Hierarchical Bayesian model for failure analysis of offshore wells during decommissioning and abandonment processes

Risk analysis of offshore wells decommissioning, and abandonment processes is challenging due to limited life-cycle information of the well, and failure data of safety barriers in place. To this end, it is essential to capture and implement the variability associated with the sparse data for conducting risk analysis with considerable confidence level. The hierarchical Bayesian analysis provides a viable alternative to address the uncertainty of the data through aggregation for each causation. Bayesian network, through its robust computation engine, is used to define dependence of causations and uses Bayes' theorem to update the analysis as new information becomes available. In addition, the Bayesian network helps to represent complex dependencies among causations through appropriate relaxation strategy to minimize uncertainty in the data, link parameter of interest, and overall accident scenario modelling. This paper presents the integration of Hierarchical Bayesian model with a Bayesian network to conduct the risk analysis of well decommissioning and abandonment processes. The proposed methodology is illustrated using a well plugging and abandonment operational failure reported by the Department of Mineral Management Service (MMS). The results demonstrate the potential of the proposed approach as a robust means to study complex well decommissioning activities.