Supervised dynamic probabilistic risk assessment of complex systems, part 2: Application to risk-informed decision making, practice and results

Abstract

One challenge that has received attention in maritime industry is assessing the risk level of dynamic positioning (DP) systems in emergency situations. Statistics from recent years have shown that the risk level of some DP operations is above the industry's risk criteria. Operators have a significant impact on incidents’ consequences by making responsive decisions. In emergencies, one is afforded little time to make a decision. Available risk models are not efficient enough to provide systems’ risk level in a short period of time.

In this study, the application of a new supervised methodology to assist decision making in emergencies is proposed. This method significantly reduces the processing and execution time of a system's probabilistic risk assessment models. In this methodology, the most probable failure scenarios are generated using an optimization model. The objective of the optimization model in this study is to find scenarios with the highest occurrence probabilities. The constraints are a system's dynamic simulation and its risk model. The proposed method is applied to three incidents that occurred in the Norwegian offshore sector in previous years. The results show that the model can predict the most probable scenarios with an acceptable accuracy in a very short time.