Failure assessment of oil and gas transmission pipelines using an integrated Bayesian belief network and GIS model

Abstract

Corrosion is one of the main reasons for pipeline failure in the oil and gas industry. Because a pipeline failure can result in serious personal injury, monetary loss, and environmental damage, pipeline operators need to make timely, and cost-effective decisions to prevent accidents in high consequence areas. The majority of models proposed in this area are designed and computed for individual pipe segments, with only a limited number of studies considering the system level. The system level pertains to spatially distributed pipelines that span hundreds of miles across different environmental conditions. The current study propose combining GIS and Bayesian belief network to determine the probability of failure of transmission pipelines due to external pitting corrosion. The model incorporates data from an extensive network of pipes spanning hundreds of miles and their surroundings to compute the failure probability of the pipeline infrastructure. The combination of spatial GIS capabilities with the reasoning capabilities of the Bayesian network provides a powerful tool to estimate the likelihood of transmission pipelines failing in a specific area, based on the available information.