The oil and gas supply chain (OGSC) is considered to have one of the most significant stakes in the U.S. economy because of its interconnectedness with supply chains in other sectors, such as health and medicine, food, heavy manufacturing, and services. While oil and gas development is expanding exponentially, various factors ranging from man-made to natural disasters can hinder OGSC processes, which, in turn, can result in inefficient and costly operations in other sectors. This study presents a Bayesian Network (BN) model to predict and assess disasters in the OGSC based on seven main factors: technical, economic, social, political, safety, environmental, and legal. BBN is a probabilistic graphical model that is predominantly used in risk analysis to illustrate and assess probabilistic relationships among different variables. To draw meaningful managerial insights into the proposed model, sensitivity analysis and belief propagation are used. The results indicate that of the seven factors responsible for OGSC disasters, technical factors have the highest impact while legal and political factors have the lowest.

石油和天然气供应链（OGSC）被认为是美国经济中最重要的股份之一，因为它与健康，医药，食品，重工业和服务业等其他部门的供应链相互关联。尽管石油和天然气的发展呈指数级增长，但从人为灾害到自然灾害等各种因素都可能阻碍OGSC流程，进而导致其他部门的效率低下和成本高昂。这项研究提出了一个贝叶斯网络（BN）模型来预测和评估基于七项主要因素在OGSC灾害：技术，EC经济，社会，政治，安全，环境和法律。BBN是一种概率图形模型，主要用于风险分析中，以说明和评估不同变量之间的概率关系。为了将有意义的管理洞察力引入建议的模型中，使用了敏感性分析和信念传播。结果表明，在造成OGSC灾难的七个因素中，技术因素影响最大，而法律和政治因素影响最小。