A methodology for risk analysis of emergency process in deepwater blowout scenarios

For complex systems and their operations, risk analysis is of great significance yet encountered with data deficiency. This paper proposes a methodology for risk analysis based on virtual-reality-generated information, particularly for emergency process. First, the accident scenario is determined. Second, the corresponding virtual reality (VR) model is established and the original data is collected. Third, the data is augmented through deep generative models. Fourth, based on the enhanced data, risk analysis is performed by data-driven Bayesian networks. Finally, the proposed methodology is validated with the case study of emergency operations in deepwater blowout. The results show that the proposed methodology is feasible to deal with the data deficiency problem in risk analysis.