Risk coupling analysis of subsea blowout accidents based on dynamic Bayesian network and NK model

Risk analysis of subsea blowout accidents is critical to well control strategies and the overall safety of offshore drilling operations. Therefore, the interactive relationships among different types of risk factors should not be neglected. This paper proposes a novel method to quantify risk coupling of subsea blowout accidents based on dynamic Bayesian network (DBN) and NK model. First, causation of subsea blowout accidents is analyzed and risk factors are classified. Second, the types of risk coupling caused by human factors, electrical factors, hydraulic factors and mechanical factors are defined. Third, the DBN model is developed based on risk analysis of subsea blowout accidents and its NK model. Forth, parameters of risk coupling nodes in the developed DBN are determined by the calculation results from NK model. Finally, the developed model is validated by a three-axiom-based method. Dynamic characteristics of risk evolution and risk coupling of the subsea blowout accidents could be described by the developed DBN. In addition, sensitivity analysis of risk coupling types is performed and influences of risk factors are quantified by mutual information. With the developed model, uncertainty analysis is perform to research the effects of failure rates of risk factors on main risk coupling types.