Risk analysis of emergency operations in presence of limited prior knowledge

The risk of emergency operations can lead to the expansion of accident losses. The emergency process can introduce uncertain factors, and the resulting risk can threaten the safety realization of the emergency goal. By considering the characteristics of emergency operations, we proposed a methodology to assess the risk of emergency operations. In the methodology, Bayesian network is applied to capture the risk characteristics in the emergency process. Fault tree is utilized to depict the reason for emergency failure. Fuzzy set theory is employed to determine the prior probabilities of the root nodes in presence of limited prior knowledge. The methodology was applied to the emergency operations in the deepwater blowout accident. The risk-influencing factors of emergency operations and their correlations were identified. In emergency operations, fault tree is used to assess the risk of the lower process. A Bayesian network-based emergency operation model for the deepwater blowout is developed. The model captures the variability of parameters and simulates the evolution of emergency operations over time, with probabilistic updates based on field observations. The mutual information is also utilized to conduct sensitivity analysis and diagnostic reasoning on the model.