Muutke küpsiste eelistusi

Oil and Gas Processing Equipment: Risk Assessment with Bayesian Networks [Kõva köide]

(Kuwait Oil Company, Ahmadi, Kuwait)
  • Formaat: Hardback, 138 pages, kõrgus x laius: 234x156 mm, kaal: 358 g, 29 Tables, black and white; 80 Illustrations, black and white
  • Ilmumisaeg: 15-Sep-2020
  • Kirjastus: CRC Press
  • ISBN-10: 0367254409
  • ISBN-13: 9780367254407
Teised raamatud teemal:
Oil and Gas Processing Equipment: Risk Assessment with Bayesian NetworksSuurem pilt
  • Formaat: Hardback, 138 pages, kõrgus x laius: 234x156 mm, kaal: 358 g, 29 Tables, black and white; 80 Illustrations, black and white
  • Ilmumisaeg: 15-Sep-2020
  • Kirjastus: CRC Press
  • ISBN-10: 0367254409
  • ISBN-13: 9780367254407
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367254407.html
Märksõnad:
"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 causalreasoning. 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"--

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

Preface xi
Author xiii
1 Introduction
1(6)
1.1 Application of BNs for Risk Assessment
1(1)
1.2 The Readership
2(1)
1.3 Major Limitations of QRA
2(1)
1.4 BN and Its Advantages
3(1)
1.5 Scope of the Book
4(1)
1.6 Structure of the Book
5(2)
2 Bayes Theorem, Causality and Building Blocks for Bayesian Networks
7(22)
2.1 Probability Basics
7(6)
2.1.1 Law of Total Probability
10(1)
2.1.2 Bayes Formula for Conditional Probability
11(2)
2.2 Bayes Theorem and Nature of Causality
13(1)
2.3 Bayesian Network (BN)
14(4)
2.3.1 General Expression for Full Joint Probability Distribution of a BN
15(1)
2.3.2 Illustrative Example of Application
15(3)
2.4 Oil and Gas Separator
18(4)
2.5 Sensitivity to Findings
22(2)
2.6 Use of Probability Density Functions and Discretization
24(1)
2.7 Framework for BN Application for Major Hazards
25(1)
2.8 Sources of Failure Data
25(3)
2.8.1 Published Data
25(3)
2.8.2 Industry Reports
28(1)
2.9
Chapter Summary
28(1)
3 Bayesian Network for Loss of Containment from Oil and Gas Separator
29(12)
3.1 Oil and Gas Separator Basics
29(1)
3.2 Causes for Loss of Containment
30(1)
3.3 Bayesian Network for LOC in Oil and Gas Separator
30(5)
3.4 Sensitivities
35(1)
3.5 Application of BN to Safety Integrity Level Calculations for Oil and Gas Separator
36(4)
3.5.1 The Independent Protection Layers (IPLs)
37(1)
3.5.2 ET for Layer of Protection Analysis (LOPA)
38(2)
3.6
Chapter Summary
40(1)
4 Bayesian Network for Loss of Containment from Hydrocarbon Pipeline
41(26)
4.1 Causes of Pipeline Failures
41(2)
4.2 Mitigation Measures
43(1)
4.3 BN for Loss of Containment from Pipeline
44(5)
4.4 NoisyOr Distribution
49(7)
4.5 Sensitivities
56(1)
4.6 Event Tree for Pipeline LOC
56(2)
4.7 Case Study Using BN for Pipeline: Natural Gas Pipeline, Andhra Pradesh, India
58(5)
4.7.1 Background
58(1)
4.7.2 Key Findings
59(4)
4.7.3 Application of the BN Model
63(1)
4.7.4 BN for the Case Study
63(1)
4.8
Chapter Summary
63(4)
5 Bayesian Network for Loss of Containment from Hydrocarbon Storage Tank
67(30)
5.1 Storage Tank Basics
67(1)
5.2 Causal Factors for Loss of Containment
68(1)
5.3 Methodology for the Development of BN for LOC and Evaluation
69(23)
5.3.1 Quality of Design
70(6)
5.3.2 Quality of Maintenance and Inspection
76(1)
5.3.3 Quality of Construction
77(1)
5.3.4 Quality of Equipment Selection
78(2)
5.3.5 Quality of Risk Assessments
80(1)
5.3.6 Quality of Systems and Procedures
81(1)
5.3.7 Quality of Human and Organizational Factors
81(3)
5.3.8 Intermediate Causes
84(1)
5.3.9 Other Root Causes
85(1)
5.3.10 BN for LOC Scenarios from Floating Roof Tank
85(3)
5.3.11 Sensitivities
88(4)
5.4 Event Tree for the Post LOC Scenario in Floating Roof (FR)Tank
92(1)
5.5 BN for LOC in Cone Roof (CR) Tank
93(3)
5.6
Chapter Summary
96(1)
6 The Jaipur Tank Farm Accident
97(6)
6.1 What Happened at IOC Jaipur Tank Farm: Predictability of Bayesian Network
97(2)
6.2 Summary of the Investigation Committee Findings
99(1)
6.3 BN for Post LOC ET
100(2)
6.4
Chapter Summary
102(1)
7 Bayesian Network for Centrifugal Compressor Damage
103(10)
7.1 Compressor Failure Modes
103(1)
7.2 Compressor Failure Rates
104(2)
7.3 Findings from the BN for Compressor Damage
106(3)
7.4 Sensitivity of Compressor Damage Node to Parent Nodes
109(1)
7.5 LOC and Its Consequences
110(1)
7.6
Chapter Summary
111(2)
8 Bayesian Network for Loss of Containment from a Centrifugal Pump
113(8)
8.1 Introduction
113(1)
8.2 Causes of LOC a Centrifugal Pump
114(2)
8.2.1 Mechanical Seal
114(1)
8.2.2 Casing
114(1)
8.2.3 Suction or Discharge Gasket/s
114(2)
8.3 BN for LOC in a Centrifugal Pump
116(3)
8.3.1 Consequences of LOC from a Centrifugal Pump
117(2)
8.4
Chapter Summary
119(2)
9 Other Related Topics
121(8)
9.1 Introduction
121(1)
9.2 Bayesian Inference
121(3)
9.2.1 Computational Aspects
122(2)
9.3 Comparison between Traditional QRA and BN Methods
124(5)
References 129(6)
Index 135
G. Unnikrishnan has over 40 years of experience in oil and gas industry. His experience spans the areas of process design, process safety, engineering & project management. He is currently on assignment as Engineering Specialist with a National Oil Company in the Middle East. He previously worked with engineering consultancy companies in India and abroad. His current work involves review and assessment of Front End Engineering Design and engineering management for upstream oil and gas projects.

He is keenly interested in optimization of process design and how it can be done with the highest process safety. He believes that much needs to be done in process plant design and operations to minimize accidents. He is an active researcher in the area and has presented and published papers on the subject in several international conferences and technical journals. He is a certified Functional Safety Engineer on Safety Instrumented Systems. He holds a degree in Chemical Engineering from Calicut University, MTech from Cochin University of Science & Technology and PhD from University of Petroleum and Energy Studies, Dehradun, India.