关键词: 系统理论事故建模与过程(STAMP)模型, 人为因素分析与分类系统(HFACS)模型, 油气管道, 事故原因, 分类模型, 社会网络分析

Abstract:

In order to improve the effectiveness of oil and gas pipeline accident prevention strategies, a classification model for the causes of oil and gas pipeline accidents was developed, and social network analysis was applied to the classification model. Firstly, the STAMP model and HFACS model were combined to get the control structure of oil and gas pipeline accident prevention, and then the causes of 35 oil and gas pipeline accidents at home and abroad were analyzed according to the control structure. The analysis results were coded using grounded theory to get the classification model for the causes of oil and gas pipeline accidents. Social network analysis methods were applied to construct a relationship network of factors related to oil and gas pipeline accidents, and core edge analysis, centrality analysis, and correlation direction index analysis were used to identify the core factors and factors with high correlation and strong influence in the oil and gas pipeline accident classification model. The research results show that the classification model for the causes of oil and gas pipeline accidents included 6 levels and 22 bottom cause factors, which are government and regulatory factors, third-party factors, operator organizational factors, operator unsafe supervision and the prerequisites for unsafe behavior of on-site personnel. Among the causal factors, the internal factors of the government and regulatory authorities, organizational factors of operators, unsafe supervision of operators, and third-party factors are core factors. System flaws, insufficient supervision, improper operation plans, third-party sabotage behavior, pipeline and weld defects, construction/repair/accessory issues, and skill errors are factors with high correlation and strong influence.

Key words: systems theoretic accident model and process (STAMP) model, human factors analysis and classification system (HFACS) model, oil and gas pipeline, accident causal factors, classification model, social network analysis