Dynamic risk analysis of emergency operations in deepwater blowout accidents

The risk in emergency operations can amplify accident losses and threaten the safe achievement of emergency response objectives. In this paper, by considering the dynamic characteristics of emergency operations, we propose an integrated model of estimating the probability of emergency failure by integrating fault tree (FT), dynamic Bayesian network (DBN), and fuzzy set theory. In the hybrid model, FT is utilized to identify the risk influencing factors of emergency operations. DBN is applied to capture the dynamic features in the emergency process. In presence of limited prior knowledge, the fuzzy set theory is employed to determine the prior probabilities of the root nodes. The methodology is utilized to evaluate the risk of oil recovery operations in the deepwater blowout accident. Particularly, we assessed the dynamic risk of lowering, installation and cutting of emergency equipment, as well as the formation of gas hydrate. The risk-influencing factors of emergency operations and their correlations are identified. The influence of the priority order of the process on the emergency operation is expounded. Eventually, a DBN-based emergency operation model for the deepwater blowout is developed. The model captures the spatial variability of parameters and simulates the evolution of emergency operations over time and space. The mutual information is utilized to conduct sensitivity analysis and diagnostic reasoning on the model.

Automated summary

This paper proposes an integrated model combining fault tree, dynamic Bayesian network, and fuzzy set theory for estimating the probability of emergency failure. The model is used to evaluate the risk of emergency operations in deepwater blowout accidents, identify risk factors, and improve emergency response capabilities.