Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico

被引:33
作者
Khakzad, Nima [1 ]
Khakzad, Sina [1 ]
Khan, Faisal [1 ]
机构
[1] Mem Univ Newfoundland, Fac Engn & Appl Sci, Safety & Risk Engn Grp, St John, NF A1B 3X5, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Major accident; Risk analysis; Bayesian analysis; Accident precursor data; Offshore blowout; DECISION-MAKING; SAFETY ANALYSIS; BOW-TIE; OIL; PRECURSORS; INCIDENTS; SYSTEMS; MISSES;
D O I
10.1007/s11069-014-1271-8
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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 data-accident precursor data-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.
引用
收藏
页码:1759 / 1771
页数:13
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