Risk Assessment of Polar Drillship Operations Based on Bayesian Networks

In the extreme polar marine environment, safety risks pose a significant threat to drilling vessels. By conducting a safety risk assessment, potential hazards can be predicted and identified, thereby significantly reducing the frequency of accidents and promoting the sustained stability of economic activities. This paper investigates a Bayesian-network-based risk assessment model for polar drilling operations. Grey relational analysis was employed to identify the main risk factors. The model is trained using 525 valid incident sample data and is combined with expert knowledge. The accuracy rate is above 88%. Additionally, corresponding decision-making recommendations are provided through sensitivity analysis. The three most sensitive elements to fire nodes are human error, other causes, and equipment damage, with sensitivity coefficients of 0.046, 0.042, and 0.022, respectively. In terms of deck/handrail collision nodes, the highly sensitive elements are related to lifting (totally more than 0.1). For the events that have already transpired, the probabilities of most related nodes are 0.73 and 0.74, both of which are above 0.5, thereby validating the accuracy of forward and backward reasoning. Risk assessments based on Bayesian networks can offer pertinent decision-making recommendations and preventive measures.