REPUTATION LOSS FRAMEWORK FOR CONSEQUENCE … · abstrak Penilaian kesan kegagalan paip saluran...

REPUTATION LOSS FRAMEWORK FOR CONSEQUENCE ASSESSMENT OF

ONSHORE PIPELINE DAMAGE

LIBRIATI ZARDASTI

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Civil Engineering)

Faculty of Civil Engineering

Universiti Teknologi Malaysia

MAY 2016

iii

DEDICATION

In the name of ALLAH, the Most Gracious, Most Beneficent.

I dedicate this thesis especially to:

… My beloved parent for their unconditional love…

Nursinah binti Tanggi and the late Zardasti bin Dawi

… My dear siblings who never giving up on me…

Nurmalawati (Along), Sri Martina (Angah), Elfi Zalena (Kak Epi), Nurmilawati

(Kak Mila), Rudi Hartono (Abang Rudi), Nani Ariani (Kak Pani), Marini (Kak

Erin), the late Muhammad Aswandi whom I never met and Fitrianengsih (Kak

Neneng);

… My adored brother in-laws who endlessly supports me…

Kasniza Effendy Shamsir (Abang Ngah), Muhammad Taufik (Abang Taufik),

Muhammad Khuzaimy Abdullah (Abang Jimmy) and Muhammad Firdaus (Abang

Apis);

… My naughty nieces and nephews who motivates me …

Kasniza Zulkarnain (Naim), Nurul Fatihah (Fatin), Nurul Irfani (Irfan), Kasniza

Khairulnazreen (Krol), Siti Aisyah (Aisyah), Nurasyidah (Syidah), Nurul

Najihah (Jiha), Nur Amrina (Amrina), Muhammad Hafiz (Apis), Putri Natasha

(Tasha), Muhammad Khusyairy (Harry), Muhammad Haikal (Haikal), Putri

Sarah Shakira (Ira), Marissa (Misa), and Muhammad Fayyadh (Fayyadh).

…My super awesome supervisors who inspire me…

Associate Professor Dr. Norhazilan Md. Noor and Professor Dr. Nordin Yahaya.

Thank you for everything. May ALLAH bless and grant us Jannatul Ferdous.

iv

ACKNOWLEDGEMENT

The preparation of this thesis requires me to get in contact with many people,

researchers, academicians and practitioners. They have contributed towards my

understanding and thoughts. In particular, I wish to express my sincere appreciation

to my main thesis supervisor, Associate Professor Dr. Norhazilan Md Noor, for

encouragement, guidance, critics and friendship. I am also very thankful to my co-

supervisors Professor Dr. Nordin Yahaya for their guidance, advices and motivation.

Without their continuous support and interest, this thesis would not have been the

same as presented here.

I am also indebted to Universiti Teknologi Malaysia (UTM) for funding my

Ph.D. study; the staff of PETRONAS Gas Berhad (PGB) Malaysia, especially Encik

Mohd. Nazmi Mohd Ali Napiah and his colleagues; Prof. Dr. Faisal Khan of

Memorial University of Newfoundland (MUN) Canada and his research teammates;

as well as other respondents of this research surveys that also deserve special thanks

for their assistance in supplying the relevant literatures and survey responses.

My fellow postgraduate students should also be recognised for their support.

My sincere appreciation also extends to all my research group members of

Reliability Engineering and Safety Assessment (RESA), colleagues in Faculty of

Civil Engineering, UTM and others who have provided assistance at various

occasions. Their views and tips are useful indeed. Unfortunately, it is not possible to

list all of them in this limited space. I appreciate supports from all in this PhD

journey.

v

ABSTRACT

Consequence assessment for pipeline damage is exercised to determine the

losses of a failure event such as human, asset, production and environmental loss.

However, assessment of reputation loss, which is part of failure impact, is usually

excluded due to its qualitative nature. Therefore, the need for a quantitative model of

reputation loss is of great interest among pipeline risk assessors. The available

current model assesses reputation loss qualitatively; it is a self-centered assessment,

time-independent loss factors and internal stakeholder’s influence are typically

neglected. Therefore, the study aimed to develop a quantitative model to quantify

reputation loss of the pipeline owner in order to improve the calculation of risk of

pipeline damage according to the four different stakeholders’ perceptions. A total of

30 reputation loss factors were identified via 30 case studies related to onshore

pipeline damage accident reports. These factors were included in a structured online

survey which was designed for the stakeholders in Malaysia to rank the factors

according to its influence on owner’s reputation based on a given imaginary worst

case scenario. A total of 200 respondents participated in the survey and the ranking

of the factors based on the four different stakeholders were obtained. All

stakeholders were in agreement that the factor which most influences the loss of

owner reputation is factor D3 “Accident Severity”. It is the parent factor of D31

“Multiple fatality and injuries”, D32 “Fire extinguished in longer duration”, D33

“Destroyed private properties”, and D34 “Damaged of vast environment area”.

Statistical analysis and fuzzy analytic hierarchy process (FAHP) were implemented

to prioritize and weigh the factors according to the four different stakeholders’

preferences. Four reputation loss models were then proposed to predict the reputation

loss due to pipeline explosion. Eight experts from PETRONAS Gas Berhad (PGB)

with 15 years of experience in pipeline integrity management were selected for

model validation. Model development was presented to the experts for validation

survey. The result indicates that this model is considered as comprehensive, fulfilled

the objective, well-defined and practical to be used with a moderate level of overall

reliability which can be improved by utilizing the model in a real case study. Thus,

the proposed model was implemented in a case study of pipeline in Malaysia by

taking the 2014 explosion event occurred in Lawas, Sarawak as a benchmark. The

value of consequence of failure calculated by the proposed model was 7% lower than

the current model from PETRONAS Technical Standard. This reduction has

significantly shifted the risk of pipeline failure from “High” to “Moderate” for the

rural area. To conclude, the inclusion of the proposed reputation loss model may

produce a comprehensive consequence assessment of pipeline damage and provide a

higher level of confidence to the pipeline owner to optimize their risk-based

inspection and maintenance scheme, hence, prolonging the long-term integrity of

their pipeline assets and simultaneously securing the company annual profit margins.

vi

ABSTRAK

Penilaian kesan kegagalan paip saluran minyak dan gas dilakukan untuk

menentukan kerugian terhadap manusia, harta benda, pengeluaran dan alam sekitar.

Walaubagaimanapun, impak lain iaitu kehilangan reputasi kebiasaannya

dikecualikan kerana sifat kualitatifnya. Maka, satu model kuantitatif kehilangan

reputasi amat diperlukan oleh para penilai risiko. Model sedia ada menilai

kehilangan reputasi secara kualitatif. Penilaiannya bersifat kendiri, faktor kehilangan

tidak bersandarkan masa, dan pengaruh pihak berkepentingan dalaman diabaikan.

Oleh itu, kajian dilakukan untuk membina model kuantitatif untuk mengukur

kehilangan reputasi pemilik paip saluran agar penilaian risiko kerosakan paip saluran

diperhebatkan berdasarkan persepsi empat jenis pihak berkepentingan. Sebanyak 30

faktor penyebab kehilangan reputasi dikenalpasti melalui 30 kajian kes laporan

berkaitan kerosakan saluran paip daratan. Faktor-faktor dimuatkan dalam borang

kajiselidik dalam talian yang direkabentuk untuk pihak-pihak berkepentingan di

Malaysia bagi menentukan kedudukan faktor mengikut pengaruhnya terhadap

reputasi pemilik paip saluran berdasarkan senario bayangan kes terburuk yang

disertakan. Seramai 200 responden telah terlibat dan kedudukan faktor-faktor

menurut empat pihak berkepentingan berbeza diperoleh. Kesemua pihak

berkepentingan bersependapat bahawa faktor D3 “Tahap keterukan kemalangan”

adalah faktor yang paling berpengaruh yang merupakan faktor induk kepada faktor

D31 “Kematian dan kecederaan berganda”, D32 “Tempoh panjang untuk memadam

kebakaran”, D33 “Kemusnahan harta benda awam”, dan D34 “Luas kawasan alam

sekitar yang terjejas”. Analisis statistik dan proses hierarki analitik kabur dijalankan

untuk menentukan pemberat faktor menurut tahap keutamaan pihak berkepentingan

berbeza. Empat model kehilangan reputasi kemudian dicadangkan untuk meramal

kehilangan reputasi disebabkan oleh letupan paip saluran. Lapan pakar dari

PETRONAS Gas Berhad (PGB) dengan 15 tahun pengalaman dalam pengurusan

integriti paip saluran dipilih untuk tujuan pengesahan model. Ia didahului dengan

pembentangan pembangunan model untuk tinjauan pengesahan dan keputusan

menunjukkan model dianggap menyeluruh, mencapai matlamat, jelas dan praktikal

untuk digunakan dengan kebolehpercayaan sederhana. Oleh itu, model telah

diimplementasi dalam kajian kes sebenar iaitu kes letupan saluran paip yang berlaku

di Lawas, Sarawak pada tahun 2014 bagi meningkatkan kebolehpercayaan. Nilai

kesan kegagalan berdasarkan model cadangan adalah 7% lebih rendah berbanding

nilai model Piawai Teknikal PETRONAS menyebabkan risiko kegagalan berubah

daripada “Tinggi” kepada “Sederhana” bagi kawasan luar bandar. Kesimpulannya,

model cadangan kehilangan reputasi menghasilkan penilaian kesan kegagalan paip

saluran yang menyeluruh dan meningkatkan tahap keyakinan pemilik untuk

mengoptimumkan skema pemeriksaan dan penyelenggaraannya berasaskan risiko.

Maka, integriti jangka panjang paip saluran dapat dilanjutkan dan sekaligus

melindungi keuntungan tahunan syarikat.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xiv

LIST OF FIGURES xxii

LIST OF ABBREVIATIONS xxvi

LIST OF SYMBOLS xxviii

LIST OF APPENDICES xxix

1 INTRODUCTION 1

1.1 Preface

1.2 Background and Motivation

1.3 Research Problem

1.4 Research Aim and Objectives

1.5 Research Scope

1.6 Research Significance

1.7 Research Methodology

1.8 Structure of Thesis

1

2

4

6

7

7

8

9

2 LITERATURE REVIEW 12

2.1 Introduction 12

viii

2.2 Pipeline Accident Documentations

2.2.1 Definition of Pipeline Accident

2.2.2 Pipeline Accident Statistics

2.3 Pipeline Integrity Management Program (PIMP)

2.3.1 Oil and Gas Pipeline Risk Assessment

2.4 Consequence Assessment

2.5 Loss Categories

2.6 Reputation Loss and its Definition

2.7 Reputation and Expectations

2.8 Reputation Loss Model

2.9 Reputation Loss Index

2.10 Reputation Loss Indicators in the Pipeline

Accidents

2.11 Major Onshore Oil and Gas Pipeline Accidents

2.11.1 Pipeline Explosion Cases (1965 – 1974)





2.13 Prioritization of Reputation Loss Factors

2.13.1 Analytic Hierarchy Process (AHP)

2.13.2 Fuzzy Analytic Hierarchy Process (FAHP)

2.14 Concluding Remarks

13

14

15

18

20

22

32

35

36

38

44

47

50

53

57

59

60

64

70

71

72

73

3 RESEARCH METHODOLOGY 75

3.1 Introduction

3.2 Overview of Research Design

3.3 Data Collection

3.3.1 Literature Review

3.3.2 Case Studies

3.3.3 Interview with Experts

3.3.3.1 Agreements on the Reputation

Loss Factors Identification

75

76

76

78

78

83

85

ix

3.3.3.2 Questionnaire Survey Validations

3.3.3.3 Determination of Reputation Loss

Severity Scale

3.3.4 Questionnaires

3.3.5 Questionnaire Design

3.3.5.1 Preliminary Questionnaire Design

– Type 1

3.3.5.2 Preliminary Questionnaire Design

– Type 2

3.3.6 Main Questionnaire Design

3.3.6.1 Main Questionnaire Design

– Type 1


– Type 2


– Type 3


– Type 4


– Type 5

3.3.7 Validation of Reputation Loss Model

Questionnaire Design

3.4 Data Analysis

3.4.1 Objective 1: Identification of Reputation

Loss Factors

3.4.1.1 Frequency analysis

3.4.1.2 Sample Size and Return Rate

3.4.1.3 Reliability analysis

3.4.1.4 Mann-Whitney Test

3.4.1.5 Kruskal-Wallis Test

3.4.2 Objective 2: Relationship between

Identified Factor and Reputation

3.4.2.1 Average Index

85

86

87

87

88

90

94

94

96

97

98

99

100

102

104

104

105

106

107

108

109

109

x

3.4.2.2 Relative Importance Index

3.4.2.3 Analytic Hierarchy Process (AHP)

3.4.2.4 Fuzzy Analytic Hierarchy Process

(FAHP)

3.4.2.5 Spearman’s rho Correlation

Analysis

3.4.3 Objective 3: Reputation Loss as a

Consequence Assessment

3.4.3.1 The Scale of Severity of the

Reputation Loss Factor

3.4.3.2 Reputation Loss Model

3.4.3.3 Validation of Reputation Loss

Model by Expert

3.4.3.4 Validation of Reputation Loss

Model by Case Study


110

111

116

118

119

119

121

121

122

126

4 QUESTIONNAIRE DATA ANALYSIS 128

4.1 Introduction

4.2 Result Reputation Loss Factor Identification

4.2.1 Survey Reliability Analysis

4.2.2 Sample Size and Return Rate

4.2.3 Results of Main Survey – Type 1

4.2.3.1 Section 1: Demographic

4.2.3.2 Section 2: Stakeholder Perceptions

4.2.3.3 Section 3: Operator’s Reputation

4.2.4 Result of Main Survey – Type 2


4.2.4.2 Section 2: Operator’s Reputation



4.2.5.2 Section 2: Customer’s Information

128

129

129

130

131

131

132

136

139

139

140

142

142

143

xi

4.2.5.3 Section 3: Level of Company

Reputation Loss


4.2.6.1 Section 1: Demographics

4.2.6.2 Section 2: Reputation Loss

Indicators



4.2.7.2 Section 2: Public Information

4.2.7.3 Section 3: Level of Company

Reputation Loss

4.3 Comparison of Reputation Loss Factors Rating

between Stakeholders

4.3.1 Factors Rating Comparison between Types

of Customer


of Employee


of Public


of Stakeholders

4.4 Comparison of Reputation Loss Factors Ranking

between Surveys

4.4.1 Comparison of Factor Ranking between

Stakeholder

4.4.2 Comparison of Factor Ranking between

Types of Survey


145

147

148

148

151

151

152

153

155

155

158

160

164

165

166

169

174

5 PRIORITIZATION OF REPUTATION LOSS

FACTORS

175

5.1 Introduction

5.2 Prioritization of Factors

175

175

xii

5.2.1 Analytic Hierarchy Process (AHP)

Framework

5.2.2 Fuzzy Analytic Hierarchy Process

5.2.3 Priority Vector

5.3 Reputation Loss Factors Weight Validation by

Experts

5.4 Correlation Analysis


177

181

184

191

193

201

6 DEVELOPMENT OF REPUTATION LOSS MODEL 203

6.1 Introduction

6.2 Reputation Loss Formulation

6.3 Reputation Loss Model

6.4 Model Validation by Expert

6.5 Major Pipeline Accident Reputation Loss

Assessment

6.6 Fluctuation of Share Price prior to Pipeline

Accidents

6.7 Model Validation via Pipeline Case Study in

Malaysia


203

205

212

213

216

220

225

232

7 DISCUSSION 235

7.1 Overview

7.2 Identification of Reputation Loss Factor

7.2.1 Survey Reliability Test and Return Rate

7.2.2 Selection of Reputation Loss Factor by

Stakeholders

7.3 Reputation Loss Factor Relationship with Pipeline

Owner’s Reputation

7.4 The Assessment of Pipeline Owner Reputation

Loss

235

236

237

238

243

248

xiii

7.5 Validation of Reputation Loss Model by Panels of

Expert

7.6 Limitations of the model

7.7 Model Validation via Pipeline Case Study in

Malaysia


254

258

258

260

8 CONCLUSION AND RECOMMENDATION 261

8.1 Summary

8.2 Conclusion

8.3 Significant of Research Contributions

8.4 Recommendations

261

262

263

265

REFERENCES 267

Appendix A-G 291 - 311

xiv

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 A Sample of PAER Model 5x5 Risk Assessment Matrix

for Oil and Gas Pipeline Integrity Management Program

(PIMP) (Source: PTS 30.40.60.33, 2012)

21

2.2 Guidelines for assessment of risk category

(Source: PTS 30.40.60.33, 2012)

23

2.3 Impact on People (Source: PTS 30.40.60.33, 2012) 24

2.4 Impact on Asset (Source: PTS 30.40.60.33, 2012) 24

2.5 Impact on Reputation (Source: PTS 30.40.60.33, 2012) 25

2.6 Impact on Environment (Source: PTS 30.40.60.33, 2012) 26

2.7 Identification of potential consequences for pipeline

(Source: DNV-RP-F107, 2010)

27

2.8 Safety consequence ranking (human) (Source: DNV-RP-

F107, 2010)

28

2.9 Spillage ranking (environment) (Source: DNV-RP-F107,

2010)

28

2.10 Economic consequence ranking (economic) (Source:

DNV-RP-F107, 2010)

28

2.11 Third party safety impact (Source: DNV-DSS-316, 2013) 29

2.12 Environmental Impact (Source: DNV-DSS-316, 2013) 30

2.13 Assessment of consequence of pipeline failure

(Source: DNV-RP-F116, 2009)

31

2.14 Six Level Safety, Health and Environmental Consequence

Categories (Source: API-RP-580, 2009)

32

xv

2.15 Loss Costing Guidelines for Oil and Gas Exploration and

Production (Source: E&P 6.54/246, 1996)

34

2.16 A taxonomy of a company’s stakeholders and related

expectations (Source: Scandizzo, 2011)

37

2.17 Reputation Paradigm – The Schools of Thought

(Source: Chun, 2005)

38

2.18 Qualitative reputation models (Source: Trotta and

Cavallaro, 2012)

40

2.19 Summary of reputation loss model characteristics 44

2.20 Components of Reputation Index (Source: Cravens et al.,

2003)

46

2.21 Causes and the percentage of pipeline accident in Europe,

North America and the former Soviet Union in the past

30 years (Source: Janssens et al., 2009)

47

2.22 Summary of the selected major onshore pipeline

accidents case study

53

3.1 Summary of the severity of the selected major onshore

pipeline accident case study

79

3.2 Summary of the extracted reputation-threat factors in the

selected major onshore pipeline accidents case study

81

3.3 Identified reputation loss factor and its indicators found in

the selected major onshore pipeline accidents case study

82

3.4 Fundamental Comparison Scale for AHP method 89

3.5 Scale transformation between Likert and AHP Method 89

3.6 Corrected Scale between Preliminary Survey Design 2

and AHP Method

92

3.7 Qualitative Scale of Preliminary Survey Design 2 and

AHP Method

93

3.8 Valid return rate of questionnaire for analysis and

reporting purposes (Source: Miller, 1991)

105

3.9 Cronbach’s alpha reliability coefficient (Source: Gliem

and Gliem, 2003)

107

3.10 Average Index classification 110

xvi

3.11 5-point rating scale of relative importance index

classification

111

3.12 9-point rating scale of relative importance index

classification

111

3.13 Number of comparisons 113

3.14 Data transformation scheme to pairwise judgment

(Source: Chen, 2010)

114

3.15 Random Index for factors in the process of decision

making

116

3.16 Linguistic scale for importance 116

3.17 Characteristic of selected sites (Source: Nazim, 2015) 123

3.18 Formula of onshore pipeline consequence assessment

with scoring descriptor (Source: PTS 30.40.60.33, 2012)

126

4.1 Result of questionnaires’ Cronbach’s alpha reliability

coefficient

129

4.2 Result of sample size and return rate of main

questionnaire

130

4.3 Respondent’s demographics of main survey type 1 131

4.4 Average index of post-event reputation loss level 133

4.5 Demographics of the employees 134

4.6 The influence of factors on pipeline owner’s reputation 137

4.7 Respondent’s demographic of main survey type 1 140

4.8 Average index of reputation loss factor influence on

pipeline owner

141


4.10 Retailer’s information 143

4.11 End-user’s information 144

4.12 End-user’s monthly income and percentage spent on

products

145

4.13 Average index of factors according to customer

perceptions

146

4.14 Respondent’s demographics of main survey type 4 148

xvii

4.15 Average index of reputation loss factor according to

employee perception

149


4.17 Public information 152

4.18 Average index of reputation loss factor according to

public perception

154

4.19 Difference of the reputation loss factor ratings on

customer demographic

156


customer information

157

4.21 Difference of the reputation loss factor ratings by the

post-accident product changes preferences

158


employee information

159

4.23 Difference of the reputation loss factor ratings by

company’s sector

160

4.24 Difference of the reputation loss factor ratings on public

information

161


employee information

162

4.26 Difference of the reputation loss factor ratings by the

post-accident product preference changes

163

4.27 Difference of the reputation loss factor ratings by types of

stakeholders

164

4.28 Ranking of reputation loss factor according to different

types of stakeholders

167

4.29 Difference of the reputation loss factor ratings by

different stakeholders

168

4.30 Reputation loss factors ranking according to category of

influence

169

4.31 Reputation loss factors ranking according to all factors 171

4.32 Difference of the reputation loss factor ratings 172

5.1 Reputation loss factors dependency 176

xviii

5.2 Pairwise comparison matrix for Criterion 180

5.3 Pairwise comparison matrix for Sub-criterion 181

5.4 Pairwise comparison matrix for Sub-subcriterion 181

5.5 Calculation of priority vector for Criterion 181

5.6 TFN pairwise comparison matrix for Criterion 182

5.7 Normalized weight vector for factors in Criterion level 184

5.8 Priority vector of factors using AHP, FAHP and Super

Decisions

185

5.9 Difference of priority vector between types of

stakeholders and analysis methods

186

5.10 Ranking of factors by AI, AHP, FAHP and Super

Decisions

187

5.11 Local and global weight for factors according to investor

perceptions

189

5.12 Local and global weight for factors according to customer

perceptions

190

5.13 Local and global weight for factors according to

employee perceptions

190

5.14 Local and global weight for factors according to public

perceptions

191

5.15 Expert profiles 192

5.16 Priority vector of factors between experts and respondents

using FAHP

192

5.17 Ranking of factors between experts and respondents using

FAHP

193

5.18 Correlation analysis between factors in Criterion level

(stakeholder-influenced)

194

5.19 Correlation analysis for between factors Sub-subcriterion

(consequence-influenced)

194

5.20 Correlation analysis between factors in Criterion and

Sub-criterion

195

5.21 Correlation analysis between factors by customers

(employee-influenced)

196

xix

5.22 Correlation analysis between factors by customers

(public-influenced)

196

5.23 Correlation analysis between factors by employees

(investor-influenced)

197

5.24 Correlation analysis between factors by employees


198

5.25 Correlation analysis between factors in Criteria and Sub-

criterion by employees

198

5.26 Correlation analysis between factors in Sub-criterion and

Sub-subcriterion by the public

198

5.27 Correlation analysis between factors in Criterion level by

the public

199

5.28 Correlation analysis between factors by the public

(investor-influenced)

199

5.29 Correlation analysis between factors by the public


200


criterion by the public (investor-influenced)

200


criterion by the public (customer-influenced)

201


criterion by the public (public-influenced)

201

6.1 The equation for reputation loss calculation of the impact

by investor

205


by customers

206


by employee

206


by the public

207

6.5 5-point scale reputation loss severity level (investor-

influenced)

209

xx

6.6 5-point scale reputation loss severity level (customer-

influenced)

209

6.7 5-point scale reputation loss severity level (employee-

influenced)

210

6.8 5-point scale reputation loss severity level (public-

influenced)

211

6.9 Reputation loss model 212

6.10 Relative importance index of factor based on experts’

judgments

213

6.11 Ranking of the factors by experts and various

stakeholders

214

6.12 PETRONAS experts’ profiles 215

6.13 Reputation loss model validation result by experts 215

6.14 Share price details of the pipeline owner of the case study 217

6.15 Reputation loss evaluation for factor A1 “Share price

dropped and market capitalization affected”

218

6.16 Reputation loss evaluation for factor D31“Multiple

fatality and injuries”

218

6.17 Evaluation of Reputation Loss for factor A1 “Share price

dropped and market capitalization affected” and D31

“Multiple fatality and injuries”

219

6.18 Increment of reputation loss value by different numbers

of factor

219

6.19 100 data of share price of recent onshore pipeline

accidents

223

6.20 Pipeline owners’ share price details 225

6.21 Reputation loss result of each stakeholder for the case

study

230

6.22 Risk score for pipeline at rural area 231

6.23 Risk score for pipeline at urban area 232

7.1 Percentage of difference of the reputation loss factors’

priority vector between expert and respondents

238

7.2 Ranking of reputation loss factors by the stakeholder 240

xxi

7.3 Correlation of factor in two consecutive AHP levels 244

7.4 Correlation between factors in similar AHP levels

according to Customer

246


according to Employee

247


according to the Public

248

7.7 Stakeholders with similar opinions on reputation loss

factors correlations

248

7.8 Expert opinions for validation criteria of level of

comprehensiveness

255


objectivity

255

7.10 Expert opinions for validation criteria of level of clarity 256


reliability

256


practicality

257

xxii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 Annual number of incident (1970 – 2013) (Source:

EGIG, 2014)

16

2.2 Cumulative number of incidents (1970 – 2013) (Source:

EGIG, 2014)

16

2.3 Annual number of accidents and incidents in TSB (2004

– 2013) (Source: TSB, 2014)

17

2.4 Annual serious and significant incidents in PHMSA

(2004 – 2013) (Source: PHMSA, 2015)

17

2.5 PETRONAS PIMP elements (Source: PTS 30.40.60.33,

2012)

19

2.6 Illustration of threats, values and ‘loss categories’ in risk

– and vulnerability management (Source: Hokstad and

Steiro, 2006)

33

2.7 Consequence categories with their loss indicators

(Source: Arunraj and Maiti, 2009)

33

2.8 The reputation loss model of Dutch private banks

caused by IT-security incidents (Source: Bie, 2007)

41

2.9 The retail industry reputation model (Source: APCO

Insight, 2010)

42

2.10 The retail industry reputation index performance score

(Source: APCO Insight, 2010)

45

2.11 Accident frequency data in various energy sector

(Source: Sovacool, 2008)

51

xxiii

2.12 Craters beneath a demolished three-storey building at

the LaSalle Height (Source: Gravenor, 2008)

54

2.13 Aerial view of burning buildings at Richmond, Indiana

(Source: Burke, 2015)

55

2.14 Osaka gas pipeline explosion (Source: Akatsuka and

Kobayashi, 2010)

56

2.15 Modern shops damaged in Clarkston Toll, Renfrewshire

(Source: Brown, 2011)

57

2.16 Smoke all over a burning hotel in Fremont (Source:

Real-McKeighan, 2002)

58

2.17 Trans-Siberian Railway tracks scattered upon explosion

(Source: Onliner, 2015)

59

2.18 Guadajalara streets destroyed after pipeline explosion

(Source: Expansion, 2012)

60

2.19 Search and rescue workers begin operation in shoe store

at Rio Pedras (Source: Wikipedia, 2015)

61

2.20 Aerial view of Pecos River campsite post-accident

(Source: NTSB/PAR-03/01, 2003)

63

2.21 Ariel view of Ghislenghein pipeline explosion (Source:

ARIA, 2009)

64

2.22 Burning oil pipeline in Abule Egba, Lagos (Source:

China.org.cn, 2006)

65

2.23 Fire burning Xingang Port after pipeline explosion in

Dalian (Source: Boston.com, 2010)

66

2.24 Cars destroyed after Pemex pipeline explosion in

Puebla, Mexico (Source: People's Daily Online, 2010)

67

2.25 Ripping roads and damaged vehicles after explosion in

Shandong (Source: Barber, 2013)

68

2.26 Fire after explosion of GAIL gas pipeline in India

(Source: Janyala, 2014)

69

2.27 Vehicles overturned and trapped in the middle of split

road at Kaohsiung, Taiwan (Source: The Malaysian

Insider, 2014)

70

xxiv

2.28 A general analytic hierarchy process framework 71

3.1 Overview of research design 77

3.2 The proposed reputation loss model framework 83

3.3 Interview sessions objectives and results 84

3.4 Preliminary Questionnaire Design – Type 1 90

3.5 Preliminary Questionnaire Design – Type 2 93

3.6 Main Questionnaire Design – Type 1 95





3.11 Validation of Reputation Loss Model Questionnaire

Design

101

3.12 Framework of data analysis 102

3.13 Table for determining minimum returned sample size

for a given population size for continuous and

categorical data (Source: Bartlett et al., 2001)

106

3.14 Hierarchical breakdown of reputation loss model

framework

112

3.15 Peninsular Gas Utilisation networks in Peninsular

Malaysia (Source: Oil Peak, 2012)

123

3.16 Pictures of topographical area at the five selected sites

(Source: Nazim, 2015)

124

3.17 Arial view the two selected urban sites (Source: Google

Map)

125

4.1 Method used by the customer to express dissatisfactory

towards the pipeline owner

134

4.2 Customer purchase preference after event 134

4.3 The highest contributor of pipeline owner reputation

loss

136

4.4 Monthly income spent for oil and gas products by the

end-users

145

5.1 AHP process flowchart 177

xxv

5.2 AHP framework in Super Decisions main window 178

5.3 A snapshot of pairwise comparison window of Super

Decisions

179

5.4 A snapshot of priorities window of Super Decisions 180

5.5 FAHP process flowchart 182

6.1 Process flowchart of the results of previous chapters 206

6.2 The fluctuation of pipeline owner’s share price (400

data)

221

6.3 The fluctuation of pipeline owner’s share price (100

data)

221

6.4 Conceptual framework of the proposed research 234

7.1 Local priority vector for public influence factors of each

stakeholder

240

7.2 Local priority vector of different types of stakeholder

perceptions

242

7.3 The aerial view of deep trenches of splitting roads by

Kaohsiung pipeline blast (Source: Lux, 2014)

252

xxvi

LIST OF ABBREVIATIONS

AHP - Analytic hierarchy process

AI - Average Index

ALARP - As Low As Reasonably Practicable

BP - British Petroleum

CEO - Chief Executive Officer

CGTD - China General Terminal and Distribution Corporation

CI - Consistency Index

CNPC - China National Petroleum Corporation

CR - Consistency Ratio

CPC - CPC Corporation

DNV - Det Norske Veritas

EGIG - European Gas pipeline Incident data Group

EPNG - El Paso Natural Gas Company

E&P - Exploration and Production

FAHP - Fuzzy analytic hierarchy process

GAIL - Gas Authority of India Limited

HCA - High Consequence Area

IT - Information Technology

KPC - Kenya Pipeline Company

LCY - LCY Chemical Corporation

LNG - liquefied natural gas

MCDM - Multi criteria decision making

MYR - Malaysia Ringgit

NGO - Non-Government Organisation

NIL - Not in list

NNPC - Nigerian National Petroleum Corporation

xxvii

NTSB - National Transportation Safety Board

OGP - International Association of Oil and Gas Producers.

PAER - People, Asset, Environment and Reputation

Pemex - Petroleos Mexicanos

PETRONAS - Petroliam Nasional Berhad

PCG - PG&E Corp.’s

PHMSA - Pipeline Hazardous Materials Safety Administration

PGB - PETRONAS Gas Berhad

PGU - Peninsular Gas Utilisation

PG&E - Pacific Gas and Electric Co.

PTS - PETRONAS Technical Standards

QNG - Quebec Natural Gas Corporation

RI - Random Index

RII - Relative Importance Index

RL - Reputation loss

RQ - Reputation Quotient

SD - Super Decisions

SS - Sample Size

SPSS - Statistical Packages for the Social Sciences

SSGP - Sabah-Sarawak Gas Pipeline

S&P - Standard & Poor's

TFN - Triangular Fuzzy Number

TGT - Tennessee Gas Transmission Co.

TSB - Transportation Safety Boards

UK - United Kingdom

US - United States

USA - United States of America

USD - United States Dollar

WMAC - World’s Most Admired Companies

xxviii

LIST OF SYMBOLS

A - the comparison matrix

α - Cronbach’s alpha reliability coefficient

ai - is the constant expressing the weight given to i,

d - degree of accuracy expressed as a proportion (0.05)

d - ordinate of the highest intersection point D

f - frequency of an observation and

Indexmax - maximum index

Indexmin - minimum index

λmax largest eigenvalue

K - sample with more than two groups

k - convex fuzzy numbers

N -

number of respondents; population size; number of rating

scale index

ni - number of respondents who rate the importance or

influence of the factor as i = 1 as “very low”; 2 as

“low”;3 as “moderate”; 4 as “high”; and5 as “very high”.

n - the dimension of the matrix

P - population proportion

s - the required sample size

W - a non-fuzzy number

w - eigenvector

wi - weight of factor i

X2 - the table value of chi-square

x - variable expressing the frequency response for i = 1, 2, 3,

4, and 5.

LIST OF APPENDICES

APPENDIX TITLE PAGE

A PRELIMINARY SURVEY – 1st trial 291

B PRELIMINARY SURVEY – 2nd trial 293

C MAIN QUESTIONNAIRE – SURVEY TYPE 1 296

D MAIN QUESTIONNAIRE – SURVEY TYPE 2 301

E MAIN QUESTIONNAIRE – SURVEY TYPE 3 304

F MAIN QUESTIONNAIRE – SURVEY TYPE 4 308

G MAIN QUESTIONNAIRE – SURVEY TYPE 5 311

CHAPTER 1

INTRODUCTION

1.1 Preface

Onshore oil and gas pipeline damage due to explosion, leakage, and etc. is an

undesired event. Accidents cause significant negative impact such as loss of life,

destruction of private and public property and serious environmental damage. In fact,

this event is capable of tarnishing the pipeline owner’s reputation as well as

jeopardizes the confidence level of their internal and external stakeholders such as

investors, employees, customers, public etc. In a famous quote, Warren Buffet, the

chief executive officer (CEO) of Berkshire Hathaway warned: “It takes 20 years to

build a reputation and five minutes to ruin it. If you think about that you will do

things differently”, (Rochette, 2007; Vallens, 2008; Gaultier-Gaillard, Louisot and

Rayner, 2009; Bibi, 2011). This quote emphasized the importance of protecting and

enhancing the stability of company reputation. Hence, negative perception among

stakeholders decreases and eventually improves company’s profit margin.

In the risk assessment of pipeline damage, the impact of an accident in terms

of monetary value is most preferable by the owner. Thus human, environment, and

economic losses are converted into dollars to assist them in forecasting their losses in

each occurrence to obtain risk value in monetary terms. Nevertheless, reputation loss

is a function of the impact or consequence of failure as well. It can be included in the

2

consequence assessment of pipeline damage as the impact of the accident on owner’s

reputation is certainly significant. Therefore, the presence of reputation loss

assessment provides comprehensive risk estimation and subsequently allows the

owner to prepare an optimum inspection and maintenance schedule, hence boosting

annual corporate profit.

1.2 Background and Motivation

Pipelines are susceptible to failure even though it is the most economical,

fastest, and safest means of transporting natural gas and hazardous liquids in large

amount (Dziubínski et al., 2006; Carvalho et al., 2008; Brito and Almeida, 2009;

Brito et al., 2010; Furchtgott-Roth, 2013). A comprehensive Pipeline Integrity

Management Program (PIMP) is vital for the maintenance of a safe and reliable oil

and gas pipeline. It consists of a foundation of pipeline inspection, assessment,

mitigation and communication aimed at minimizing the risk of the pipeline failure to

As Low As Reasonably Practicable (ALARP). This program has experienced

significant changes since the early 2000’s. The number of gas transmission pipeline

incidents had increased over the past 15 years according to the United States

Department of Transportation (USDOT) of Pipeline and Hazardous Material Safety

Administration (PHMSA) online data source (PHMSA, 2015). These failure events

can harm the public, the environment, assets and production. The reputation of

pipeline owners is endangered regardless of how the pipeline had been operating

prior to the failure event. Pipeline failure has great financial costs for pipeline

owners. For gas transmission pipelines alone, failure events have cost pipeline

owners approximately one billion US Dollars over the last 15 years (2000 – 2015)

(PHMSA, 2015). A well-planned pipeline inspection and maintenance program is

necessary to avoid pipeline damage and reduce the impact of failure events. PIMP

secures the annual profit margins of pipeline owners and protects its reputations.

3

The previous time-based inspection of pipeline integrity management was

improved by the implementation of Risk-Based Inspection (RBI). RBI allows

pipeline owners to choose the most cost effective pipeline inspection scheme. RBI

optimizes maintenance scheduling and reduces unnecessary inspections. As a part of

a RBI module, pipeline damage risk is assessed as a product of the likelihood or

frequency of pipeline damage probability and the impact or consequence of such an

event. Existing consequence assessments are quite effective in evaluating the

monetary loss of pipeline failure, such as the number of fatalities and injuries, cost of

asset damages, cost of production loss, and the cost of environmental pollution fines.

This assessment does not calculate the actual cost of pipeline damage due to the

qualitative nature of a company’s reputation influencing factors. This may be due to

time dependency, difficulties in quantifying factors into monetary value, or lack of

identification of reputation loss impact on local conditions. Onshore pipelines buried

underground are laid across various types of geographical surfaces with different

demographic populations. These various conditions contribute to different impacts

on company’s reputation due to a failure event. The impact of pipeline failure

causing an explosion in Europe is different from an explosion in Nigeria due to

different education levels. Public awareness of safe and reliable pipeline operation

varies between countries.

The reputation of a company depends on stakeholder beliefs. Each company

has at least four major stakeholders, including investors, customers, employees and

the public. Pipeline accidents impact all stakeholders directly or indirectly.

Stakeholder post-accident negative beliefs and responses to loss of human life,

economic damage and environmental damage due to a pipeline damage event can be

considered indicators of company reputation loss. Stakeholder perceptions and

expectations differ and are highly incident-dependent. Pipeline damage may affect

stakeholders physically or mentally and has a negative impact on the pipeline

owner’s reputation – an intangible asset that could be capable of generating tangible

loss. Current risk assessment for pipeline damage includes an assessment of failure

event effects on owner reputation. If a pipeline owner can identify the reputation loss

factors influenced by the views of external and internal stakeholder prior to a failure

4

event, a comprehensive consequence assessment for pipeline damage can be

established.

1.3 Research Problem

Onshore pipeline accidents have become common in recent years. In 2014,

there were a number of pipeline explosion events such as Kaohsiung in Taiwan,

Andhra Pradesh in India, and Sarawak in Malaysia. Current consequence of failure

assessment calculates the monetary losses of these pipeline damage events i.e.

human loss, production loss, asset loss and environmental loss because they are

quantitatively countable and visible, in addition to reputation loss. This loss

assessment is assessor-centered and ranges from very low to very high. The effects

of post-accident reputation loss on stakeholder perceptions is neglected due to

difficulties in quantifying factors (Khan and Haddara, 2004; Arunraj and Maiti,

2009). The effects of post-accident reputation loss are vital to most organizations

(Cravens et al., 2003); as it endangers profit margins (Money and Hillenbrand,

2006).

Most industry players choose to exclude post-accident reputation loss due to

its qualitative nature and the subjectivity of its factors. The factors for reputation loss

are as follows: time-dependent (Dunbar and Schwalbach, 2000; Bie, 2006);

multidimensional (Fombrun, 1996); behavior-dependent (Bie, 2006); and influenced

by stakeholder experience (Spence, 2011). Current practices for pipeline risk

assessment assume that the cost of reputation loss is equivalent to business

interruption costs (Muhlbauer, 2004). The loss of company reputation is judged and

calculated based on fluctuations in share price over a period of time in order to

simplify assessment procedures (Vergin and Qoronfleh, 1998; Money and

Hillenbrand, 2006; Tonello, 2007; Scandizzo, 2011). This type of reputation loss

quantification is time-dependent, but affects only a single stakeholder (investors).

The expectations of other stakeholders have similar impacts i.e. jeopardizing the

5

reputation of the company and significantly influencing company operations

(Macnamara, 2006). Public perception prior to a pipeline damage event is crucial as

it forces pipeline operators to apply mitigation measures. Public pressure for pipeline

safety differs by geographical location and the status of the pipeline owner.

Efforts have been made to quantify reputation loss for pipeline owners (E&P

6.54/246, 1996; Muhlbauer, 2004; PTS 30.40.60.33, 2012). Despite these efforts, a

model to calculate intangible reputation loss based on overall stakeholders’

perspectives, whether internal or external is currently unavailable. None of the

current available models prioritize reputation loss factors to assists operators in

responding to the most severe factors affecting the perspective of company

stakeholders. Reputation loss models for onshore pipeline damage do not yet exist,

but models covering reputation loss in other industries such as the banking and retail

do (Muller and Vercouter, 2008; APCO Insight, 2010; Li et al., 2010; Cherchiello,

2011; He and Wu, 2013).

As pipelines age and the risk of a failure event increases, there is a need to

account for additional factors in pipeline risk assessments such as reputation loss.

The inclusion of reputation loss in pipeline risk assessments makes those

assessments more conservative. If reputation loss, which is currently neglected in

calculations of monetary impact, has a significant contribution to total cost of a

failure event, neglecting it may result in the inaccurate assessment of failure

consequences. Planning errors for pipeline inspections and maintenance program

impose additional costs due to unnecessary inspections programs, affecting a

company’s annual profit margins. If a reputation loss model is successfully

developed, pipeline damage can be prevented with reasonable increments in

inspection frequency as pipeline operators pay more attention to higher risk

pipelines. Great effort is needed to develop a comprehensive consequence

assessment model incorporating the intangible elements of reputation loss for

comprehensive risk assessment. To reach this milestone, a detail investigation on

reputation loss factors is crucial.

6

1.4 Research Aim and Objectives

This study aims to develop a quantitative consequence assessment model for

pipeline failure associated with the reputation loss of the owner, based on a

Malaysian stakeholder perspective. The proposed model is unique in that reputation-

threat factors are both time-dependent and time-independent. This model is tailored

for onshore oil and gas pipeline damage resulting from an explosion. The objectives

of this study are:

1. To identify owner reputation-threat factors which lead to negative

perceptions among stakeholders in Malaysia prior to pipeline damage as

reported in selected onshore pipeline explosion case studies.

2. To determine the priority vector of the identified stakeholder-influenced

reputation-threat factors according to the degree of negative perceptions

among the major constituents of a company i.e. investor, customer, employee

and the public using Multi Criteria Decision Making (MCDM) method.

3. To evaluate the impact of reputation loss on pipeline owners by applying

stakeholder-oriented priority vectors for reputation-threat factors as a

consequence assessment of pipeline damage, including the prediction and

validation of the model via expert interviews and case studies in Malaysia.

These outcomes may contribute to the consequence assessment for pipeline

damage by exploring a selection of reputation loss factors for future pipeline owner

reputation loss modelling.

7

1.5 Research Scope

There are numbers of factors that may influence an oil and gas company’s

reputation. This study focuses on the relationship between reputation loss and

stakeholder perceptions resulting from a pipeline explosion by observing company

stakeholders’ responses (investor, customer, employee and public) prior to the event.

This study focuses on the geographical area of Malaysia, limited to the stakeholders

of the country’s oil and gas companies. Unstructured interviews and questionnaire

surveys are carried out for this study. Qualitative experts’ judgments were

transformed into quantitative information using fuzzy and Analytic Hierarchy

Processes (AHP) in order to reduce errors and increase accuracy. Statistical analysis

was used to identify the relationships between reputation loss and stakeholder

perceptions. The index method was used to rate the severity level of a company’s

reputation loss for modelling purposes. The model was then validated via expert

interviews and case studies in Malaysia.

1.6 Research Significance

The main challenge of reputation loss model development is to understand

factor selection in order to obtain an accurate model. This model is to be used by

pipeline risk assessors for engineering analysis. Previous studies did not include

reputation-threat factors towards pipeline owner in their assessment of pipeline

damage. The outcome of this study shows the influence negative stakeholder

perceptions have on a company’s reputation loss prior to an accident. A

comprehensive consequence assessment for onshore oil and gas pipeline damage can

be obtained by incorporating post-accident owner reputation-threat factors.

8

1.7 Research Methodology

Qualitative and quantitative approaches were conducted in this study. It

consists of four stages: preliminary study, data collection, data interpretation and

analysis and conclusions and recommendations. The first stage requires a

comprehensive review of literatures on pipeline risk assessment and reported

onshore pipeline post-accident damages in the past 50 years on a worldwide basis. It

was achieved through literature search via reports, journals, articles, books, internet

sources, online newspapers archives, informal discussion with experts and

researchers. This stage attains background knowledge of the topic, knowledge gaps

relating to the research problems, which eventually produces research aim and

objectives within a reachable research scope and the significance.

Second stage requires a wide-ranging literature search as well. All reviews

are supported with trusted sources to comply with statement validity. It includes

collecting data from case study, interviews with experts in pipeline risk management

and questionnaire survey distribution to the respective stakeholders namely investor,

customer, employee and public. This secondary data is able to identify the following

requirements: post-accident reputation-threat factor; the importance of the reputation

loss factor; the influence of reputation loss factor towards pipeline owner; the impact

of reputation loss factors on pipeline company’s stakeholders; and validation of

reputation loss severity scale for modelling.

The next stage interpreted and analyzed successfully answered questionnaire

surveys. The significance of reputation loss factors are tested before modelling is

developed. Statistical analysis, AHP method and fuzzy AHP method were

implemented in the priority vector calculation processes with the aid of Microsoft

Excel and Super Decisions. The experts are involved in the validation process to

justify the obtained values of factors priority. The evaluation of reputation loss is

formulated later using the priority vector for each factor obtained from different

stakeholders. The model is developed to classify the level of degradation of pipeline

owner’s reputation. It is then imposed on the selected onshore pipeline explosion

9

case studies to assess the respective level of reputation loss of pipeline owners. The

final stage concluded the findings that attain the research objectives and derive

recommendations within the research scope along with advice for future study.

1.8 Structure of Thesis

This thesis consists of eight chapters structured in the following manner:

1. Chapter 1 shares the introduction of the study. It covers the

motivation and background of the research, problems, aim and

objectives, scope and significance of the research. A brief research

methodology with the approach and method is stated. The outlines of

the research are mentioned at the end of this chapter.

2. Chapter 2 provides a comprehensive literature review which covers

the pipeline accident documentations, the overviews of oil and gas

pipeline risk assessment and the consequence assessment including

the loss categories in terms of risk, the extensive explanation on

reputation loss and its definition, the relationship between reputation

and expectations, the current reputation model and reputation index in

various research field, the reputation loss indicators in various

pipeline explosion case, and the prioritization method of reputation

loss is reviewed in this chapter.

3. Chapter 3 demonstrates the overall methodology of the study.

Overview of research design, data collection methods, techniques to

identify reputation loss factors and the prioritization is explained

comprehensively. The reputation loss severity level and the model are

presented and the model validation procedures are described.

10

4. Chapter 4 produces the analysis and results for questionnaire survey

distributed to the respective pipeline owner’s stakeholder namely

investor, customer, employee and public. The reliability of the survey,

sample size and return rate, the demographic of the respondents and

the given 5-point rating of reputation loss level of each factor from all

types of survey is presented. The average index and ranking for each

factor in various surveys is calculated and the significant difference in

rating given by the respondents of all surveys between stakeholder

and the significant difference between surveys is tested.

5. Chapter 5 presented the priority vector of the reputation-threat or

reputation loss factor using analytic hierarchy process method and

fuzzy analytic hierarchy process with the aid of Microsoft Excel

software and Super Decisions software. The significant difference of

rating given by respondents between methods of obtaining priority

vector is tested. These priority vectors are validated done by the

experts. The correlated factors according to respective stakeholders

are extracted as well.

6. Chapter 6 transforms the previous priority vectors into a formulation

to assess pipeline owner’s reputation loss prior to pipeline accident.

The level of severity for all reputation-threat factors discussed with

the experts is listed in a scale of 1 to 5. This severity level produces

range of reputation loss values, which the class of reputation loss

index is explained. Simultaneously, a reputation loss model with

ranges of reputation loss index is presented, and the model validation

scores given by the experts are provided. The implementation of the

model in the selected case studies is applied.

7. Chapter 7 discusses the results obtained in previous three chapters.

This chapter deliberates on the relevance of analysis in order to

11

accomplish all research objectives. The achievement of the aim of this

study is declared at the end of this chapter.

8. Chapter 8 concludes the accomplishment of the research objectives

and the aim of the study. It also stated the contribution of this study

towards the industry of oil and gas. Research limitation and

recommendation is specified for future study.

267

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