Hybrid Dynamic Bayesian network method for performance analysis of safety barriers considering multi-maintenance strategies

Abstract

Safety barriers play a critical role in preventing unintentional hydrocarbon flow leaking from reservoir to external environment or another formation during different offshore operation stages, and such a leakage has the potential to trigger cascading events and may lead to catastrophic consequences. The present study aims at the development of a hybrid DBN-based approach for dynamic performance analysis of safety barriers in the prevention of subsea downhole leakage incidents. Events in operation, such as different types of maintenances and process demand are taken into account to enhance the safety barrier performance. These factors could be analyzed by reflecting inspecting and repair activities of safety barriers with multistate-based multiphase Markov process. In order to obtain a dynamic and synthetic risk analysis of subsea downhole leakage, a dynamic Bayesian network-based model is proposed, incorporating the failure analysis of safety barriers and downhole multiple leakage pathways. Such analysis allows determining the dynamic risk characteristic of leakage events, and key safety barriers under different maintenance scenarios. Dynamic performance of such safety barriers is evaluated with respect to four aspects: preventive maintenance and imperfect repair, degradation effects, process demand and maintenance cost. The approach is tested through the application to a case study with an offshore oil and gas well. The results the importance of safety barrier performance in controlling the expected leakage scenarios.