Oil and Gas Processing Equipment: Risk Assessment with Bayesian Networks

✍ Scribed by Unnikrsnan, G

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 153
• Category: Library

✦ Synopsis

Oil and gas industries apply several techniques for assessing and mitigating the risks that are inherent in its operations. In this context, the application of Bayesian Networks (BNs) to risk assessment offers a different probabilistic version of causal reasoning. Introducing probabilistic nature of hazards, conditional probability and Bayesian thinking, it discusses how cause and effect of process hazards can be modelled using BNs and development of large BNs from basic building blocks. Focus is on development of BNs for typical equipment in industry including accident case studies and its usage along with other conventional risk assessment methods. Aimed at professionals in oil and gas industry, safety engineering, risk assessment, this book Brings together basics of Bayesian theory, Bayesian Networks and applications of the same to process safety hazards and risk assessment in the oil and gas industry Presents sequence of steps for setting up the model, populating the model with data and simulating the model for practical cases in a systematic manner Includes a comprehensive list on sources of failure data and tips on modelling and simulation of large and complex networks Presents modelling and simulation of loss of containment of actual equipment in oil and gas industry such as Separator, Storage tanks, Pipeline, Compressor and risk assessments Discusses case studies to demonstrate the practicability of use of Bayesian Network in routine risk assessments

✦ Table of Contents

Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright......Page 5
Dedication......Page 6
Table of Contents......Page 8
Preface......Page 12
Author......Page 14
1.1 Application of BNs for Risk Assessment......Page 16
1.3 Major Limitations of QRA......Page 17
1.4 BN and Its Advantages......Page 18
1.5 Scope of the Book......Page 19
1.6 Structure of the Book......Page 20
2.1 Probability Basics......Page 22
2.1.1 Law of Total Probability......Page 25
2.1.2 Bayes Formula for Conditional Probability......Page 26
2.2 Bayes Theorem and Nature of Causality......Page 28
2.3 Bayesian Network (BN)......Page 29
2.3.2 Illustrative Example of Application......Page 30
2.4 Oil and Gas Separator......Page 33
2.5 Sensitivity to Findings......Page 37
2.6 Use of Probability Density Functions and Discretization......Page 39
2.8.1 Published Data......Page 40
2.9 C hapter Summary......Page 43
3.1 Oil and Gas Separator Basics......Page 44
3.3 Bayesian Network for LOC in Oil and Gas Separator......Page 45
3.4 Sensitivities......Page 50
3.5 Application of BN to Safety Integrity Level Calculations for Oil and Gas Separator......Page 51
3.5.1 The Independent Protection Layers (IPLs)......Page 52
3.5.2 ET for Layer of Protection Analysis (LOPA)......Page 53
3.6 Chapter Summary......Page 55
4.1 Causes of Pipeline Failures......Page 56
4.2 Mitigation Measures......Page 58
4.3 BN for Loss of Containment from Pipeline......Page 59
4.4 NoisyOr Distribution......Page 64
4.6 Event Tree for Pipeline LOC......Page 71
4.7.1 Background......Page 73
4.7.2 Key Findings......Page 74
4.8 Chapter Summary......Page 78
5.1 Storage Tank Basics......Page 82
5.2 Causal Factors for Loss of Containment......Page 83
5.3 Methodology for the Development of BN for LOC and Evaluation......Page 84
5.3.1 Quality of Design......Page 85
5.3.2 Quality of Maintenance and Inspection......Page 91
5.3.3 Quality of Construction......Page 92
5.3.4 Quality of Equipment Selection......Page 93
5.3.5 Quality of Risk Assessments......Page 95
5.3.7 Quality of Human and Organizational Factors......Page 96
5.3.8 Intermediate Causes......Page 99
5.3.10 BN for LOC Scenarios from Floating Roof Tank......Page 100
5.3.11 Sensitivities......Page 103
5.4 Event Tree for the Post LOC Scenario in Floating Roof (FR) Tank......Page 107
5.5 BN for LOC in Cone Roof (CR) Tank......Page 108
5.6 Chapter Summary......Page 111
6.1 What Happened at IOC Jaipur Tank Farm: Predictability of Bayesian Network......Page 112
6.2 Summary of the Investigation Committee Findings......Page 114
6.3 BN for Post LOC ET......Page 115
6.4 Chapter Summary......Page 117
7.1 Compressor Failure Modes......Page 118
7.2 Compressor Failure Rates......Page 119
7.3 Findings from the BN for Compressor Damage......Page 121
7.4 Sensitivity of Compressor Damage Node to Parent Nodes......Page 124
7.5 LOC and Its Consequences......Page 125
7.6 Chapter Summary......Page 126
8.1 Introduction......Page 128
8.2.3 Suction or Discharge Gasket/s......Page 129
8.3 BN for LOC in a Centrifugal Pump......Page 131
8.3.1 Consequences of LOC from a Centrifugal Pump......Page 132
8.4 Chapter Summary......Page 134
9.2 Bayesian Inference......Page 136
9.2.1 Computational Aspects......Page 137
9.3 Comparison between Traditional QRA and BN Methods......Page 139
References......Page 144
Index......Page 150

✦ Subjects

TECHNOLOGY / Petroleum;Electronic books