Quantitative risk analysis of offshore well blowout using bayesian network

Blowout is the most feared and undesired accident during offshore drilling. It is inevitable, but the risk can be maintained to be below the acceptable criteria with effective strategies devised by risk analysis. An application of Bayesian networks (BN) for quantitative risk analysis on offshore blowouts was presented. First, we analyzed the SINTEF offshore well blowout data. 95% of blowout occurred in drilling, completion and workover during offshore drilling. Second, BN was applied to conduct risk analysis of offshore blowout. Based on these data, BN models were built. The prior probabilities with statistical probability method were calculated. The posterior probabilities during blowout were calculated using GeNIe software. The principal risk factors were identified by comparing them with prior probabilities. Shallow gas and abnormal high pressure were the principal risk factors of primary well control failure. Poor cementing and blowout preventer (BOP) failure were that of secondary well control failure. BOP failure is one of the main reason for blowout. Then, the risks of subsea and surface BOP failure were analyzed, combining with BN and Standardized Plant Analysis Risk Human Reliability Analysis Method. According to ExproSoft BOP failure data, the posterior probabilities with the concerning of component failure and human error were calculated. The principal factors were identified. This method provides greater value than the previous models since it can consider the complicated characteristics of geological condition, the whole offshore drilling, completion and workover technologies and operations, surface and subsea BOP common cause failures.