Oil and Gas Processing Equipment: Risk Assessment with Bayesian Networks

153 Pages·2021·9.576 MB·English

by  Unnikrsnan, G

Oil and Gas Processing
Equipment
Risk Assessment with Bayesian
N etworks

Oil and Gas Processing
Equipment
Risk Assessment with Bayesian
Networks
G. Unnikrishnan
First edition published 2021
by CRC Press
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Library of Congress Cataloging‑in‑Publication Data
Names: Unnikrishnan, Ji., 1944– author.
Title: Oil and gas processing equipment : risk assessment with Bayesian
networks / authored by G. Unnikrishnan.
Description: First edition. | Boca Raton : CRC Press, 2021. | Includes
bibliographical references and index.
Identifiers: LCCN 2020019126 | ISBN 9780367254407 (hardback) |
ISBN 9780429287800 (ebook)
Subjects: LCSH: Gas manufacture and works—Risk assessment—Mathematics. |
Petroleum refineries—Risk assessment—Mathematics. | Gas manufacture
and works—Equipment and supplies—Safety measures—Mathematics. |
Petroleum refineries—Equipment and supplies—Safety
measures—Mathematics. | Bayesian statistical decision theory.
Classification: LCC TP752 .U66 2021 | DDC 620.1/07—dc23
LC record available at https://lccn.loc.gov/2020019126
ISBN: 978-0-367-25440-7 (hbk)
ISBN: 978-0-429-28780-0 (ebk)
Typeset in Palatino
by codeMantra
This book is dedicated to my parents and wife
For a safer world

Contents
Preface ......................................................................................................................xi
Author ...................................................................................................................xiii
1. Introduction .....................................................................................................1
1.1 Application of BNs for Risk Assessment ...........................................1
1.2 The Readership ......................................................................................2
1.3 Major Limitations of QRA ...................................................................2
1.4 BN and Its Advantages .........................................................................3
1.5 Scope of the Book ..................................................................................4
1.6 Structure of the Book ............................................................................5
2. Bayes Theorem, Causality and Building Blocks for
Bayesian Networks .........................................................................................7
2.1 Probability Basics ..................................................................................7
2.1.1 Law of Total Probability ........................................................10
2.1.2 Bayes Formula for Conditional Probability .......................11
2.2 Bayes Theorem and Nature of Causality .........................................13
2.3 Bayesian Network (BN) ......................................................................14
2.3.1 General Expression for Full Joint Probability
Distribution of a BN ..............................................................15
2.3.2 Illustrative Example of Application ....................................15
2.4 Oil and Gas Separator ........................................................................18
2.5 Sensitivity to Findings ........................................................................22
2.6 Use of Probability Density Functions and Discretization ............24
2.7 Framework for BN Application for Major Hazards .......................25
2.8 Sources of Failure Data ......................................................................25
2.8.1 Published Data .......................................................................25
2.8.2 Industry Reports ....................................................................28
2.9 Chapter Summary...............................................................................28
3. Bayesian Network for Loss of Containment from
Oil and Gas Separator..................................................................................29
3.1 Oil and Gas Separator Basics .............................................................29
3.2 Causes for Loss of Containment ......................................................30
3.3 Bayesian Network for LOC in Oil and Gas Separator ...................30
3.4 Sensitivities ..........................................................................................35
3.5 Application of BN to Safety Integrity Level Calculations for
Oil and Gas Separator ........................................................................36
vii
viii Contents
3.5.1 The Independent Protection Layers (IPLs) .........................37
3.5.2 ET for Layer of Protection Analysis (LOPA) ......................38
3.6 C hapter Summary...............................................................................40
4. Bayesian Network for Loss of Containment from
Hydrocarbon Pipeline..................................................................................41
4.1 Causes of Pipeline Failures ................................................................41
4.2 M itigation Measures ...........................................................................43
4.3 BN for Loss of Containment from Pipeline .....................................44
4.4 N oisyOr Distribution .........................................................................49
4.5 S ensitivities ..........................................................................................56
4.6 Event Tree for Pipeline LOC ..............................................................56
4.7 Case Study Using BN for Pipeline: Natural Gas Pipeline,
Andhra Pradesh, India .......................................................................58
4.7.1 B ackground .............................................................................58
4.7.2 K ey Findings ...........................................................................59
4.7.3 Application of the BN Model ...............................................63
4.7.4 BN for the Case Study ...........................................................63
4.8 C hapter Summary...............................................................................63
5. Bayesian Network for Loss of Containment from
Hydrocarbon Storage Tank .........................................................................67
5.1 Storage Tank Basics .............................................................................67
5.2 Causal Factors for Loss of Containment ..........................................68
5.3 Methodology for the Development of BN for LOC and
Evaluation .............................................................................................69
5.3.1 Quality of Design ...................................................................70
5.3.2 Quality of Maintenance and Inspection .............................76
5.3.3 Quality of Construction ........................................................77
5.3.4 Quality of Equipment Selection ...........................................78
5.3.5 Quality of Risk Assessments ................................................80
5.3.6 Quality of Systems and Procedures ....................................81
5.3.7 Quality of Human and Organizational Factors ................81
5.3.8 I ntermediate Causes ..............................................................84
5.3.9 Other Root Causes .................................................................85
5.3.10 BN for LOC Scenarios from Floating Roof Tank ...............85
5.3.11 S ensitivities .............................................................................88
5.4 Event Tree for the Post LOC Scenario in Floating Roof
(FR) Tank ..............................................................................................92
5.5 BN for LOC in Cone Roof (CR) Tank ................................................93
5.6 C hapter Summary...............................................................................96
6. The Jaipur Tank Farm Accident .................................................................97
6.1 What Happened at IOC Jaipur Tank Farm: Predictability of
Bayesian Network ...............................................................................97
Contents ix
6.2 Summary of the Investigation Committee Findings .....................99
6.3 BN for Post LOC ET ..........................................................................100
6.4 Chapter Summary.............................................................................102
7. Bayesian Network for Centrifugal Compressor Damage ..................103
7.1 Compressor Failure Modes ..............................................................103
7.2 Compressor Failure Rates ................................................................104
7.3 Findings from the BN for Compressor Damage...........................106
7.4 Sensitivity of Compressor Damage Node to Parent Nodes ........109
7.5 LOC and Its Consequences ..............................................................110
7.6 C hapter Summary.............................................................................111
8. Bayesian Network for Loss of Containment from a Centrifugal
Pump .............................................................................................................113
8.1 I ntroduction .......................................................................................113
8.2 Causes of LOC a Centrifugal Pump ...............................................114
8.2.1 Mechanical Seal ...................................................................114
8.2.2 Casing ....................................................................................114
8.2.3 Suction or Discharge Gasket/s ...........................................114
8.3 BN for LOC in a Centrifugal Pump ...............................................116
8.3.1 Consequences of LOC from a Centrifugal Pump ...........117
8.4 Chapter Summary.............................................................................119
9. Other Related Topics ..................................................................................121
9.1 Introduction .......................................................................................121
9.2 Bayesian Inference ............................................................................121
9.2.1 Computational Aspects .......................................................122
9.3 Comparison between Traditional QRA and BN Methods .........124
References ...........................................................................................................129
Index .....................................................................................................................135