Petroleum supply chain dynamic risk assessment using Bayesian network

•The creation of a dynamic risk assessment framework for the entire oil supply chain.•Quantitative risk analysis of the oil supply chain.•Identifying and assessing hazards over a longer time frame.•Using the Bayesian network, importance measures, and Markov chain Monte Carlo for risk assessment. The oil and gas supply chain encounters high-impact and low-probability risks (HILP). This supply chain should have the lowest probability of disruption and related risks to meet market demands. One of the risk assessment objectives is to calculate the probability of risks and determine which is more important. This study will help experts make better decisions to mitigate the consequences of risks. Static risk assessment is not efficient for handling HILP risks. After all, old-fashioned methods undermine these risks; therefore, in this study, a dynamic approach with a Bayesian network has been used to assess the probability of the oil and gas supply chain risks. The Iranian petroleum supply chain has been considered a case study, and information has been collected from experts. The most influential risks were identified as economic crisis, regulations/ political cost, organizational leadership, and IT issues.