Risk assessment of rare events

Abstract

Rare events often result in large impacts and are hard to predict. Risk analysis of such events is a challenging task because there are few directly relevant data to form a basis for probabilistic risk assessment. Due to the scarcity of data, the probability estimation of a rare event often uses precursor data. Precursor-based methods have been widely used in probability estimation of rare events. However, few attempts have been made to estimate consequences of rare events using their precursors. This paper proposes a holistic precursor-based risk assessment framework for rare events. The Hierarchical Bayesian Approach (HBA) using hyper-priors to represent prior parameters is applied to probability estimation in the proposed framework. Accident precursor data are utilized from an information theory perspective to seek the most informative precursor upon which the consequence of a rare event is estimated. Combining the estimated probability and consequence gives a reasonable assessment of risk. The assessed risk is updated as new information becomes available to produce a dynamic risk profile. The applicability of the methodology is tested through a case study of an offshore blowout accident. The proposed framework provides a rational way to develop the dynamic risk profile of a rare event for its prevention and control.