Application of dynamic risk analysis in offshore drilling processes

Abstract Accident probability estimation is the most vital step to all quantitative risk assessment methods. Among many available techniques, bow-tie model (BT) is very popular because it represents the complete accident scenario comprising of both causes and consequences. However, the static structure of bow-tie models fails to support real-time monitoring and failure probability updating which are important factors in dynamic risk analyses. This work makes use of Bayesian networks, capable of updating probabilities by forward and backward analysis. A Bayesian network model is developed to represent a well kick scenario. The developed Bayesian network is operated in a dynamic environment in which the occurrence probabilities of accident consequences changes with time. The failure probability values of identified safety barriers are monitored real-time and their time-dependent probability variations are fed to the Bayesian network to obtain corresponding time-dependent variations in kick consequences. This study reveals the importance of real-time monitoring of safety barrier performances and shows the effect of deterioration of barrier performance on kick consequence probabilities.