Evidential Reasoning-Based Condition Assessment Model for Offshore Gas Pipelines

Condition assessment of oil and gas pipelines is a significant component in pipeline operations and maintenance. Such assessments are used to ensure better decisions for repair and/or replacement to reduce pipelines' failure possibilities. Therefore, it is essential to have an effective condition assessment model for pipelines as their failure incidents may lead to catastrophic, economical, and environmental consequences. Current practices of assessing gas pipelines condition can be considered simplified for the intended purpose. They mainly depend on experts' opinions in interpreting inspection data, where the process is influenced by human subjectivity and reasoning uncertainty. In other words, they need detailed knowledge on the translation of raw inspection data into valuable information. This will surely lead to decisions lacking thorough and extensive review of the most influential aspects on pipelines' conditions. To address the weaknesses of current practices, this research proposes a new fuzzy-based methodology that utilizes an integrated analytic network process (ANP) and hierarchical evidential reasoning (HER) to develop a meticulous condition assessment model for offshore gas pipelines. The proposed model is validated using historical inspection reports that are obtained from a local pipeline operator in Qatar. The model delivers satisfactory outcomes in assessing offshore gas pipelines' conditions based on real field data.