TY  - JOUR
ID  - epos1754
UR  - http://doi.org/10.1007/s11069-014-1271-8
IS  - 3
A1  - Khakzad, Nima
A1  - Khakzad, Sina
A1  - Khan, Faisal
N2  - 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.
VL  - 74
TI  - Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico
AV  - none
EP  - 1771
Y1  - 2014/06//
PB  - Springer Verlag
JF  - Natural Hazards
KW  - Major accident Risk analysis Bayesian analysis Accident precursor data Offshore blowout
SN  - 0921-030X
SP  - 1759
ER  -