Major accidents are low-frequency, high-consequence accidents which are not well supported by conventional statistical methods due to the scarcity of directly relevant data. Modeling and decomposition techniques such as event tree have been proved as robust alternatives as they facilitate incorporation of partially relevant near accident dataâ€"accident precursor dataâ€"in probability estimation and risk analysis of major accidents. In this study, we developed a methodology based on event tree and hierarchical Bayesian analysis to establish informative distributions for offshore blowouts using data of near accidents, such as kicks, leaks, and failure of blowout preventers collected from a variety of offshore drilling rigs. These informative distributions can be used as predictive tools to estimate relevant failure probabilities in the future. Further, having a set of near accident data of a drilling rig of interest, the informative distributions can be updated to render case-specific posterior distributions which are of great importance in quantitative risk analysis. To cope with uncertainties, we implemented the methodology in a Markov Chain Monte Carlo framework and applied it to risk assessment of offshore blowouts in the Gulf of Mexico.

Item Type:	Refereed Article
Keywords:	accident precursor data, Bayesian analysis, major accident, offshore blowout, risk analysis
Research Division:	Engineering
Research Group:	Engineering practice and education
Research Field:	Risk engineering
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in engineering
UTAS Author:	Khan, FI (Professor Faisal Khan)
ID Code:	120752
Year Published:	2014
Web of Science® Times Cited:	32
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2017-08-30
Last Modified:	2017-11-03
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