Dynamic occupational risk model for offshore operations in harsh environments

The expansion of offshore oil exploitation into remote areas (e.g., Arctic) with harsh environments has significantly increased occupational risks. Among occupational accidents, slips, trips and falls from height (STFs) account for a significant portion. Thus, a dynamic risk assessment of the three main occupational accidents is meaningful to decrease offshore occupational risks. Bow-tie Models (BTs) were established in this study for the risk analysis of STFs considering extreme environmental factors. To relax the limitations of BTs, Bayesian networks (BNs) were developed based on BTs to dynamically assess risks of STFs. The occurrence and consequence probabilities of STFs were respectively calculated using BTs and BNs, and the obtained probabilities verified BNs' rationality and advantage. Furthermore, the probability adaptation for STFs was accomplished in a specific scenario with BNs. Finally, posterior probabilities of basic events were achieved through diagnostic analysis, and critical basic events were analyzed based on their posterior likelihood to cause occupational accidents. The highlight is systematically analyzing STF accidents for offshore operations and dynamically assessing their risks considering the harsh environmental factors. This study can guide the allocation of prevention resources and benefit the safety management of offshore operations.