UBC Faculty Research and Publications

Internal Corrosion Hazard Assessment of Oil & Gas Pipelines Using Bayesian Belief Network Shabarchin, Oleg; Tesfamariam, Solomon


A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia, Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To facilitate the need, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment tool for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline’s mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF parameters have been obtained and visualized with the aid of the Geographic Information System. The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.

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