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Full Description
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
Contents
Introduction. Bayes Theorem, Causality and Building Blocks for Bayesian Networks. Bayesian Network for loss of Containment in Oil & Gas Separator. Bayesian Network for Loss of Containment in Hydrocarbon Pipelines. Bayesian Network for Loss of Containment in Hydrocarbon Storage Tank. The Jaipur Tank Farm Accident. Bayesian Network for Centrifugal Compressor Damage. Bayesian Network for Loss of Containment in Centrifugal Pump. Other related topics. References. Index.
