorial waters [53]. This Act supplements the already existing regime for the

UK’s internal and territorial waters; section 99 of this Act deals with navigation

requirements for OREIs [101].

The National Policy Statement for Energy, the Planning Act 2008, the Electricity

Act 1989 [102], the National Policy Statement for Renewable Energy

Infrastructure [103], the Marine and Coastal Act 2009 [104] all constitute legal

requirements that affect offshore renewables.

Significant risks can be avoided if the legal stipulations and licensing

requirements are adhered to from the project initiation to the project

decommissioning phases.

25 The offshore renewables industry is not a cohesive whole presently because there is not yet a

widely accepted configuration; thus making the industry fragmented [57]. 26 There are different legal requirements for development consent and licensing based on the

power capacity of the OREIs; for above 100 MW [99] and 1 MW – 100 MW [100].

34

OREI sites that are under the jurisdiction of port limits or in open sea areas are

legally required to prepare scoping, EIA and ES reports in addition to compliance

with other specific criteria such as HSE requirements, safety management

systems, contingency management systems and the Port Marine Safety Code

during all phases of the project [53].

The risk of marine vessels colliding with OREIs varies with the type of offshore

renewables device as some installations are totally submerged (wave

generators), some protrude a little above sea surface (tidal generators) and some

are very visible (offshore wind farms).

Thus, legislation concerning offshore marking of OREIs vary according to the

installation type based on the IALA guidelines [95]. Legislations governing

offshore renewables installations are very complex and pose a high level of

legislative risk for developers both nationally and internationally.

Various laws affect OREIs; at an international level: the UN Convention on the

Law of the Sea27; at a continental level: the Renewable Energy Directive, the EIA

Directive (consisting partly of the Habitats Directive and the Wild Birds Directive),

the Water Framework Directive, the Marine Spatial Planning Directive and the

Marine Strategy Framework Directive [54].

The inconsistency of the law creates a high amount of risk for developers

investing in offshore renewable projects outside their ‘home country’. The fast

changing offshore energy policies also act as sources of risk for the offshore

renewables industry as developers must consistently keep in pace with the

‘volatile’ legislations.

In some countries like Nigeria, the use of water bodies for installations is

governed by the Land Use Act of 1990 [105] where all the land (and water bodies)

in the country is owned by the government and can only be leased to an

organization for a maximum period of 99 years after which it reverts to

27 The UN Convention on the Law of the Sea gives the state sovereign the absolute control over

water within 200 nautical miles of its coastline [106].

35

government’s possession. In the UK also; for OREIs, licenses have to be given

and leases approved by the Crown Estate. The grant process of these leases can

be very lengthy thereby leading to significant amount of risk of delays for offshore

renewable projects [54].

Other associated legal risks with the offshore renewables industry include [54],

[57]: commitment to legally bound targets, design information leaks to

competitors, unapproved patents, costs associated with legal battles and

licensing permits.

The major legal stakeholders that can cause these risks are [57]: government

organizations such as National Grid, DEFRA, European Commission and the UN;

licensing organizations such as Marine Licensing Scotland, Marine Licensing

Wales, MMOs; renewable energy lawyers and policy makers. These

stakeholders should be effectively managed beginning from the project initiation

stage to reduce the occurrence of legal risks.

2.2.6 ENVIRONMENTAL ANALYSIS

HSE risks, particularly environmental risks are considered the most important for

the offshore renewables industry. The ORJIP recently launched a project aimed

at interpreting and quantifying the impact of offshore wind farms on key marine

bird species [58]. The offshore renewables industry pose several risks on the

sedimentary makeup, biological constituents, fishing and navigational activities

of the environment. OREIs especially offshore wind farms are constantly criticized

for reducing the visual appeal of their surroundings [58] and for causing acoustic

disturbances [53].

Scoping, EIA, SEA and ES reports are legally required to monitor the impact of

offshore renewable projects on the environment with a view to mitigating the

attendant environmental risks [55]. Most governments especially in Europe and

emerging economies in Africa and other parts of the world are trying to produce

energy with lower GHG emissions and air pollution, offshore renewables can be

of immense help in this regard [106].

36

Although offshore renewables reduce emissions from fossil fuels, their wider

effect on the environment has to be analysed [54]. Several legislations

compelling offshore renewable energy developers to perform environmental

analyses for their proposed installations abound such as: the Environmental

Impact Assessment Directive and the Strategic Environmental Assessment

Directive stipulated by the European Commission [107].

Offshore renewable energy developers have to consider the requirements of both

the governmental and non-governmental environmental agencies in order to

reduce the environmental impact of their installations and the risk of non-

compliance. Local, national and international organizations [54], [57] such as:

EU-OEA, RAFTS, DEFRA, DECC, JNCC, CEFAS, MMO, WWF and RSPB are

environmental stakeholders for offshore renewables which formulate both

strategic and tactical policies that directly or indirectly affect the offshore


A major risk that the offshore renewables industry has on the environment is the

fact that being a ‘relatively young’ industry compared to other offshore energy

installations such as offshore oil and gas, the medium and long term

environmental impacts of OREIs are not clearly known [108].

Long term environmental studies such as [109]: accumulated collision, light

scattering, avoidance behaviour, sound disturbance, EM field scattering, sound

disturbance, water quality, artificial reef effects, aquatic archaeological damage

and benthic habitats need to be considered; however, this would require offshore

renewables to have been in existence for a long period of time.

Uncertainties surrounding the project decommissioning phase, delays caused by

environmentalists during project initiation and impacts of scaling up a single OREI

to an OREI farm also have to be considered to mitigate the environmental risks

[54], [57].

37

2.3 COMBINED RISK REGISTER

The seven risk groups (political, operational, economic, supply and demand,

health and safety, environmental and strategic) identified in section 2.1 for the

offshore oil and gas industry and the six risk groups (political, economic, social,

technical, legal and environmental) identified in section 2.2 for the offshore

renewables industry can be further broken down using a ‘top-down’ approach into

single line items of thirty risks for each industry as shown in Table 2-1.

Table 2-1 Combined risk register

S/N Offshore oil and gas industry risks Offshore renewables industry risks

1. Access to offshore reserves ‘Blade throw’ risks

2. Climate change concerns Collision risks

3. Competition for proven reserves Commissioning risks

4. Competition from offshore renewables Competition from offshore oil and gas

5. Construction risks Construction and installation risks

6. Contractual risks Contractual risks

7. Decommissioning risks De-commissioning risks

8. Environmental risks Engineering design uncertainties

9. Exploration risks Environmental risks

10. Expropriation and nationalization risks Fluctuating energy standards

11. Fluctuating fiscal terms Global energy market uncertainties

12. Geological risks Grid connection and integration risks

13. Governmental regulations Investment risks

14. Health risks Inconsistent energy policies

15. Human capital deficit Insurance risks

38

Table 2-1 (continued) Combined risk register

S/N Offshore oil and gas industry risks Offshore renewables industry risks

16. Installation risks Legal risks

17. Insurance risks Licensing risks

18. Investment risks Offshore communications interferences

19. Legal risks Political instabilities

20. New operational challenges Price fluctuations

21. Political instabilities Project approval risks

22. Price volatility Public and private sector partnership risks

23. Processing and separation risks Public disapproval of projects

24. Safety risks Reduction of subsides and tariffs

25. Supply chain glitches Regulatory compliance risks

26. Taxation risks Structural failure risks

27. Transportation risks Structural maintenance risks

28. Uncertain energy policies Supply chain fluctuations

29. Unfamiliar environment risks Taxation risks

30. Unstable market conditions Technology maturity level risks

39

3 MULTI CRITERIA DECISION MAKING

3.1 INTRODUCTION

The effective comparison of risks in the offshore oil and gas sector to those

present in the offshore renewables industry requires the consideration of different

factors to make an accurate analysis. This leads to the consideration of a multi

criteria analysis as opposed to a single decision making approach [110].

This multi criteria analysis takes into account the various seven risk groups of the

offshore oil and gas industry and the six risk groups (PESTLE) of the offshore

renewables industry previously enumerated [111].

The conventional single criteria approach makes comparison decisions based

only on cost and efficiency; this leads to a biased result caused by a neglect of

other technical and non-technical factors.

Criteria multiplicity and the ever increasing complexity of offshore energy projects

makes MCDM (and similar methods such as: MCDA, MADM28, MODM and

MCGDM) analysis pertinent for effective decision making and comparison [110].

Rational decision making in energy economics, management and planning is

pertinent to sustainability as shown in Figure 3-1. Figure 3-2 [111] shows the

MCDA process in sustainable energy decision making applications required to

simplify the complex interactions as shown in Figure 3-3 [111].

MCDA helps to eliminate uncertainty difficulties; thereby, making decision making

more adaptable and flexible to better suit the increasing complexities that energy

planning presents in real life applications and energy decisions.

28 MADM is used when the set of alternatives of a decision consists of a finite number of elements

precisely known before the solution process begins [114]. MODM is used when the number of choices for a decision is unbounded and the alternatives are not directly specified but defined as decision variables [115].

40

Figure 3-1 General MCDA process flow chart

41

Figure 3-2 MCDA process flow chart for sustainable energy applications [111]

42

Figure 3-3 Complex interactions of sustainable energy systems [111]

3.2 MULTI CRITERIA ANALYSIS

Multi criteria analysis evaluates a set of alternatives on the basis of multiple

criteria. It includes choosing the best alternatives from a set and sorting the

alternatives into a pre-ordered preference or selecting the best one with several

concerned criteria [112], [113]. The decision maker can either indicate priority

between alternatives with respect to each considered criterion or maximize a

utility function [116].

Multi criteria analysis can be applied to both engineering and non-engineering

areas such as [117]: preference modelling, knowledge discovery, HFLTSs,

HFLSs, OR and evolutionary multi-objective optimization. The combination of

fuzzy set theory29, FDM, FCM and LFDN with MCDM has led to the development

of the fuzzy MCDM theory which also has many real world practical applications

[118].

29 The uncertainty theory which is the basis of the fuzzy set theory was introduced by Bellman,

Zadeh and Zimmermann; and it is commonly used in combination with MCDM methods [119].

43

According to Carlson and Fuller [120], multi criteria analysis and by extension

MCDM can be classified into five groups which are:

The outranking approach based on the work of Bernard Roy implemented

in the ELECTREE and PROMETHEE methods.

The utility theory and value approaches started by Raiffa and Keeney

implemented in the AHP and ANP methods developed by Saaty30 [121].

The interactive MOLP approach pioneered mainly by: Yu Stanley Zionts,

Ralph Steuer and Milan Zeleny.

The group decision and negotiation theory which introduces new ways to

work explicitly with varying group dynamics, knowledge differences, value

systems and objectives.

Other MCDM methods such as the weighted sum method, weighted

product method, TOPSIS and fuzzy TOPSIS methods.

According to Yager [122], [123]; during MCA, possible kinds of relationship

among criteria can be classified as prioritizations. These types are common in

energy applications and are known as prioritized MCDM problems.

A lot of research is now focused on the feasibilities of aggregating information

with prioritizations and the construction of prioritized aggregation operators [124].

Although, in practical prioritized MCDM problems, the large number of criteria

and complex prioritizations makes calculations quite wieldy; aggregation

operators help in the simplification process.

The main constituents of a MCDM problem are criteria set: C = {𝑐1, 𝑐2, … , 𝑐𝑛}

and a set of possible alternatives: X ={𝑥1, 𝑥2, … , 𝑥𝑚} which would be evaluated

to select the best [124].

According to Bellman and Zadeh [125], a fuzzy subset of all the alternatives can

be developed from a representation of each criterion. Thus, if cj (j = 1, 2… n) is a

criterion, it can be represented as a fuzzy subset cj over X such that cj (xi) is the

30 The AHP method is the backbone of the popular MCDM software package known as Expert

Choice [119].

44

satisfaction level of xi over cj. This is done with the assumption that cj (xi) ϵ [0,1] (i

= 1,2,…,m;j = 1,2,…,n).

However, there are several interdependencies which can occur among criteria in

MCDM problems apart from prioritizations. For example, when incorporating

prioritizations into a MCDM calculation: if criterion ck is ranked prior to criterion c1

for (k,l ϵ {1,2,…,n}); it’s pertinent to note that the loss of ck cannot be made up for

by a benefit in c1.

According to Yager [123], prioritized MCDM problems can be classified as strictly

ordered prioritizations (if the prioritizations set up in the criteria are a strict linear

ordering) or weakly ordered prioritizations (if the prioritizations are resolvable by

means of an OWA operator to strictly ordered prioritizations [126]).

According to Tsoutos et al [127], the use of MCA (prioritized or not prioritized) is

justified based on four main reasons which are:

It allows for the integration and investigation of the different objectives and

interests of the multiple actors considered since quantitative and

qualitative information from every actor is considered in terms of weight

and criteria factors.

It significantly reduces the complexity of the multi actor problem by

providing an output which is easier to understand.

It is a generally accepted method of alternatives assessment and has the

flexibility of being applied to different versions developed to suit specific

problems.

It allows for inclusiveness and objectivity of varying perceptions with a

minimal use of energy and cost.

The various MCA methods also help in the facilitation, negotiation,

communication and quantification of the various priorities [128]. Based on

Kangas’ research work [129], it has been discovered that inconsistencies may

arise during MCA if preference information is processed in a way different from

other conventional MCA methods, if the criterion weights are interpreted

45

differently by the varying methods or if the problem formulations are not a

reflection of the assigned preference structures.

There are five main steps involved in MCA and MCDM which are [110]: problem

definition, generating alternatives and criteria; assigning criteria weights;

construction of the evaluation matrix; selection of the appropriate method and

ranking of the alternatives.

Major MCDM methods which can be used to compare the risks of the offshore oil

and gas industry to those of the offshore renewables industry would be reviewed.

Methods and standards applied in published papers and journals which are

relevant would also be thoroughly analysed to consider the best method which

can be used. The main methods which would be reviewed with relevant examples

are:

Analytic and fuzzy analytic hierarchy process (AHP/FAHP) methods.

Weighted sum method and weighted product method

Technique for order preference by similarity to ideal solution method

(TOPSIS)

The following MCDM methods are not analysed in depth by this thesis but

referred to:

PROMETHEE method

Elimination Et Choix Traduisant la REalite (ELimination and Choice

Expressing REality) – ELECTRE method

VIKOR method

Multi criteria data envelopment method

Fuzzy PROMETHEE

The Shapley value method

Fuzzy TOPSIS method

46

3.3 AHP

The AHP method is a MCDM method proposed by Saaty [130] in the 1970s, it

operates by decomposing a MCDM problem into several hierarchy levels; thus

forming a hierarchy with unidirectional hierarchical relationships between levels

[131]. There are four basic steps involved in AHP model development which are

[132]:

Problem identification, objective setting and criteria identification.

Hierarchy construction and decomposition of the problem into interrelated

levels.

Introduction of comparative judgments to construct comparison matrices.

Calculation of weights for each criteria, consistency index and ratio, Eigen

values, sub-criteria and alternatives for each comparison matrix to identify

priority rating.

AHP based decision analyses (FAHP, ANP inclusive) are widely used in MCDM

and MCA for energy applications because [136]:

They allow decision makers to analyse complex MCDM problems by

breaking down the main problem into more manageable sub problems.

Interdependencies among element groups, criteria and alternatives can be

managed effectively.

They provide a detailed analysis of interdependencies and priorities

between clusters’ elements resulting in a more reliable analysis and final

decision.

MCDM problems requiring pairwise comparison31 occur a lot during AHP; and

Saaty [130] developed a scale of 1-9 for use in pairwise comparison. Table 3-1

shows the various scales and their definitions as proposed by Saaty [131].

31 Pairwise comparison is the relationship between two elements represented numerically which

helps to determine which element is more important according to a higher criterion [132].

47

Table 3-1 Pairwise comparison scale [131]

Importance Scale Scale Definition

1 Equally important

2 Equally to moderately important

3 Moderately important

4 Moderately to strongly important

5 Strongly important

6 Strongly to very strongly important

7 Very strongly important

8 Very strongly to extremely important

9 Extremely important

3.3.1 AHP IN ENERGY PLANNING

AHP has been utilized a lot in energy planning. Energy resource allocation to the

household sector in India was evaluated by the use of AHP and goal

programming [137]. In Jordan, AHP was also used for the energy conservation

policy selection process and to differentiate the impact of various fuel based

power plants on life quality [138], [139].

In 2009 [140], AHP was applied for comparing various power plants available for

electricity generation from economic and technological perspectives. It has also

been used for the selection of clean coal or wind technologies for a utility

company [141].

Local, national and global energy planning stakeholders in Crete previously

applied AHP in its MCDM approach for sustainable energy system planning for

the island [142]. Various available technologies for distributed generation were

analysed to ensure a sustainable generation [143]. Likewise, the AHP method

48

was used to select an adequate financing scheme for renewable energy projects

for Cyprus [144].

Jaber et al. [145] and Chinese et al. [146] used the AHP method for analysing

space heating methods in the residential and industrial sectors respectively. In

2013, Turkey’s electricity supply chain was analysed by Bas [147] while Amer

and Daim [148], evaluated sustainable generation of electricity for Pakistan.

Most recently, in 2014 [149], Ahmad et al. applied the AHP method for selecting

the adequate renewable energy source for the development of an adequate

electricity generation system in Malaysia.

The above examples confirm that the AHP method is very reliable for making

energy planning decisions involving multi criteria especially when qualitative and

quantitative data are mixed [150].

3.3.2 AHP PROCESS OVERVIEW

The main steps to solving the energy choice MCDM problem using AHP are [136]:

1. Structuring the decision making problem as a hierarchy and breaking it

down into several levels. The seven risk groups of the offshore oil and gas

industry and the six risk groups of the offshore renewables industry are

sometimes interdependent. Thus, the problem has to be decomposed into

elements according to their common characteristics to form the

hierarchical model showing the relationships between the alternatives and

the goal criteria [110].

2. Obtaining the criteria weights by comparing the n criteria in the same level

using the scales in Table 3-1 to obtain matrix A based on the decision

makers judgments.

A = (1 ⋯ 𝑎1𝑛

⋮ ⋱ ⋮𝑎𝑛1 ⋯ 1

) where aji = 1/aij; i,j = 1, …, n

(3-1)

49

The judgment inconsistencies of the matrix are checked by the CR which is given

by:

CR = ((ƛ𝑚𝑎𝑥−𝑛)/ (𝑛−1)

𝑅𝐼) where ƛmax is the maximal eigenvalue of A

(3-2)

If CR is greater than the set threshold value, judgments from matrix A would be

reviewed but if otherwise, the judgments from the matrix are accepted [136].

According to Saaty’s proposal [131], the principal right eigenvector of matrix A is

used to calculate the local priorities vector, P.

P = (p1, p2, …, pi, …, pn) (3-3)

P is synthesized across all criteria to determine the global priority gi of all criteria

where i is from 1 to nH where nH is the number of criteria and sub criteria in the

hierarchy32.

3. Alternatives are assessed for each criterion based on the nature and

number of the alternatives.

4. Priorities of the bottom level criteria and alternatives are used to build the

decision matrix.

5. The MCDM method is used to aggregate alternative and criteria priorities;

using the weighted sum model.

Figures 3-4 and 3-5 show the process flow diagrams of the AHP method and the

ANP method (which can handle more complexities compared to the AHP)

respectively.

Appendix A outlines the basic AHP principles used in this thesis with

corresponding calculations shown in Appendix D.

32 The local and global priority of the main goal is taken as 1; the sum of the global priorities of all

the bottom level criteria should also be equal to 1 [136].

50

The application of the AHP model in this thesis would compare the seven risk

groups of the offshore oil and gas industry to the six risk groups of the offshore

renewables industry earlier enumerated. These risk groups would act as the main

criteria and the AHP model would prioritize both industries in line with set criteria.

Figure 3-4 AHP process flow diagram

51

Figure 3-5 ANP process flow diagram [136]

3.3.3 FUZZY AHP (FAHP)

Although, AHP has proven to be a reliable decision analysis technique for MCDM

problems, it cannot adequately consider uncertainty levels when evaluating

criteria and alternatives [133]. Thus, FAHP is used to solve problems requiring

uncertainty levels through the fuzzy scale by applying lower, median and upper

interval values. This makes FAHP relevant to the risk comparison of offshore oil

and gas and renewables.

FAHP was proposed in early 1980 by Van Laarhoven and Pedrycz [134]; Chang

[135] extended FAHP applications by introducing an extent analysis approach for

synthetic extent values which can be applied to interval values of pairwise

comparisons earlier shown in Table 2-1. FAHP uses the fuzzy set theory to solve

52

ambiguous and not clearly defined situations in MCDM. Vague information is

changed to useful data by applying the fuzzy set33, membership function and

fuzzy numbers [132]. The TFN is used to enumerate the vagueness of the

parameters during the alternative selection process. The major FAHP steps are

[132]:

Pairwise comparison judgments of criteria by making use of fuzzy scales.

Calculation of the value of fuzzy synthetic extent, Sk with respect to the ith

object.

Comparison of values and calculation of the degrees of possibility, V(Sj ≥

Sj ).

Calculation of the minimum degree of possibility, dj.

Normalization of dj to derive the weight vector W`.

Iteration of this process for all levels of the hierarchy structure.

3.3.4 SPECIFIC APPLICATION OF THE AHP METHOD

In this application, an AHP model would be developed to perform a risk

assessment in order to select between a proposed offshore oil and gas

installation project and an offshore renewables project. The risk register of both

industries enumerated earlier would be used for the model development. Political,

economic, social, technical, legal and environmental risks would be the criteria

considered for the offshore renewables industry; while political, operational,

economical, supply and demand, health and safety, environmental and strategic

risks would be the criteria considered for the offshore oil and gas industry. In

addition to the major criteria outlined above, relevant sub-criteria would also be

analysed as shown in Table 3-2.

33 A fuzzy set utilizes the membership function which allocates a value between 0 and 1 to each

criterion or alternative [132].

53

Table 3-2 Criteria and sub-criteria used for the AHP model

S/N Criteria Sub-criteria Description

1. Economical Technology cost (a) Total cost of equipment and installation

Operational life (b) Number of useful years before decommissioning

Resource potential (c) Offshore energy fuel availability

Feed-in tariff rate (d) Estimated amount paid to generators per kWh produced

Payback period (e) Estimated number of years to recoup investment

2. Environmental GHG emission reduction (a) Capability of the offshore energy project to reduce GHG

Environmental impact (b) Effects on surroundings, biodiversity, flora and fauna

Water requirement (c) Extent of water, average installation would cover

3. Health & Safety HSE regulations (a) Impact of constantly changing HSE laws on installations

HSE NGOs and groups (b) Probabilities of project stoppage due to pressure groups

4. Legal Legal requirements (a) Effects on offshore renewables development

Legislation changes (b) Effects on project life cycle and cost considerations

5. Operational Exploration activities (a) Impacts of increasingly difficult offshore exploration

Partnership ventures (b) Effects of alliances between IOCs for exploration

Hazards (c) Consideration of operational hazards

Human capital deficit (d) Impacts of shortages of skilled workers on operations

54

Table 3-2 (continued) Criteria and sub-criteria used for the AHP model

S/N Criteria Sub-criteria Description

Logistics (e) Effects of offshore transportation requirements

6. Political Political uncertainty (a) Political instability effects on offshore energy projects

Nationalization (b) Impacts of expropriation and forceful nationalization

Government policies (c) Effects of stringent government regulations

7. Social Public approval (a) Public acceptance for a certain kind of technology

Job creation (b) Potential employment opportunities to be created

8. Supply/Demand Demand fluctuations (a) Effects of supply and demand variations

Market conditions (b) Impacts of unstable market scenarios

Price volatility (c) Cost hikes and price considerations

9. Strategic Insurance (a) Impacts of disasters and risks causing huge damages

Taxation (b) Impacts of tax hikes and claims

10. Technical Lead time (a) Elapsed time from construction to operation

Efficiency (b) Comparing efficiencies of various alternatives

Maturity (c) Technology with initial faults reduced and in the market

55

The AHP model to be developed would have four levels, the first level is the goal

of the AHP model followed by the criteria and sub-criteria outlined in Table 3-2 in

levels two and three.

The two offshore energy sources to be considered make up level four which are

named the alternatives; Figure 3-6 shows the structure of this hierarchical AHP

model based on the nomenclature of the criteria and sub-criteria given previously

in Table 3-2.

The pairwise matrix A is constructed using quantitative data (universal technology

performance data) from available literature as shown in Tables 3-3 to 3-6.

Average values are considered but this has been proven to have a small impact

on the AHP analysis [149].

Figure 3-6 Proposed hierarchical AHP model

56

Table 3-3 considers investment cost and not production cost because production

cost varies significantly from country to country compared to investment cost.

Table 3-4 compares the life-cycle CO2 emissions between the offshore energy

types. Table 3-5 gives the average operational life and construction times while

Table 3-6 estimates the number of jobs created, efficiency and land requirements.

Table 3-3 Average investment costs of offshore energy technologies [149], [151]

Type of Offshore Energy Investment Cost (£/kW)

Renewables 1180 – 2950

Oil and Gas 800 – 1450

Table 3-4 Average operational life and construction time for offshore energy

technologies [149], [152] and [153]

Type of Offshore

Energy

Operational Life

(years)

Construction Time

(years)

Renewables 40 – 50 1 – 2

Oil and Gas 25 – 30 1 – 3

Table 3-5 Life cycle CO2 emissions of offshore energy technologies [149], [154]

Type of Offshore Energy Emission (g-CO2/kW)

Renewables 9.7 – 123.7

Oil and Gas 755.4 – 5569.7

57

Table 3-6 Efficiency, land requirements and job potential for offshore energy

technologies [149], [155]

Type of

Offshore

Energy

Efficiency (%) Water

Requirement

(km2/1000MW)

Jobs Creation

(employees/500MW)

Renewables 40 – 70 100 – 300 5635 – 8521

Oil and Gas 50 – 85 250 – 500 7327 – 10754

Due to the difficulty in selecting an optimal option, this AHP model used 30 sub-

criteria in 10 categories to assess the alternatives as shown in Figure 3-3. A

pairwise comparison model would be developed for each level of the model, the

elements of the matrix would signify the numerical element of each matrix in

relation to the other in comparison.

The qualitative information in Tables 3-3 to 3-6 would be transformed into

numerical values using the pairwise comparison scale shown in Table 3-1. The

normalized principal eigenvector (priority vector34) for each matrix would be

computed. Equations (3-1) and (3-2) would be used to check the consistency of

the comparison as outlined in Section 3.3.2.

Once the priority weights are obtained from the various model levels, the priority

ranking for the various alternatives would be established by integrating the

judgment results to give the final matrix shown in Equation (3-4).

[𝑃𝑊𝐴𝑅𝐶] x [𝑃𝑊𝐶𝑅𝐺] = [𝑂𝑂𝐺𝑃 𝑂𝑅𝑃] (3-4)

Where:

PWARC = Priority weight of alternatives with respect to criteria

PWCRG = Priority weight of criteria with respect to goal

34 A priority vector shows the importance of each element in relation to its parent level [149].

58

OOGP = Offshore oil and gas project

ORP = offshore renewables project

Thus, elements of pairwise comparison matrix A and Anorm can be populated as:

A =

[ 1 2

1

52 1

1

31 5 3

1

21

21 1 7 7 7 5 7 7 6

5 1 1 6 7 7 5 7 8 61

2

1

7

1

61 2 1

1

32 2 1

11

7

1

7

1

21

1

3

1

41 3

1

3

31

7

1

71 3 1

1

33 4 2

11

5

1

53 4 3 1 4 5 1

1

5

1

7

1

7

1

21

1

3

1

41 2

1

41

3

1

7

1

8

1

2

1

3

1

4

1

5

1

21

1

5

21

6

1

61 3

1

21 4 5 1]

The weight for each criteria is obtained by dividing each entry in column i of A by

the sum of entries in column i to yield the matrix Anorm which would be used to

evaluate matrix W. W would be used to compute the value of maximum Eigen

value ƛmax as shown in Equation (3-5).

ƛmax = 1

𝑛 ∑

𝑖𝑡ℎ 𝑒𝑛𝑡𝑟𝑦 𝑖𝑛 𝐴𝑊𝑇

𝑖𝑡ℎ 𝑒𝑛𝑡𝑟𝑦 𝑖𝑛 𝑊𝑇𝑛𝑖=1

(3-5)

Anorm =

[ 0.07 0.39 0.06 0.09 0.03 0.01 0.07 0.14 0.075 0.030.03 0.20 0.32 0.31 0.24 0.34 0.35 0.20 0.175 0.330.34 0.20 0.32 0.27 0.24 0.34 0.35 0.20 0.200 0.330.03 0.03 0.05 0.04 0.07 0.05 0.02 0.06 0.050 0.050.07 0.03 0.05 0.02 0.03 0.01 0.02 0.03 0.075 0.020.21 0.03 0.05 0.04 0.10 0.05 0.02 0.09 0.100 0.110.07 0.04 0.06 0.13 0.14 0.14 0.07 0.12 0.125 0.050.01 0.03 0.05 0.02 0.03 0.01 0.02 0.03 0.050 0.010.02 0.03 0.06 0.02 0.01 0.01 0.01 0.01 0.025 0.010.14 0.03 0.05 0.04 0.10 0.02 0.07 0.12 0.125 0.05]

W = [0.0965 0.2465 0.2760 0.0450 0.0355 0.0800 0.0945 0.0260 0.2050 0.0745]

59

WT =

[ 0.09650.24650.27600.04500.03550.08000.09450.02600.20500.0745]

and AWT =

[ 0.15320.27460.61980.04980.05030.27080.09420.03670.03500.1403]

ƛmax = 1

10 [

0.1532

0.0965 +

0.2746

0.2465 +

0.6198

0.2760 +

0.0498

0.0450 +

0.0503

0.0355 +

0.2708

0.0800 +

0.0942

0.0945 +

0.0367

0.0260

+ 0.0350

0.2050 +

0.1403

0.0745]

ƛmax = 15.317

Using Equation 3-2 to calculate CR and using RI value of 1.49 as given in

Appendix A and the mathematical model developed as shown in Appendix D:

CR = ((15.317−10)/ (10−1)

1.49) = 0.396

The value of CR is not small enough to make the decision analysis perfectly

consistent [149]; and the ratio 𝐶𝑅

𝑅𝐼 > 0.10. Thus, inferences deduced using the AHP

method for this specific example have the probability of a high level of inaccuracy.

3.4 WEIGHTED SUM AND WEIGHTED PRODUCT METHODS

Weighted sum and weighted product methods are MADM information elementary

methods used in MCDA. They require the decision maker’s criteria preference as

core inputs [156].

Other examples of elementary weighting methods are [156]: dominance,

maximin, maximax, conjunctive, disjunctive and lexicographic weighting

methods; elimination by aspects; linear assignments; weighted sum and

weighted product methods.

60

3.4.1 WEIGHTED SUM METHOD (WSM)

A lot of decision making regarding sustainable energy systems and the selection

of requisite energy choices use the WSM especially in single dimensional

problems [157]. The score given to any alternative is computed as [156]:

Si =∑ 𝑤𝑗𝑛𝑗=1 𝑥𝑖𝑗, i = 1, 2… m (3-6)

Where:

m = the number of alternatives which are possible.

n = the number of criteria.

xij = value of the ith alternative in terms of the jth criterion.

wj = weight of importance of the jth criterion.

After computation, cardinal scores are used to choose and rank each considered

alternative; with the best alternative being the one with the maximum score (max

Si). The WSM gives inconsistent results when applied to multi-dimensional

decision making problems, where different units are combined and the additive

utility assumption is violated [158] leading to significant errors.

3.4.2 WEIGHTED PRODUCT METHOD (WPM)

The WPM process is very similar to that of the WSM, the major difference

between both methods is the fact that multiplication is used in WPM instead of

addition used in the WSM. Alternatives are compared to each other by multiplying

specific numbers of ratios for each criterion and each ratio is raised to the

exponent of the relative weight of each criterion [157]. The score given to any

alternative is computed as [156]:

Si =∏ 𝑥𝑖𝑗𝑤𝑗𝑛

𝑗=1 , i = 1, 2… m (3-7)

Where:

61

m = the number of alternatives which are possible.

n = the number of criteria.

xij = value of the ith alternative in terms of the jth criterion.

wj = weight of importance of the jth criterion.

Similar to the WSM, the alternative with the highest score is chosen as the best

scheme which is synonymous to the alternative which is equal to or better than

the other alternatives [158]. Ratings used are recommended to be greater than 1

and there is no upper bound for alternative scores derived from the WPM [159].

Comparison between an alternative score and the standard score is done as

shown in Equation (3-8) to check the viability of the results similar to the 𝐶𝑅

𝑅𝐼 ratio

used in the AHP method.

Ri = 𝑆𝑖

𝑆∗ = ∏ 𝑥𝑖𝑗

𝑤𝑗𝑛𝑗=1

∏ (𝑥𝑗∗)

𝑤𝑗𝑛𝑗=1

, i = 1, 2… m

(3-8)

Where the best performance for criteria j is 𝑥𝑗∗ and the preference for alternative

i increases as Ri tends to 1 (thus, alternative Si is much more preferred to

alternative S*).

3.4.3 SPECIFIC APPLICATION OF THE WSM AND WPM

The WSM would be developed to select between an offshore oil and gas and

offshore renewables project similar to Section 3.3.4. The criteria which would be

used to evaluate the alternatives are: economic (investment cost), social (jobs

creation), technical (construction time), environmental (life cycle CO2 emissions),

operational (efficiency) and strategic risks (operational life). Table 3-7 shows a

comparison of the numerical values of each criteria for each offshore energy

source which would serve as the evaluation matrix. Equation (3.5) would be used

to calculate the WSM scores for each alternative.

62

Table 3-7 WSM and WPM evaluation matrix [149], [150], [151], [152], [153], [154], [155]

Offshore Energy (Alternative)

Investment Cost (£/kW)

Efficiency (%)

Emission

(g-CO2/kW)

Operational Life (years)

Jobs Creation (employees/500MW)

Construction Time (years)

Renewables (A1) 1180 - 2950 40 - 70 9.7 – 123.7 40 - 50 5635 – 8521 1 - 2

Oil and Gas (A2) 800 - 1450 50 - 85 755.4 – 5569.7 25 - 30 7327 – 10754 1 - 3

Table 3-8 Weight of importance (wj) of considered criteria

Criteria Weight of importance (wj)

Investment Cost (£/kW) 5

Efficiency (%) 5

Emission (g-CO2/kW) 8

Operational Life (years) 4

Jobs Creation (employees/500MW) 4

Construction Time (years) 5

63

The weights of importance (wj) of each criteria are derived based on the survey

conducted and information from Saaty’s pairwise comparison scale (range of

values between 1 and 9) used earlier for AHP calculations; this is shown in Table

3-8. The information given in Table 3-7 contains a range of values, thus; the

arithmetic mean would be used in the calculations.

Si =∑ 𝑤𝑗6𝑗=1 𝑥𝑖𝑗, i = 1, 2

(3-9)

Thus, using Equation (3.6), for offshore renewables; alternative A1:

S1 = (5 x 2065) + (5 x 55) + (8 x 66.7) + (4 x 45) + (4 x 7078) + (5 x 1.5)

S1 = 39700.6

Similarly, calculating the value of S2 for offshore oil and gas; alternative A2:

S2 = (5 x 1125) + (5 x 67.5) + (8 x 3162.55) + (4 x 27.5) + (4 x 9040.5) + (5 x 2)

S2 = 67544.9

Comparing the values of S1 and S2, max (S1, S2) = S2; thus; alternative A2

(offshore oil and gas) is the better ranked alternative according to the WSM.

Similar to WSM calculations, the WPM would be used to rank the alternatives

following the same set of data and criteria given in Tables 3-7 and 3-8; Equations

(3-10) and (3-11) would be applied.

Equation (3-11) would be used to compare offshore oil and gas installations

(alternative A1) with offshore renewables installations (alternative A2); if the value

of Ri is greater than one, then the results of the WPM would be consistent with

those obtained from the WSM.

Si =∏ 𝑥𝑖𝑗𝑤𝑗6

𝑗=1 , i = 1, 2 (3-0-10)

Ri = 𝑆2

𝑆1 =

∏ 𝑥2𝑗𝑤𝑗6

𝑗=1

∏ 𝑥1𝑗𝑤𝑗6

𝑗=1

, i = 1, 2 (3-11)

64

Ri = (11255) 𝑥 (67.55) 𝑥 (3162.558) 𝑥 (27.54) 𝑥 (9040.54) 𝑥 (25)

(20655) 𝑥 (555) 𝑥 (66.78) 𝑥 (454) 𝑥 (70784) 𝑥 (1.55)

Ri = 3.089 𝑥 (1075)

5.786 𝑥 (1062) = 5.339 x 1012

The value of Ri >>>1, thus; alternative A2 (offshore oil and gas) is the better ranked

alternative, which is synonymous to results from the WSM.

3.4.4 WSM AND WPM IN ENERGY PLANNING

The weighted sum method has been applied to decision making in several

sustainable energy applications especially by Jovanovic et al [160], Liposcak et

al [161] and Pilavachi et al [162]. The weighted product method has not been

severally used in comparison to the weighted sum method because of the

drawback which occurs with the computation of extremes which are from the

average using the WPM formula.

3.5 TOPSIS

3.5.1 INTRODUCTION

The TOPSIS method is used both as a weighing method and as a MCDA method

and it was developed by Hwang and Yoon [163] to use a finite number of criteria

to select the best alternative.

The TOPSIS principle works on the fact that the alternative with all the worst

criteria value is the negative ideal while the alternative with the best level for all

criteria is the ideal alternative [164].

Mathematically, this translates to the fact that the best alternative is closest to the

positive ideal solution while being farthest from the negative solution. Each

criteria is treated as uniformly increasing or decreasing making problem solution

easier [163].

65

Major advantages of the TOPSIS method include the facts that: it ranks

alternatives cardinally, it is not dependent on attribute preferences although it

makes full use of attribute information [165].

Euclidean distances are used in getting the preference order of alternatives from

the m number of alternatives and n number of criteria [165]. The weighted

decision matrix is constructed from normalized values and followed by the

positive and negative ideal solutions.

BCR analysis implies that when cost ratios are considered the decision maker

wants the minimum values amongst the alternatives and the maximum values

amongst the alternatives for benefit ratios [163].

Conventionally, when the benefit criteria is maximized and the cost criteria is

minimized, the positive ideal solution results; and if the otherwise happens, the

negative ideal solution results [166].

After the calculation of the relative closeness to the ideal solution and the

implementation of the separation measure, the best alternative can be selected

by the decision maker in accordance with the problem requirements [163], [165].

Equations (3-12), (3-13) and (3-14) gives the positive distance, negative distance

and relative closeness degree expressions respectively used in TOPSIS

analysis. The alternative with the shortest distance to the ideal solution and the

highest maximum degree is selected as the best alternative [164].

Si+ = √∑ (𝑥𝑖𝑗

𝑛𝑗=1 − 𝑥𝑗

+)2 (3-12)

Si- = √∑ (𝑥𝑖𝑗

𝑛𝑗=1 − 𝑥𝑗

−)2 (3-13)

Ri = 𝑠𝑖−

𝑠𝑖−+ 𝑠𝑖

+ (3-10-2)

Where:

Si+ = Positive distance between positive ideal solution A+ and alternative Ai

66

Si- = Negative distance between negative ideal solution A- and alternative Ai

xj+ = jth criteria’s performance of positive ideal solution A+

xj- = jth criteria’s performance of negative ideal solution A-

Ri = Relative closeness degree of Ai and A+

3.5.2 TOPSIS PROCESS OVERVIEW

According to Hwang and Yoon [163], the main steps for implementing the

TOPSIS framework in MCDM applications as shown in Figure 3-7 include:

Formation and normalization of an initial decision matrix

Building the weighted normalized decision matrix

Determination of A+ and A- values

Calculation of the separation measures for each alternative

Computing of the Ri values

Ranking of the alternatives according to the descending order of Ri

Apart from the process flow diagram being simple, the TOPSIS method considers

information simultaneously from both the positive and negative ideal solutions.

Thus, the TOPSIS method would be used in collaboration with the FMEA method

to analyse survey data from the combined risk register developed from both the

offshore oil and gas and renewables industries because TOPSIS is capable of

evaluating many practical issues [166].

The major disadvantage of the TOPSIS method is that information overlap can

sometimes occur as a result of indices correlation which Euclidean distances

cannot account for in the TOPSIS process flow [164], [165].

Thus, during index screening; the index independence is increased and the

indicator correlation is reduced by qualitative analysis making the overall process

fairly subjective but still within a high margin of decision making accuracy [166].

67

Figure 3-7 TOPSIS methodology stepwise analysis [163]

The TOPSIS methodology shown is further analysed in the steps below [110]:

Step 1: The m alternatives and n criteria are used to formulate a decision matrix

with the requisite normalized value rij calculated as:

rij = 𝑓𝑖𝑗

√∑ 𝑓𝑖𝑗2𝑚

𝑗=1

(3-15)

Where:

rij = Normalized value

fij = ith criterion function value for the alternative Aj (j = 1, …, m; i = 1,…, n)

Step 2: vij which is the weighted normalized value is calculated as:

vij = wirij (3-16)

Where:

68

vij = weighted normalized value

wi = weight of i criterion and ∑ 𝑤𝑖𝑛𝑖=1 = 1

Step 3: The positive ideal solution A+ and the negative ideal solution A- is

calculated as35:

A+ = {v1+,…,vn

+} = {(maxj vij | i ϵ I`), (minj vij | i ϵ I``)} (3-17)

A- = {v1-,…,vn

-} = {(minj vij | i ϵ I`), (maxj vij | i ϵ I``)} (3-18)

Where:

I` = Benefit criteria values

I`` = Cost criteria values

Step 4: The separation of each alternative from the positive ideal solution (Dj+)

and the negative ideal solution (Dj+) respectively using n-dimensional Euclidean

distance can be calculated as:

Dj+ = √∑ (𝑣𝑖𝑗−

𝑛𝑖=1 𝑣𝑖

+)2 (3-19)

Dj- = √∑ (𝑣𝑖𝑗−

𝑛𝑖=1 𝑣𝑖

−)2 (3-20)

Step 5: The relative closeness (Cj+) of the positive ideal solution A+ to the

alternatives aj is calculated as:

Cj+ =

𝐷𝑗−

(𝐷𝑗++ 𝐷𝑗

−)

(3-21)

The alternatives are then ranked and the values of Cj+ are sorted in descending

order. The alternative which has the maximum measure is best ranked and is

proposed as the solution for the MCDM problem.

35 Conventionally, considering cost criteria, the decision maker uses minimum values among

alternatives; considering benefit criteria, the decision maker uses maximum values among alternatives [110].

69

3.5.3 SPECIFIC APPLICATION OF THE TOPSIS METHOD

The risk register developed in Section 2.3, Table 2-1 would initially be analysed

by an online survey tool using the FMEA method explained in Chapter 4. Results

from this survey would be subsequently analysed using the TOPSIS method in

line with the methodology step-wise analysis in Figure 3-8. The TOPSIS method

would rank the alternatives in accordance with Equations (3-15) to (3-21) as

further explained in Chapter 5.

The results would give the highest ranking risks in both offshore energy industries

and would form the second stage of the risk prioritization process for both the

offshore oil and gas and offshore renewables industries. All TOPSIS calculations

are shown in Appendix D.

70

4 FAILURE AND EFFECT MODE ANALYSIS (FMEA)

4.1 INTRODUCTION

According to the FMEA Pocket handbook: ‘FMEA analyses and ranks risks

associated with various processes systematically including their existing and

potential failure modes; prioritizes the risks based on ranking results, processes

the highest ranked risks, evaluates and re-evaluates the risks, and loops through

the prioritization process until marginal returns are achieved’ [168]. It is a

systematic tool which identifies, prevents, eliminates and controls potential errors

in a project [169]; thereby reducing overall cost. Reliability is enhanced by

recognizing likely failures before they occur using FMEA [170], [171].

The overall FMEA process which includes failure finding, prioritization and

minimization makes it applicable to a wide range of processes including energy

analysis; to aid in the application of adequate corrective and preventive

maintenance actions [172]. FMEA manages the documentation and

implementation of ‘error causing scenarios’ [173].

Based on the analysis conducted in previous sections, the various risk groups in

the offshore oil and gas and offshore renewables industries as summarized in

Table 2-1, Section 2.3 would be ranked using the FMEA method through an

online survey which would be constructed using the Survey Monkey electronic

questionnaire tool shown in Appendix C. This would enable the evaluation of

possible effects of failure modes based on the ranking of risks to facilitate further

analysis using the TOPSIS method.

4.2 FMEA PROCESS MECHANISMS

The major advantage of the FMEA method is its ‘action’ not ‘reaction’ approach

to dealing with failure [175]. This reduces the amount of money spent on resolving

damages because failure is prevented; thus, FMEA is performed before the

project design stage because costs due to damages are much higher if

71

discovered after the project design stage. The major process mechanisms

involved in FMEA are [175]:

Concise identification of possible risks and errors in a process and

possible outcomes resulting from such errors if they occur.

Identification and determination of activities which can cause a reduction

in the requisite probabilities of occurrence of such potential errors.

These process mechanisms can be broken down into a cycle of FMEA

implementation activities as shown in Figure 4-1. The main tasks of FMEA

analysis used in this thesis are information collection (on risk causing activities

and processes) of offshore renewables and offshore oil and gas installations

which have been done in preceding sections.

This also includes the formation of a combined risk register (Table 2-1). In

performing the risk assessment, adequate attention would be paid to

requirements, regulations, operation standards and documents governing the

afore-mentioned offshore energy industries. The severity, occurrence and

detectability rates would then assigned to each risk to calculate the RPN. Severity

of the risk considers its overall effect; occurrence deals with the frequency at

which a potential error would occur; detectability is a qualitative assessment that

identifies the cause mechanism of the risk event [175] as shown in Appendix B.

4.3 RISK PRIORITY NUMBER (RPN)

The basic FMEA procedure which requires reviewing design details, illustration

of equipment block diagrams and recognizing of potential failures lead to the

classification of possible causes and effects to the related failure modes. Thus,

priority ranking is achieved based on the assignment of a requisite RPN which is

defined as [171]:

RPN = S x O x D (4-1)

Where: S = Severity; O = Frequency of occurrence and D = Detectability

72

Figure 4-1 FMEA implementation cycle

The RPN values would vary between 1 and 1000 leading to the prioritization of

the risks according to their RPN values. The computation of RPNs allows focus

on risk events with high RPNs based on their higher priority ranking compared to

other risks.

Events with high RPN values are thus classified as ‘high risk events’ and would

be further analysed using TOPSIS. The RPN is basically used as a metric for

classifying risks as ‘acceptable’ or ‘unacceptable’.

After the computation of the RPN, the main corrective actions which can be

carried out to reduce failure include [175]:

Elimination of the base of risk causes

Reduction of error severity

Increase in the probability of detection

73

The values of S, O and D as given in Equation (4-1) which would be used for

FMEA in this thesis would be ranked on a scale of 1 to 10. Based on Towler and

Sinnot, [176], Tables 4-1, 4-2 and 4-3 give the qualitative description of the

severity, occurrence and detectability scales used for the online risk assessment

survey of the combined risk register in Table 2-1. Figure 4-2 shows the FMEA

process flow chart which would be used for the initial analysis of the results from

the online survey before the TOPSIS stage.

Table 4-1 Severity rating scale for online survey FMEA

Severity rank Description

1-2 The risk occurring would result in a very minor impact

3-5 The risk occurring would result in slight deterioration

6-7 The risk occurring would result in medium scale deterioration

8-9 The risk occurring would result in a high degree of deterioration

10 The risk occurring would result in a major damage

Table 4-2 Occurrence rating scale for online survey FMEA

Occurrence rank Description

1 An unlikely probability of occurrence (< 0.001)

2-3 A remote probability of occurrence (0.001 < probability < 0.01)

4-6 An occasional probability of occurrence (0.01 < probability < 0.10)

7-9 An occasional probability of occurrence (0.10 < probability < 0.20)

10 A high probability of occurrence (0.20 < probability)

74

Table 4-3 Detectability rating scale for online survey FMEA

Rank Description

1-2 Very high probability that the risk would be detected before it occurs

3-4 High probability that the risk would be detected before it occurs

5-7 Moderate probability that the risk would be detected before it occurs

8-9 Low probability that the risk would be detected before it occurs

10 Very low (or zero) probability that the risk would be detected before it occurs

Figure 4-2 FMEA process flow chart based on survey results

75

4.4 MAJOR SHORTCOMINGS OF THE FMEA METHOD

The RPN method has proved to be very useful but has been reviewed to have

some major weaknesses when used for FMEA in real world applications. This

has led to the development of various modifications of the traditional RPN method

to make it more applicable to real world problems when FMEA or FMECA is

applied [177]. Due to the possible uncertainties existing in the FMEA evaluation

process, RPNs representing ‘crisp numbers’ which are used in conventional

FMEA methods are being replaced with linear programming methods, fuzzy and

weighted RPNs [176]. This thesis takes into account the major shortcomings of

the RPN method during the FMEA, however; the errors which these shortcomings

might present to the overall data analysis results are considered negligible.

According to Lui et al [177] and Gilchrist [178], the major shortcomings of the

FMEA cum RPN approaches are:

Only three risk factors mainly in terms of safety are considered when the

RPN method is used.

The RPN elements have a lot of duplicate numbers.

RPN calculations are highly sensitive to variations in risk factor

evaluations.

Various interdependencies occur among failure modes and effects and

these are not taken into account by the RPN.

RPNs are not continuous values and thus, have many ‘holes’.

The effect of corrective actions cannot be measured by RPNs.

Score conversion is different for each of the three risk factors considered

in FMEA.

A lot of controversy abounds concerning the mathematical formula for

performing RPN calculations.

Evaluation of the three risk factors cannot be carried out precisely.

Equal values of RPNs can occur from different combinations of

occurrence, severity and detectability; but with totally different risk

implications.

The relative importance among occurrence, severity and detectability is

not taken into account; this might not be true for ‘real-world’ applications.

76

5 OVERALL RISK PRIORITIZATION

5.1 METHODOLOGY

The combined risk register in Table 2-1 was developed into two electronic

surveys with the aid of the Survey Monkey online analysis tool. Tables 4-1, 4-2

and 4-3 were incorporated as weighting scales (from 1 - 10) for the respondents

to use. The survey links were mailed to a sum total of eight respondents

comprising of: industry professionals and doctoral researchers from Cranfield

University with research areas in the offshore oil and gas and offshore

renewables industries respectively to assign weights to each risk based on their

probability of occurrence (O), severity level (S) and detectability (D). Average

values of all the responses were taken to calculate the RPN of each risk in both

industries based on Equation (3-21). The screen shots of the electronic surveys

are shown in Appendix C and the corresponding URL for each survey is:

Offshore oil and gas: www.surveymonkey.com/s/67R528V

Offshore renewables: www.surveymonkey.com/s/ZGFPTL7

The responses give a fair analysis of the weight of each risk in correlation to its

use for FMEA. Although this electronic survey method might have some slight

deviations with regard to accuracy based on personal thoughts and previous

research experiences of the respondents, the use of the average factor across

all respondents would reduce any inaccuracies that might result from these

deviations to a very negligible value. The ten highest risks based on the values

of the RPN for each industry were further analysed using the TOPSIS method

explained in Section 3.5 to get the final ranking. Appendix D shows the excel

spreadsheets for all calculations. The application of the TOPSIS method to further

rank the risks gives a higher level of credence to the ranking from the FMEA

(based on the RPNs) because of its more exhaustive comparison process. Figure

5-1 shows the process work flow chart. Tables 5-1 and 5-2 show the calculation

results including the corresponding ranks based on average RPN values from

Survey Monkey for the thirty risks analysed for the offshore oil and gas and

offshore renewables industries respectively. Figures 5-2 to 5-5 give the graphical

illustration of the FMEA survey results.

http://www.surveymonkey.com/s/67R528V

http://www.surveymonkey.com/s/ZGFPTL7

77

Figure 5-1 Methodology work flow chart

78

Table 5-1 Offshore oil and gas industry RPN calculations

Rank Risks Frequency of occurrence (O) Severity (S) Detectability (D) RPN (O x S x D)

28 Access to offshore reserves 7.00 7.67 2.00 107.38

30 Climate change concerns 4.33 6.33 2.67 73.181763

29 Competition for proven reserves 6.00 6.33 2.67 101.4066

21 Competition from offshore renewables 8.00 7.00 3.00 168

16 Construction risks 6.33 8.00 4.00 202.56

26 Contractual risks 6.00 6.33 3.67 139.3866

20 Decommissioning risks 6.67 5.67 4.67 176.614263

4 Environmental risks 9.00 8.67 4.33 337.8699

1 Exploration risks 7.67 8.00 6.67 409.2712

10 Expropriation and nationalization risks 7.00 4.67 7.00 228.83

8 Fluctuating fiscal terms 7.33 5.00 7.33 268.6445

9 Geological risks 6.33 7.00 5.67 251.2377

19 Governmental regulations 5.67 6.00 5.67 192.8934

17 Health risks 5.33 6.67 5.67 201.574737

22 Human capital deficit 7.00 7.00 3.33 163.17

79

Table 5-1 (continued) Offshore oil and gas industry RPN calculations


15 Installation risks 7.00 8.00 4.00 224

27 Insurance risks 5.00 6.00 4.33 129.9

14 Investment risks 6.00 8.00 4.67 224.16

25 Legal risks 4.67 5.33 6.00 149.3466

12 New operational challenges 6.67 6.33 5.33 225.038463

6 Political instabilities 5.67 6.67 8.00 302.5512

7 Price volatility 7.00 5.33 8.00 298.48

23 Processing and separation risks 5.00 5.50 5.67 155.925

5 Safety risks 8.67 8.33 4.67 337.272537

13 Supply chain glitches 5.33 6.67 6.33 225.038463

24 Taxation risks 5.00 5.00 6.00 150

18 Transportation risks 6.33 6.33 5.00 200.3445

3 Uncertain energy policies 6.67 7.00 8.33 388.9277

11 Unfamiliar environment risks 4.67 6.33 7.67 226.733637

2 Unstable market conditions 7.33 6.67 8.00 391.1288

80

Table 5-2 Offshore renewables industry RPN calculations


17 ‘Blade throw’ risks 3.50 8.50 4.00 119.00

21 Collision risks 5.00 6.00 3.00 90.00

22 Commissioning risks 5.50 6.50 2.50 89.38

26 Competition from offshore oil and gas 4.00 4.50 4.00 72.00

14 Construction and installation risks 6.00 5.50 5.00 165.00

18 Contractual risks 5.50 6.50 3.00 107.25

10 De-commissioning risks 6.50 4.00 7.50 195.00

20 Engineering design uncertainties 4.50 4.50 4.50 91.13

7 Environmental risks 6.50 6.00 5.50 214.50

2 Fluctuating energy standards 6.50 8.00 8.50 442.00

3 Global energy market uncertainties 7.50 7.50 7.50 421.88

16 Grid connection and integration risks 5.00 6.00 5.00 150.00

8 Investment risks 6.00 7.50 4.50 202.50

1 Inconsistent energy policies 7.00 8.00 8.00 448.00

28 Insurance risks 4.00 5.00 3.00 60.00

81

Table 5-2 (continued) Offshore renewables industry RPN calculations


23 Legal risks 5.50 4.00 3.50 77.00

23 Licensing risks 5.50 4.00 3.50 77.00

30 Offshore communications interferences 3.00 4.50 3.00 40.50

12 Political instabilities 3.50 8.00 6.50 182.00

4 Price fluctuations 7.50 7.00 7.00 367.50

19 Project approval risks 5.50 4.00 4.50 99.00

23 Public and private sector partnership risks 5.50 3.50 4.00 77.00

15 Public disapproval of projects 4.00 8.50 4.50 153.00

5 Reduction of subsides and tariffs 6+.50 8.50 5.00 276.25

10 Regulatory compliance risks 6.50 6.00 5.00 195.00

9 Structural failure risks 5.50 8.00 4.50 198.00

13 Structural maintenance risks 5.50 8.00 4.00 176.00

6 Supply chain fluctuations 6.00 6.50 6.00 234.00

27 Taxation risks 4.00 5.00 3.50 70.00

29 Technology maturity level risks 4.50 3.50 3.00 47.25

82

Figure 5-2 Offshore oil and gas FMEA survey outcome

Figure 5-3 Offshore oil and gas risk prioritization

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83

Figure 5-4 Offshore renewables FMEA survey outcome

Figure 5-5 Offshore renewables risk prioritization

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84

5.2 FIRST STAGE RISK PRIORITIZATION

The first stage risk prioritization was done by ranking each of the individual risks

based on the values of their RPNs. High RPN values are synonymous to either

high values of probability of occurrence (O), severity level (S) and detectability

(D) or a combination of both or all three factors. This means that such risks are

prone to occur and such scenarios can be classified as high risk scenarios.

These risks should be mitigated and monitored more often in accordance with

industry standards compared to the other twenty medium risk scenarios and

low risk scenarios which have much lower RPN values.

Medium risk scenarios and low risk scenarios in both industries can be monitored

periodically to check for increase in values of probability of occurrence (O),

severity level (S) and detectability (D) or a combination of both or all three factors.

Based on industry benchmarks set, such risks can be escalated into high risk

scenarios and more thoroughly analysed. Thus, the process of risk analysis

would be cyclical and dynamic in both industries and not static.

Tables 5-1 and 5-2 show the rank numbers of each of the thirty risks considered

for both the offshore oil and gas and offshore renewables industries as calculated

using FMEA principles. The top ten risks which would be further analysed using

the TOPSIS method are shown in bold font in the afore-mentioned tables.

Appendix D shows the requisite mathematical models and calculation

spreadsheets.

Tables 5-3 and 5-4 show the low risk scenarios for the offshore oil and gas and

offshore renewables industries respectively, their corresponding RPNs and rank

values. These risks are low risk scenarios which both industries can tolerate

initially if they do not escalate over time into medium risk or high risk scenarios.

Tables 5-5 and 5-6 show the medium risk scenarios based on the RPN values

which need more monitoring compared to the low risk scenarios and is proposed

to have a lower industry tolerability margin compared to the low risk scenarios.

Tables 5-7 and 5-8 show the high risk scenarios for both industries, RPN values

and ranks which would be used for the second stage risk prioritization process

which would be more detailed in accordance with the TOPSIS methodology.

85

Table 5-3 Offshore oil and gas low risk scenarios

S/N Risk Rank RPN

1 Competition from offshore renewables 21 168.00

2 Human capital deficit 22 163.17

3 Processing and separation risks 23 155.925

4 Taxation risks 24 150.00

5 Legal risks 25 149.3466

6 Contractual risks 26 139.3866

7 Insurance risks 27 129.90

8 Access to offshore reserves 28 107.38

9 Competition for proven reserves 29 101.4066

10 Climate change concerns 30 73.181763

Table 5-4 Offshore renewables low risk scenarios

S/N Risk Rank RPN

1 Collision risks 21 90.00

2 Commissioning risks 22 89.38

3 Legal risks 23 77.00

4 Licensing risks 23 77.00

5 Public and private sector partnership risks 23 77.00

6 Competition from offshore oil and gas 26 72.00

7 Taxation risks 27 70.00


9 Technology maturity level risks 29 47.25

10 Offshore communications interference 30 40.50

86

Table 5-5 Offshore oil and gas medium risk scenarios

S/N Risk Rank RPN

1 Unfamiliar environment risks 11 226.7336

2 New operational challenges 12 225.0385

3 Supply chain glitches 13 225.0384


5 Installation risks 15 224.00

6 Construction risks 16 202.56

7 Health risks 17 201.5747

8 Transportation risks 18 200.3445

9 Governmental regulations 19 192.8934

10 Decommissioning risks 20 176.61426

Table 5-6 Offshore renewables medium risk scenarios

S/N Risk Rank RPN

1 Collision risks 11 90.00

2 Political instabilities 12 182.00

3 Structural maintenance risks 13 176.00

4 Construction and installation risks 14 165.00

5 Public disapproval of projects 15 153.00

6 Grid connection and integration risks 16 150.00

7 ‘Blade throw risks’ 17 119.00

8 Contractual risks 18 107.25

9 Project approval risks 19 99.00

10 Offshore communications interference 20 40.50

87

Table 5-7 Offshore oil and gas high risk scenarios

S/N Risk Rank RPN

1 Exploration risks 1 409.2712

2 Unstable market conditions 2 391.1288

3 Uncertain energy policies 3 388.9277

4 Environmental risks 4 337.8699

5 Safety risks 5 337.2725

6 Political instabilities 6 302.5512

7 Price volatility 7 298.48

8 Fluctuating fiscal terms 8 268.6445

9 Geological risks 9 251.2377

10 Expropriation and nationalization risks 10 228.83

Table 5-8 Offshore renewables high risk scenarios

S/N Risk Rank RPN

1 Inconsistent energy policies 1 448.00

2 Fluctuating energy standards 2 442.00

3 Global energy market uncertainties 3 421.83

4 Price fluctuations 4 367.50

5 Reduction of subsidies and tariffs 5 276.25

6 Supply chain fluctuations 6 234.00

7 Environmental risks 7 214.50

8 Investment risks 8 202.50

9 Structural failure risks 9 198.00

10 Regulatory compliance risks 10 195.00

11 Decommissioning risks 10 195.00

88

5.3 SECOND STAGE RISK PRIORITIZATION

The second stage risk prioritization would be carried out on the ten high risk

scenarios outlined in Tables 5-7 and 5-8 using the TOPSIS methodology earlier

outlined in Section 3.5. The ten risks would be further accessed using six different

criteria which are:

Probability of occurrence due to normal operating conditions (PONOC)

Probability of occurrence due to extreme operating conditions (POEOC)

Short term implications (STI)

Long term implications (LTI)

Direct cost of impact (DCI)

Indirect cost of impact (ICI)

These six criteria were selected based on an extensive review of literature [160],

[161], [164] and [167]. Each criterion also relates to the RPN parameters which

are: probability of occurrence (O), severity level (S) and detectability (D).

1. Probability of occurrence due to normal operating conditions (PONOC)

and that due to extreme operating conditions (POEOC) have a direct

relation to the probability of occurrence (O) parameter used in RPN

calculations. According to Mahdi et al [175], energy systems which are

operated within the bounds of usage specification have a lower probability

of failing, with major failure modes being cyclic failure. However, if systems

are operated in extreme conditions outside the bounds of the required

usage specification, there is a higher probability of failure. Also, according

to Gilchrist [178], approximately 75% of system failures occur due to ‘life

span malfunctions’ during normal operations while the rest can be traced

to negligence and operations in extreme conditions. Thus, the TOPSIS

weights (WPONOC and WPOEOC) used for calculations as shown in Appendix

D can be derived as:

WPONOC = 75% x 0.2 = 0.15 (5-1)

WPOEOC = 25% x 0.2 = 0.05 (5-2)

89

2. Short term implications (STI) and long term implications (LTI) affect the

values of severity (S). According to Wang et al [156]; long and short term

implications of energy project failures can be used as direct measures for

assessing the overall extent of damage. The overall effect of risks on

projects can be considered to be more harmful in the long term than in the

short term due to the rippling effects of failures and associated costs. Thus,

it can be postulated that LTI weights are a little less than twice STI weights

based on the electronic survey responses shown in Appendix C and

relevant literature [164], [175] and [177]. Thus, the TOPSIS weights (WSTI

and WLTI) used for calculations in Appendix D can be derived as:

WLTI ≅ 1.6667WSTI (5-3)

WLTI = 0.4

1.6667= 0.25

(5-4)

WSTI =0.25

1.6667 = 0.15

(5-5)

3. Direct costs of impact (DCI) and indirect costs of impact (ICI) affect both

severity (S) and detectability (D). The higher the direct and indirect costs

of failure, the higher the level of severity. Also, the detectability of a risk

can be indirectly traced to the anticipated cost effect (both direct and

indirect) of the risk occurring. However, for the TOPSIS analysis in this

thesis; DCI and ICI criteria would be accounted for in terms of D. Similar

to STI and LTI weights; and using the same hypothetical reasoning based

on survey responses, literature [161], [162], [165] and the FMEA British

Standard shown in Appendix B; the TOPSIS weights (WDCI and WICI) used

for calculations in Appendix D can be derived as:

WICI ≅ 1.6667WDCI (5-6)

WICI = 0.4

1.6667= 0.25

(5-7)

WDCI =0.25

1.6667 = 0.15

(5-8)

90

Table 5-9 shows the summary of the TOPSIS weights used for each of the six

criteria in the second stage risk criteria. Tables 5-10 and 5-11 give the results of

the TOPSIS ranking using the afore-mentioned criteria for both the offshore oil

and gas and offshore renewables industries respectively as obtained from the

TOPSIS mathematical model shown in Appendix D.

Table 5-9 Weights for TOPSIS criteria

S/N TOPSIS Criterion Weight RPN Parameter

1 Probability of occurrence due to normal operating

conditions (PONOC)

0.15 Occurrence (O)

2 Probability of occurrence due to extreme operating

conditions (POEOC)

0.05 Occurrence (O)

3 Short term implications (STI) 0.15 Severity (S)

4 Long term implications (LTI) 0.25 Severity (S)

5 Direct cost of impact (DCI) 0.15 Detectability (D)

6 Indirect cost of impact (ICI) 0.25 Detectability (D)

Table 5-10 Offshore oil and gas TOPSIS rank values

S/N Top Ranked Risks di+ di- ci Rank

1 Exploration risks 0.118625 0.210827 0.639932 1

2 Unstable market conditions 0.214611 0.317901 0.596984 2

3 Uncertain energy policies 0.200955 0.113927 0.361808 8

4 Environmental risks 0.199612 0.071746 0.264397 9

5 Safety risks 0.190577 0.060305 0.240371 10

6 Political instabilities 0.149868 0.110305 0.423968 6

91

Table 5-10 (continued) Offshore oil and gas TOPSIS rank values


7 Price volatility 0.152252 0.101231 0.399361 7

8 Fluctuating fiscal terms 0.158588 0.121201 0.433188 5

9 Geological risks 0.151608 0.12661 0.455074 4

10 Expropriation and nationalization risks 0.14777 0.150066 0.503855 3

Table 5-11 Offshore renewables TOPSIS rank values


1 Inconsistent energy policies 0.09825 0.18544 0.65367 1

2 Fluctuating energy standards 0.183783 0.059235 0.243748 10

3 Global energy market uncertainties 0.159409 0.113827 0.416587 7

4 Price fluctuations 0.176075 0.062527 0.262055 9

5 Reduction of subsidies and tariffs 0.133892 0.092793 0.409348 8

6 Supply chain fluctuations 0.093025 0.132891 0.588231 2

7 Environmental risks 0.095567 0.13254 0.581044 3

8 Investment risks 0.124634 0.136728 0.523136 4

9 Structural failure risks 0.124555 0.13124 0.513067 6

10 Regulatory compliance risks 0.128933 0.138217 0.517375 5

Where:

di+ = Distance from ideal scenario; di- = Distance from worst scenario and ci =

relative closeness to ideal scenario.

92

6 RESULTS AND DISCUSSION

6.1 OFFSHORE OIL AND GAS INSTALLATIONS

The results of the first (FMEA) and second (TOPSIS) risk prioritization stages as

shown in Table 6-1 show results that give varying risk positions to specific risks

based on the TOPSIS weights and criteria used for the second stage

prioritization. Exploration risks and unstable market conditions consistently rank

high on both analyses in the first and second ranks respectively while political

instabilities and price volatility retain their sixth and seventh rank positions

respectively. The rank deviation for each risk is also calculated as:

Rank Deviation = |FMEA Rank – TOPSIS Rank| (6-1)

Table 6-1 FMEA/TOPSIS offshore oil and gas risk rank comparison

Risk FMEA Rank TOPSIS Rank Deviation

Exploration risks 1 1 0

Unstable market conditions 2 2 0

Uncertain energy policies 3 8 5

Environmental risks 4 9 5

Safety risks 5 10 5

Political instabilities 6 6 0

Price volatility 7 7 0

Fluctuating fiscal terms 8 5 3

Geological risks 9 4 5

Expropriation and nationalization risks 10 3 7

93

According to an OTC offshore oil and gas sector strategy report [27], and relevant

literature [30], [36]; safety and environmental risks are regarded as the highest

risks of the offshore oil and gas sector due to the possible fatalities they present

if such risks do occur. However it can be argued, based on inferred results from

this thesis that exploration risks can be more critical and are more prone to occur

in the offshore oil and gas industry than safety and environmental risks.

The reason for this is because in recent years, to reduce accidents in the offshore

energy sector; and with the efforts of various international organisations and

proliferation of various ‘green’ NGOs, very stringent health and safety procedures

have been put in place in the offshore oil and gas industry which has drastically

reduced both the frequency of occurrence and severity of such accidents.

Although, major incidents such as large scale oil spills and platform fires still occur

in the offshore oil and gas industry which cause large publicity in the press,

exploration risks are ubiquitous and occur during day-to-day operations.

Exploration risks includes: geological risks, equipment related, process related

and well-related hazards which all have multiple impacts on the environment,

overall safety and financial returns. These exploration risks are confronted mostly

‘internally’ by the oil and gas companies and although they are seldom made

public like health and safety concerns; they can have far more reaching

consequences causing ripple effects which can affect the safety of the

environment and personnel at large in both the short and long term.

Unstable market conditions portend a high level of risk to the offshore oil and gas

industry as seen in Table 6-1 and corroborated by the 2013 World Energy Outlook

[47] and the Wall Street Journal [35], [45]. Due to the increasing awareness of

key stakeholders in the offshore oil and gas industry regarding the propensity of

‘cleaner’ sources of energy; market scenarios and forces of supply and demand

are difficult to project for long periods of time. As a result energy markets may

face ‘downturns’ or ‘gluts’; thus, energy demands are met based on short and

medium term bases which pose a high level of risk for the industry. Fluctuating

fiscal terms which are associated with unstable market conditions also have a

parallel role in dictating the purchasing power of clients for offshore oil and gas.

94

Expropriation and nationalization risks have not been considered as a high risk

scenario for offshore oil and gas industries in recent times. However, with an

increasing scramble for energy by emerging economies in the world to make

them more relevant in the ‘energy power balance’; foreign oil companies

operating within the domestic boundaries of other countries might increasingly

face this risk. The Hull doctrine which is the international standard for

expropriation and nationalization might need to be enforced to stem this tide.

Political instabilities increase price volatility for offshore energy. According to the

Energy Studies Review [17]; the politics and the economics of oil and gas is

considered almost as important as the day-to-day operations in oil and gas

companies. Although political instabilities and price volatility are consistently

ranked sixth and seventh respectively in both FMEA and TOPSIS rankings as

shown in Table 6-1; realistically they should be ranked in the top three range.

This is because, the more politically unstable a region is, the more difficult

operations would be and the more insecure offshore oil and gas installations

would become. Essential materials needed for operations would become difficult

to harness thereby driving up the cost of production and increasing the overall

price of offshore energy. Uncertain energy policies are also indirectly related to

political instabilities, as government policies would ultimately affect offshore oil

and gas operations and production cycles.

Health, safety and environment considerations have been consistently ranked as

the highest risks in the offshore oil and gas industry by major journals and

publications [21], [30], [36], and [38]. The findings of this thesis also consider

them as high risk scenarios but not necessarily as the highest ranked risks due

to reasons afore-mentioned; however, the fact that health, safety and

environment considerations would remain pertinent to the offshore oil and gas

sector cannot be ruled out.

Thus, from above considerations, exploration risks, unstable market conditions,

uncertain energy policies, environmental risks, safety risks, political instabilities,

price volatility, fluctuating fiscal terms and expropriation risks are key in the

offshore oil and gas industry and should be mitigated by the stakeholders.

95

6.2 OFFSHORE RENEWABLES INSTALLATIONS

Similar to the offshore oil and gas industry, risk prioritization outcomes for both

the first stage (FMEA) and second stage (TOPSIS) are correlated as shown in

Table 6-2. Rank deviations are also calculated using Equation (6-1) to assign a

corresponding numerical value to each of the risk ranks.

Inconsistent energy policies recur both in FMEA and TOPSIS characterisations

as the most important risk to be considered in the offshore renewables industry.

This is to be expected because according to publications by the UN and IRENA

[59], [60]; the surge in development and growth that the offshore renewables

industry has seen is largely due to governmental interventions in the form of

sustainable energy legislations, feed-in-tariffs and subsides.

Table 6-2 FMEA/TOPSIS offshore renewables risk rank comparison

Risk FMEA Rank TOPSIS Rank Deviation

Inconsistent energy policies 1 1 0

Fluctuating energy standards 2 10 8

Global energy market uncertainties 3 7 4

Price fluctuations 4 9 5

Reduction of subsidies and tariffs 5 8 3

Supply chain fluctuations 6 2 4

Environmental risks 7 3 4

Investment risks 8 4 4

Structural failure risks 9 6 3

Regulatory compliance risks 10 5 5

96

The risk that inconsistent energy policies place on offshore renewables industries

basically border on the fact that economic imbalances and uncertain socio-

political landscapes might cause a reduction in the investments due to the

offshore renewables industry. The increase in production of shale gas, hydrates

and other ‘friendly fossil fuels’ in the nearest future might tend to cause a shift in

focus from offshore renewables due to the availability of a cheaper option and a

faster ROI period that these other fuels present compared to offshore

renewables. Fluctuating energy standards and regulatory compliance risks are

also directly related to inconsistent energy policies and are ranked as high risk

scenarios in the tenth and fifth positions respectively in Table 6-2.

According to a risk management assessment of the offshore wind energy industry

by Risktec [62] and a stakeholder survey of the tidal industry by Tyndall centre

[63]; supply chain fluctuations, price fluctuations and investment risks are

identified as problematic areas which are currently reducing the growth

capabilities of the offshore renewables industry. This is synonymous to the

classification of these risks in Table 6-2 as high risk scenarios with rank values

of two and four respectively.

The possible reduction of subsidies and tariffs is directly related to both

investment risks and inconsistent energy policies afore-mentioned. Although,

ranked eight by the TOPSIS classification, realistically it should be among the top

three risks considered because of the various ‘spill over’ effects that it can cause

if the risk occurs. Also, emerging economies which are still relatively new to the

offshore renewables industry compared to their counterparts in the Western world

might not have the financial leverage to commit a large amount of money to scale

down the cost per kWh of energy from offshore renewables like the West.

According to publications by the DECC, CORDIS and Carbon Trust; structural

failure risks and environmental risks remain a huge source of concern for the

offshore renewables industry [65], [67] and [68]. Table 6-2 classifies them as high

risk scenarios with TOPSIS rank values of six and three respectively.

Thus, the afore-mentioned risks should be critically analysed by the offshore

renewables industry with the aim of developing adequate mitigating factors.

97

6.3 COMPARATIVE RISK ANALYSIS

The high risk scenarios shown in Tables 6-1 and 6-2 show some overlapping

risks in both offshore energy industries. These risks include: fluctuating energy

standards and policies, price volatility and fluctuations, environmental risks and

market uncertainties. The FMEA and TOPSIS risk prioritizations do not include

health and safety risks in the high risk scenarios for offshore renewables because

they have a lesser impact compared to oil and gas.

Figure 6-1 shows the overlapping risks where concerted efforts in both offshore

oil and gas and offshore renewables industries can help mitigate these risks by

adequate information and resource sharing. This can help to reduce costs

associated with risks in both industries since there are multiple converging points.

Figure 6-1 Overlapping risks in offshore energy industries

98

7 CONCLUSIONS AND RECOMMENDATIONS

7.1 CONCLUSIONS

This thesis has done an exhaustive analysis of all the risks in the offshore oil and

gas and offshore renewables industries. A risk register of thirty risks in both

offshore industries was created after risk analysis and assessed based on

responses from an electronic survey using the FMEA and TOPSIS methods.

The results from the risk assessment carried out identifies the high risk scenarios

in the offshore oil and gas and offshore renewables industries with four significant

risks overlapping in both industries. These overlapping risks as shown in Figure

6-1 which are market uncertainties, fluctuating policies, environmental risks and

price volatility are important to both industries.

Based on economic imbalances in the throes of rising population figures and

attendant increase in energy demands; it is pertinent for offshore energy

industries to project into the future and find ways of risk mitigation through risk

avoidance, risk transference or other methods.

The application of similar protocols in the offshore oil and gas sector to the

offshore renewables sector has already proved in recent years to significantly

reduce supply chain and technology costs for offshore renewables development.

This approach can also be used to eliminate or reduce the effects of risks in both

industries as the future of energy demand would always be on the rise

irrespective of which source of energy becomes the lead during these times.

Medium risk scenarios and low risk scenarios in both industries identified in this

thesis should be constantly monitored periodically during project initiation,

planning, execution, monitoring and closing stages. This is because of the

dynamic nature of socio-political factors and techno-economic inputs which affect

these risks. Despite, the attendant risks of the offshore oil and gas and offshore

renewables industries, their immense contribution to solving the world’s energy

challenges has been huge. This trend is bound to increase as the world seeks to

balance energy costs against safe and reliable energy sources. Hence,

application of the findings of this thesis is key to the growth of these industries.

99

7.2 RECOMMENDATIONS FOR FURTHER WORK

The results from this thesis can be developed and built on for further research on

risks in the offshore oil and gas and offshore renewables industries. The

extensive literature review of this thesis which identified all the key risks in both

industries can be further assessed by the use of other MCDM methods such as

fuzzy TOPSIS and fuzzy AHP. This would be an interesting area of study because

the incorporation of fuzzy dimensions into risk analysis might give a more

accurate scenario compared to deterministic values such as the FMEA and

TOPSIS methods used in this thesis.

The assessment of risk dependencies and interrelations in offshore energy

systems can also be done based on the findings in these thesis. The various risk

scenarios can be studied and based on survey responses, interrelations amongst

them can be found. The findings from this survey can help in solving risk problems

and scenarios faster by tackling ‘parent risk factors’ thereby saving cost and time.

The scope of this thesis covers risk identification, analysis, ranking, prioritization

and assessment. It answers the questions: What are the risks? What are their

effects? Which risks are the most important? It doesn’t answer an important

question which is: How can we deal with these risks?

Thus, the results of the risk prioritization in this thesis can be scaled further into

further research on how offshore oil and gas and offshore renewables industries

can mitigate the risks identified either through risk avoidance or risk transference.

Risk acceptance thresholds for the identified low risk scenarios can also be done

based on RPN values or other risk analysis methods.

101

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APPENDICES

Appendix A PAIRWISE COMPARISON MATRIX

A.1 AHP MODELLING

The AHP model used in this thesis attempts to strike a balance between

practicability and robustness by evaluating requisite criterion weightings; while

incorporating both subjective and objective data into the hierarchical framework.

The AHP model developed in Figure 3-6 was based on the original three level

hierarchy model shown in Figure A1-1 [1] and the scale shown in Table A1-1 [2].

Figure A1-1 Typical AHP hierarchy [1]

Table A1-1 Fundamental ratio scale in pairwise comparison [2]

124

A.2 PAIRWISE MATRIX CONSTRUCTION

This thesis uses pairwise comparisons to find the relative importance of the

criteria in relation to achieving the goal. The information from the pairwise

comparison is used to construct the matrix as shown in Section 3.3.4. According

to Saaty [2], n (n – 1)/2 judgements are used to evaluate n criteria weightings.

Thus, for criteria (C1, C2, C3… Cn) with relative importance weights given as w1,

w2, w3… wn, the matrix is constructed from the formula given in Equation (A2-1).

(A2-1)

If there are three criteria A, B, and C for a decision D. The pairwise comparison

begins by answering the question ‘with respect to decision D: what is the

importance of criterion A over B?’ Then, ‘with respect to decision D: what is the

importance of criterion A over B?’ and ‘with respect to the decision D: what is the

importance of criterion A over B?’ The ratio scale in Table A1-1 is then used to

assess the results of the questions.

Thus, if criterion A is much more important than B with respect to decision D,

criterion A is give a value of 9. If criterion C is a little more important than criterion

B, a value of 3 is assigned to C. This is continued until all criteria have been

compared, Figure A1-1 is used to facilitate this comparison. After the pairwise

judgments, a pairwise comparison matrix shown in Table A2-2 is formed. Table

A2-1 [3] is used in the calculation of RI values based on the value of n.

Table A2-1 RI values [3]

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

RI 0 0 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 1.51 1.54

125

Table A2-2 The pairwise comparison matrix

Table A2-3 [4] gives the refined values of RI based on geometric values and not

based on Saaty’s original AHP research work which was used in this thesis.

Table A2-3 Modified RI values [4]

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

RI 0 0 0.52 0.87 1.08 1.22 1.32 1.39 1.44 1.48 1.57 1.59

126

Appendix B FMEA STANDARD (BS EN 60812:2006)

B.1 SCOPE

The British Standard (BS EN 60812:2006) describes FMEA and FMECA and how

they can be applied by [5]:

Providing the necessary steps required for a FMEA/FMECA analysis.

Identifying appropriate failure modes, terms, assumptions and measures.

Defining basic principles.

B.2 DEFINITIONS

1. Item: Any equipment, system, functional unit, subsystem, part, component

or device that can be considered individually for FMEA analysis. It may

consist of a process, hardware, software or people. A number of items may

also be considered as a collective sum and called an item.

2. Failure: The termination of an item’s ability in performing a function.

3. Fault: An item’s state characterised by its inability in performing a stated

function; excluding such inability during maintenance, planned actions or

lack of external resources. A fault can often be the result of an item’s failure

but may exist without initial prior failure.

4. Failure effect: The consequence of a failure mode in relation to operation,

status or function of such an item.

5. Failure mode: The manner in which an item fails.

6. Failure criticality: The combination of an effect’s severity and its

frequency of occurrence or other failure attributes as a measure of the

need for mitigation such occurrences.

7. System: A set of interacting or interrelated elements. A system has

defined purposes, required functions and stated operating conditions.

8. Failure severity: The level of significance of the failure mode’s effect on

an item’s operation, on the item operator or on an item’s surrounding in

relation to previously defined boundaries of the system.

127

B.3 OBJECTIVES OF FMEA

According to the British Standard (BS EN 60812:2006) [5], the major objectives

of performing FMEA in line with the purpose used for this thesis are:

To identify failures which have an unwanted effect on system operations.

To satisfy contractual requirements including stipulated standards.

To allow general improvement of the system’s reliability and safety.

To allow improvement of the system’s maintainability and reduce failure.

To perform a comprehensive evaluation and identification of unwanted

effects within defined system boundaries at various system levels.

To determine the criticality or priority for the mitigation of each failure mode

with respect to the system’s performance and impact on the process.

To classify identified failure modes according to relevant characteristics

such as detection, testability, repair and maintenance.

To identify system functional failures and the estimation of severity

measures and failure probabilities.

To develop and design improvement plans to mitigate failure modes.

To support the development of a maintenance plan to reduce failure.

B.4 FMEA STANDARD PROCEDURE

There are four main stages which the FMEA procedure consists of [5]:

1. Establishment of the basic ground rules for FMEA including scheduling

and planning to ensure adequate analysis is done.

2. Executing the FMEA using appropriate worksheets, logic diagrams and

fault trees during analysis.

3. Summarizing and reporting of the analysis to include recommendations.

4. Updating of the FMEA to reflect development activity changes.

Figure B4-1 [5] shows the relationship between failure effects and failure modes

in a system hierarchy used for this thesis in accordance with British Standards.

128

Figure B4-1 Relationship between failure modes and failure effects [5]

Failure modes are identified based on the use of the system, system element(s)

involved, mode of operation, operational specifications, time constraints,

environmental stresses and operational stress. Table B4-1 gives an example of

a set of general failure modes used in this thesis.

Table B4-1 Example set of general failure modes [5]

129

B.5 SEVERITY, OCCURRENCE AND DETECTABILITY

Severity assesses the significance of the failure mode’s effect on an item’s

operation. Table B5-1 [5] illustrates the severity classification used for end effects

in the British Standards. The classification of severity effects depends on the

FMEA application and also on several factors such as [5]:

The system nature in relation to effects on the environment and users if

failure occurs.

The process or system’s functional performance.

The customer’s contractual and legal requirements.

The safety stipulations and laws of the government.

Warranty requirements.

Table B5-1 Severity classification for end effects [5]

The probability of occurrence of the failure modes depend on four major factors

which are [5]:

1. Data derived from the life testing of the component.

2. Databases of various failure rates.

3. Data derived from field failure.

4. Data derived from failure of the component class and similar items.

The flow chart in Figure B5-1 [5] and matrix in Figure B5-1 [5] show the FMEA

analysis process and criticality matrix used in this thesis respectively.

130

Figure B5-1 FMEA analysis flow chart [5]

Figure B5-2 FMEA criticality matrix [5]

131

The event frequencies and allocated severities are used to plot the criticality

matrix shown in Figure B5-2. Due to the subjective nature of risk acceptability,

the risk acceptability classes from British Standards shown in the modified

criticality matrix Table B5-2 [5] were used in this thesis and the corresponding

failure mode evaluation criteria in Table B5-3 [5] applied as shown in the

calculations in Appendix D.

Table B5-2 Modified criticality matrix

Table B5-3 Failure mode evaluation criteria

132

Appendix C FMEA ELECTRONIC SURVEY SCREENSHOTS

C.1 RISK ASSESSMENT OF OFFSHORE OIL AND GAS INSTALLATIONS

Figure C1-1 Electronic survey – Screenshot 1

133


134


135


136


137


138

C.2 RISK ASSESSMENT OF THE OFFSHORE RENEWABLES INDUSTRY


139


140


141


142


143


144

Appendix D EXCEL SPREADSHEETS

D.1 AHP CALCULATIONS

CRITERIA (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) TOTAL

ECONOMICAL (1) 1 2 0.2 2 1 0.333333333 1 5 3 0.5 16.033333

ENVIRONMENTAL (2) 0.5 1 1 7 7 7 5 7 7 6 48.5

HEALTH AND SAFETY (3) 5 1 1 6 7 7 5 7 8 6 53

LEGAL (4) 0.5 0.142857143 0.166666667 1 2 1 0.3333333 2 2 1 10.142857

OPERATIONAL (5) 1 0.142857143 0.142857143 0.5 1 0.333333333 0.25 1 3 0.333333333 7.702381

POLITICAL (6) 3 0.142857143 0.142857143 1 3 1 0.3333333 3 4 2 17.619048

SOCIAL (7) 1 0.2 0.2 3 4 3 1 4 5 1 22.4

SUPPLY/DEMAND (8) 0.2 0.142857143 0.142857143 0.5 1 0.333333333 0.25 1 2 0.25 5.8190476

STRATEGIC (9) 0.333333333 0.142857143 0.125 0.5 0.333333333 0.25 0.2 0.5 1 0.2 3.5845238

TECHNICAL (10) 2 0.166666667 0.166666667 1 3 0.5 1 4 5 1 17.833333

TOTAL 14.53333333 5.080952381 3.286904762 22.5 29.33333333 20.75 14.366667 34.5 40 18.28333333

Table D1-1 AHP mathematical model – Spreadsheet 1

145


(1) (2) (3) (4) (5) (6) SOCIAL (7) (8) (9) (10) TOTAL AVERAGE

0.068807339 0.393626992 0.060847519 0.088889 0.034090909 0.016064257 0.0696056 0.144927536 0.075 0.027347311 0.9792063 0.0979206

0.03440367 0.196813496 0.304237595 0.311111 0.238636364 0.337349398 0.3480278 0.202898551 0.175 0.32816773 2.4766458 0.2476646

0.344036697 0.196813496 0.304237595 0.266667 0.238636364 0.337349398 0.3480278 0.202898551 0.2 0.32816773 2.7668343 0.2766834

0.03440367 0.028116214 0.050706266 0.044444 0.068181818 0.048192771 0.0232019 0.057971014 0.05 0.054694622 0.4599127 0.0459913

0.068807339 0.028116214 0.043462514 0.022222 0.034090909 0.016064257 0.0174014 0.028985507 0.075 0.018231541 0.3523819 0.0352382

0.206422018 0.028116214 0.043462514 0.044444 0.102272727 0.048192771 0.0232019 0.086956522 0.1 0.109389243 0.7924583 0.0792458

0.068807339 0.039362699 0.060847519 0.133333 0.136363636 0.144578313 0.0696056 0.115942029 0.125 0.054694622 0.9485351 0.0948535

0.013761468 0.028116214 0.043462514 0.022222 0.034090909 0.016064257 0.0174014 0.028985507 0.05 0.013673655 0.2677781 0.0267778

0.02293578 0.028116214 0.038029699 0.022222 0.011363636 0.012048193 0.0139211 0.014492754 0.025 0.010938924 0.1990685 0.0199069

0.137614679 0.032802249 0.050706266 0.044444 0.102272727 0.024096386 0.0696056 0.115942029 0.125 0.054694622 0.757179 0.0757179

1 1 1 1 1 1 1 1 1 1

146


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) TOTAL CONSISTENCY MEASURE AVERAGE

0.068807339 0.393626992 0.060847519 0.088889 0.034090909 0.016064257 0.0696056 0.144927536 0.075 0.027347311 0.9792063 11.52509468 0.0979206

0.03440367 0.196813496 0.304237595 0.311111 0.238636364 0.337349398 0.3480278 0.202898551 0.175 0.32816773 2.4766458 11.91935875 0.2476646

0.344036697 0.196813496 0.304237595 0.266667 0.238636364 0.337349398 0.3480278 0.202898551 0.2 0.32816773 2.7668343 12.16755665 0.2766834

0.03440367 0.028116214 0.050706266 0.044444 0.068181818 0.048192771 0.0232019 0.057971014 0.05 0.054694622 0.4599127 11.45594475 0.0459913

0.068807339 0.028116214 0.043462514 0.022222 0.034090909 0.016064257 0.0174014 0.028985507 0.075 0.018231541 0.3523819 11.15061944 0.0352382

0.206422018 0.028116214 0.043462514 0.044444 0.102272727 0.048192771 0.0232019 0.086956522 0.1 0.109389243 0.7924583 11.89507253 0.0792458

0.068807339 0.039362699 0.060847519 0.133333 0.136363636 0.144578313 0.0696056 0.115942029 0.125 0.054694622 0.9485351 11.5617362 0.0948535

0.013761468 0.028116214 0.043462514 0.022222 0.034090909 0.016064257 0.0174014 0.028985507 0.05 0.013673655 0.2677781 10.76915609 0.0267778

0.02293578 0.028116214 0.038029699 0.022222 0.011363636 0.012048193 0.0139211 0.014492754 0.025 0.010938924 0.1990685 11.28101377 0.0199069

0.137614679 0.032802249 0.050706266 0.044444 0.102272727 0.024096386 0.0696056 0.115942029 0.125 0.054694622 0.757179 11.24936521 0.0757179

1 1 1 1 1 1 1 1 1 1

0.590777778 CI =0.5908

1.49 RI =1.49

0.396495153 CR =0.397

147

D.2 WSM AND WPM CALCULATIONS

OFFSHORE RENEWABLES

CRITERIA LOWER RANGE VALUE UPPER RANGE VALUE AVERAGE VALUE (xij) WEIGHT OF IMPORTANCE (wj) (xij * wj)

Investment cost (£/kW) 1180 2950 2065 5 10325

Efficiency (%) 40 70 55 5 275

Emission (g-CO2/kW) 9.7 123.7 66.7 8 533.6

Operational life (years) 40 50 45 4 180

Jobs creation (employees/500MW) 5635 8521 7078 4 28312

Construction time (years) 1 2 1.5 5 7.5

TOTAL (S1) 39633.1

OFFSHORE OIL AND GAS

CRITERIA LOWER RANGE VALUE UPPER RANGE VALUE AVERAGE VALUE (xij) WEIGHT OF IMPORTANCE (wj) (xij * wj)

Investment cost (£/kW) 800 1450 1125 5 5625

Efficiency (%) 50 85 67.5 5 337.5

Emission (g-CO2/kW) 755.4 5569.7 3162.55 8 25300.4

Operational life (years) 25 30 27.5 4 110

Jobs creation (employees/500MW) 7327 10754 9040.5 4 36162

Construction time (years) 1 3 2 5 10

TOTAL (S2) 67544.9

Table D2-1 WSM mathematical model

148

OFFSHORE RENEWABLES

CRITERIA LOWER RANGE VALUE UPPER RANGE VALUE AVERAGE VALUE (xij) WEIGHT OF IMPORTANCE (wj) (xij ^ wj)

Investment cost (£/kW) 1180 2950 2065 5 3.75492E+16

Efficiency (%) 40 70 55 5 503284375

Emission (g-CO2/kW) 9.7 123.7 66.7 8 3.91748E+14

Operational life (years) 40 50 45 4 4100625

Jobs creation (employees/500MW) 5635 8521 7078 4 2.50982E+15

Construction time (years) 1 2 1.5 5 7.59375

TOTAL (S1) 5.78587E+62


CRITERIA LOWER RANGE VALUE UPPER RANGE VALUE AVERAGE VALUE (xij) WEIGHT OF IMPORTANCE (wj) (xij ^ wj)

Investment cost (£/kW) 800 1450 1125 5 1.80203E+15

Efficiency (%) 50 85 67.5 5 1401260449

Emission (g-CO2/kW) 755.4 5569.7 3162.55 8 1.00069E+28

Operational life (years) 25 30 27.5 4 571914.0625

Jobs creation (employees/500MW) 7327 10754 9040.5 4 6.6799E+15

Construction time (years) 1 3 2 5 32

TOTAL (S2) 3.08909E+75

Table D2-2 WPM mathematical model

149

D.3 FMEA CALCULATIONS


S/N RISKS FREQUENCY OF OCCURRENCE (O) SEVERITY (S) DETECTABILITY (D) RPN (O X S X D) RANK

1 Access to offshore reserves 7.00 7.67 2.00 107.38 28

2 Climate change concerns 4.33 6.33 2.67 73.181763 30

3 Competition for proven reserves 6.00 6.33 2.67 101.4066 29

4 Competition from offshore renewables 8.00 7.00 3.00 168 21

5 Construction risks 6.33 8.00 4.00 202.56 16

6 Contractual risks 6.00 6.33 3.67 139.3866 26

7 Decommissioning risks 6.67 5.67 4.67 176.614263 20

8 Environmental risks 9.00 8.67 4.33 337.8699 4

9 Exploration risks 7.67 8.00 6.67 409.2712 1

10 Expropriation and nationalization risks 7.00 4.67 7.00 228.83 10

11 Fluctuating fiscal terms 7.33 5.00 7.33 268.6445 8

12 Geological risks 6.33 7.00 5.67 251.2377 9

13 Governmental regulations 5.67 6.00 5.67 192.8934 19

14 Health risks 5.33 6.67 5.67 201.574737 17

15 Human capital deficit 7.00 7.00 3.33 163.17 22

Table D3-1 FMEA mathematical model – Spreadsheet 1

150



16 Installation risks 7.00 8.00 4.00 224 15

17 Insurance risks 5.00 6.00 4.33 129.9 27

18 Investment risks 6.00 8.00 4.67 224.16 14

19 Legal risks 4.67 5.33 6.00 149.3466 25

20 New operational challenges 6.67 6.33 5.33 225.038463 12

21 Political instabilities 5.67 6.67 8.00 302.5512 6

22 Price volatility 7.00 5.33 8.00 298.48 7

23 Processing and separation risks 5.00 5.50 5.67 155.925 23

24 Safety risks 8.67 8.33 4.67 337.272537 5

25 Supply chain glitches 5.33 6.67 6.33 225.038463 13

26 Taxation risks 5.00 5.00 6.00 150 24

27 Transportation risks 6.33 6.33 5.00 200.3445 18

28 Uncertain energy policies 6.67 7.00 8.33 388.9277 3

29 Unfamiliar environment risks 4.67 6.33 7.67 226.733637 11

30 Unstable market conditions 7.33 6.67 8.00 391.1288 2


151

OFFSHORE RENEWABLES


1 ‘Blade throw’ risks 3.50 8.50 4.00 119.00 17

2 Collision risks 5.00 6.00 3.00 90.00 21

3 Commissioning risks 5.50 6.50 2.50 89.38 22

4 Competition from offshore oil and gas 4.00 4.50 4.00 72.00 26

5 Construction and installation risks 6.00 5.50 5.00 165.00 14

6 Contractual risks 5.50 6.50 3.00 107.25 18

7 De-commissioning risks 6.50 4.00 7.50 195.00 10

8 Engineering design uncertainties 4.50 4.50 4.50 91.13 20

9 Environmental risks 6.50 6.00 5.50 214.50 7

10 Fluctuating energy standards 6.50 8.00 8.50 442.00 2

11 Global energy market uncertainties 7.50 7.50 7.50 421.88 3

12 Grid connection and integration risks 5.00 6.00 5.00 150.00 16

13 Investment risks 6.00 7.50 4.50 202.50 8

14 Inconsistent energy policies 7.00 8.00 8.00 448.00 1

15 Insurance risks 4.00 5.00 3.00 60.00 28


152

OFFSHORE RENEWABLES


16 Legal risks 5.50 4.00 3.50 77.00 23

17 Licensing risks 5.50 4.00 3.50 77.00 23

18 Offshore communications interferences 3.00 4.50 3.00 40.50 30

19 Political instabilities 3.50 8.00 6.50 182.00 12

20 Price fluctuations 7.50 7.00 7.00 367.50 4

21 Project approval risks 5.50 4.00 4.50 99.00 19

22 Public and private sector partnership risks 5.50 3.50 4.00 77.00 23

23 Public disapproval of projects 4.00 8.50 4.50 153.00 15

24 Reduction of subsides and tariffs 6.50 8.50 5.00 276.25 5

25 Regulatory compliance risks 6.50 6.00 5.00 195.00 10

26 Structural failure risks 5.50 8.00 4.50 198.00 9

27 Structural maintenance risks 5.50 8.00 4.00 176.00 13

28 Supply chain fluctuations 6.00 6.50 6.00 234.00 6

29 Taxation risks 4.00 5.00 3.50 70.00 27

30 Technology maturity level risks 4.50 3.50 3.00 47.25 29


153

D.4 TOPSIS CALCULATIONS

OFFSHORE RENEWABLES

TOP RANKED RISKS PONOC POEOC STI LTI DCI ICI

Inconsistent energy policies 67.2 22.4 67.2 112 67.2 112

Fluctuating energy standards 66.3 22.1 66.3 110.5 66.3 110.5

Global energy market uncertainties 63.2745 21.0915 63.2745 105.4575 63.2745 105.4575

Price fluctuations 55.125 18.375 55.125 91.875 55.125 91.875

Reduction of subsidies and tariffs 41.4375 13.8125 41.4375 69.0625 41.4375 69.0625

Supply chain fluctuations 35.1 11.7 35.1 58.5 35.1 58.5

Environmental risks 32.175 10.725 32.175 53.625 32.175 53.625

Investment risks 30.375 10.125 30.375 50.625 30.375 50.625

Structural failure risks 29.7 9.9 29.7 49.5 29.7 49.5

Regulatory compliance risks 29.25 9.75 29.25 48.75 29.25 48.75

Decommissioning risks 29.25 9.75 29.25 48.75 29.25 48.75

WEIGHTS 0.15 0.05 0.15 0.25 0.15 0.25

MIN MIN MAX MAX MIN MIN

IDEAL SCENARIO 29.25 9.75 67.2 112 29.25 48.75

WORST SCENARIO 67.2 22.4 29.25 48.75 67.2 112

Table D4-1 TOPSIS mathematical model – Spreadsheet 1

154


Inconsistent energy policies 0 0 655 750 0 97

Fluctuating energy standards 0.9 0.3 755 360 0.9 1.5

Global energy market uncertainties 3.9255 1.3085 560 705 3.9255 6.5425

Price fluctuations 12.075 4.025 870 125 12.075 20.125

Reduction of subsidies and tariffs 25.7625 8.5875 450 230 25.7625 42.9375

Supply chain fluctuations 32.1 10.7 230 505 32.1 53.5

Environmental risks 35.025 11.675 600 360 35.025 58.375

Investment risks 36.825 12.275 875 155 36.825 61.375

Structural failure risks 37.5 12.5 265 200 37.5 62.5

Regulatory compliance risks 37.95 12.65 785 125 37.95 63.25

Decommissioning risks 37.95 12.65 785 125 37.95 63.25

NORMALIZED VALUE 85.354703 28.451568 2033.9678 1312.2214 85.354703 172.18099

NORMED MATRIX


Inconsistent energy policies 0 0 0.3220307 0.5715499 0 0.5633607












155

WEIGHTED NORMED MATRIX













IDEAL SCENARIO 0.0666923 0.0222308 0.064529 0.1428875 0.0666923 0.1408402

WORST SCENARIO 0 0 0.0169619 0.0238146 0 0.0021779







Investment risks 0.001977 0.000659 0 0.1133574 0.001977 0.0517261


Regulatory compliance risks 0 0 0.0066373 0.1190729 0 0.0490037

Decommissioning risks 0 0 0.006637 0.119073 0 0.049004


156

CALCULATIONS BASED ON THE WORST SCENARIO



Fluctuating energy standards 0.0015816 0.0005272 0.0387174 0.0447714 0.0015816 0


Price fluctuations 0.0212203 0.0070734 0.0471984 0 0.0212203 0.0270428


Supply chain fluctuations 0.0564117 0.0188039 0 0.0723963 0.0564117 0.0755019




Regulatory compliance risks 0.0666923 0.0222308 0.0409299 0 0.0666923 0.0896586

Decommissioning risks 0.066692 0.022231 0.04093 0 0.066692 0.089659

SEPERATION DISTANCES, RELATIVE CLOSENESS AND RANKING

TOP RANKED RISKS di+ di- ci RANK

Inconsistent energy policies 0.09825 0.18544 0.65367 1

Fluctuating energy standards 0.183783 0.059235 0.243748 10

Global energy market uncertainties 0.159409 0.113827 0.416587 7

Price fluctuations 0.176075 0.062527 0.262055 9

Reduction of subsidies and tariffs 0.133892 0.092793 0.409348 8

Supply chain fluctuations 0.093025 0.132891 0.588231 2

Environmental risks 0.095567 0.13254 0.581044 3

Investment risks 0.124634 0.136728 0.523136 4

Structural failure risks 0.124555 0.13124 0.513067 6

Regulatory compliance risks 0.128933 0.138217 0.517375 5


157



Exploration risks 61.39068 20.46356 61.39068 102.3178 61.39068 102.3178

Unstable market conditions 58.66932 19.55644 58.66932 97.7822 58.66932 97.7822

Uncertain energy policies 58.339155 19.446385 58.339155 97.231925 58.339155 97.231925


Safety risks 50.590875 16.863625 50.590875 84.318125 50.590875 84.318125

Political instabilities 45.38268 15.12756 45.38268 75.6378 45.38268 75.6378

Price volatility 44.772 14.924 44.772 74.62 44.772 74.62

Fluctuating fiscal terms 40.296675 13.432225 40.296675 67.161125 40.296675 67.161125

Geological risks 37.685655 12.561885 37.685655 62.809425 37.685655 62.809425

Expropriation and nationalization risks 34.3245 11.4415 34.3245 57.2075 34.3245 57.2075

WEIGHTS 0.15 0.05 0.15 0.25 0.15 0.25

MIN MIN MAX MAX MIN MIN

IDEAL SCENARIO 34.3245 11.4415 61.39068 102.3178 34.3245 57.2075

WORST SCENARIO 61.39068 20.46356 34.3245 57.2075 61.39068 102.3178


158

ALL CRITERIA SHOULD BE MAXIMISED AS A REQUIREMENT OF TOPSIS METHOD - THUS, CONVERTING ALL MINIMISING CRITERIA


Exploration risks 0 0 655 750 0 87.3178

Unstable market conditions 2.72136 0.90712 755 360 2.72136 4.5356

Uncertain energy policies 3.051525 1.017175 560 705 3.051525 5.085875

Environmental risks 10.710195 3.570065 870 125 10.710195 17.850325

Safety risks 10.799805 3.599935 450 230 10.799805 17.999675

Political instabilities 16.008 5.336 230 505 16.008 26.68

Price volatility 16.61868 5.53956 600 360 16.61868 27.6978

Fluctuating fiscal terms 21.094005 7.031335 875 155 21.094005 35.156675

Geological risks 23.705025 7.901675 265 200 23.705025 39.508375

Expropriation and nationalization risks 27.06618 9.02206 785 125 27.06618 45.1103

NORMALIZED VALUE 50.199219 16.733073 2033.9678 1312.2214 50.199219 120.93094

NORMED MATRIX


Exploration risks 0 0 0.322031 0.57155 0 0.722047




Safety risks 0.215139 0.215139 0.221242 0.175275 0.215139 0.148843







159

WEIGHTED NORMED MATRIX






Safety risks 0.0322708 0.0107569 0.0331864 0.0438188 0.0322708 0.0372106






IDEAL SCENARIO 0.0808763 0.0269588 0.064529 0.1428875 0.0808763 0.1805117

WORST SCENARIO 0 0 0.0169619 0.0238146 0 0.0093764


Exploration risks 0.0808763 0.0269588 0.0162244 0 0.0808763 0




Safety risks 0.0486055 0.0162018 0.0313427 0.0990686 0.0486055 0.1433011



Fluctuating fiscal terms 0.0178454 0.0059485 0 0.1133574 0.0178454 0.1078325


Expropriation and nationalization risks 0 0 0.0066373 0.1190729 0 0.0872554


160

CALCULATIONS BASED ON THE WORST SCENARIO



Unstable market conditions 0.0081317 0.0027106 0.0387174 0.0447714 0.0081317 0


Environmental risks 0.0320031 0.0106677 0.0471984 0 0.0320031 0.0275255

Safety risks 0.0322708 0.0107569 0.0162244 0.0200042 0.0322708 0.0278342

Political instabilities 0.0478334 0.0159445 0 0.0723963 0.0478334 0.045779




Expropriation and nationalization risks 0.0808763 0.0269588 0.0409299 0 0.0808763 0.0838799

SEPERATION DISTANCES, RELATIVE CLOSENESS AND RANKING

TOP RANKED RISKS di+ di- ci RANK

Exploration risks 0.1186253 0.2108269 0.6399318 1

Unstable market conditions 0.2146109 0.3179011 0.5969839 2

Uncertain energy policies 0.2009551 0.1139266 0.3618076 8

Environmental risks 0.1996117 0.0717463 0.2643972 9

Safety risks 0.1905771 0.0603048 0.2403713 10

Political instabilities 0.1498683 0.1103052 0.4239679 6

Price volatility 0.1522516 0.1012309 0.3993605 7

Fluctuating fiscal terms 0.1585876 0.1212013 0.4331883 5

Geological risks 0.1516081 0.1266098 0.4550744 4

Expropriation and nationalization risks 0.1477698 0.150066 0.5038549 3


161

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