Risk identification of third-party damage on oil and gas pipelines through the Bayesian network

This paper aims to identify the risks influencing oil and gas (O&G) pipeline safety caused by third-party damage (TPD). After comprehensive literature study, we found that the traditional risk identification of TPD suffers from defining binary states of risk only and ignores the risk factors after pipeline failure. To overcome this problem, we investigated incident reports to identify previously unrecognized additional factors. This work also developed a graphic model by using Bayesian theory to cope with the multistate risks arising from third parties and to present the incident evolution process explicitly. Furthermore, this paper included a leakage case study conducted to verify the logicality of this model. The results of case study inspire that the proposed methodology can be used in a dual assurance approach for risk mitigation, namely learning from previous incidents and continuously capturing new risk information for risk prevention.