Abstract

Condition assessment of offshore gas pipelines is a key player in pipeline operations and maintenance. They are used to ensure better decisions for repair and/or replacement and reduce failure possibilities. Information obtained from pipelines assessments are regularly used for scheduling upcoming maintenance and inspection activities. Therefore, it is valuable to have effective condition assessment of pipelines because failure incidents could lead to catastrophic economical and environmental consequences. Furthermore, current practices of assessing gas pipelines condition are considered too primitive and simplified. They mainly depend on experts' opinions in interpreting inspection data where the process is influenced by the human subjectivity and reasoning uncertainty. In another way, they need the detailed knowledge on 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 condition.

To redress the weaknesses of the current practices and promote the performance of assessing offshore gas pipelines condition, this research proposes a new fuzzy-based methodology that utilizes hierarchical evidential reasoning (HER) for meticulous condition evaluation under subjectivity and uncertainty. The principle behind the posed structure is to establish an enhanced mechanism for the aggregation of different evidence bodies at multiple hierarchical levels in order to attain a reliable and exhaustive pipeline condition assessment. The essential characteristics of the proposed methodology are recapped in the following points. Firstly, the new approach suggests a more comprehensive hierarchy of the most influential factors affecting pipeline condition under three categories: physical, external, and operational. Secondly, this methodology is designed to consider the relative importance weights of all assessment factors in the hierarchy and to account for interdependencies among compared attributes. Thirdly, a hierarchical belief structure that utilizes evidential reasoning and fuzzy set theory is applied to grasp the uncertainty in pipeline evaluation. A model that utilizes HER can help combine different bodies of evidence at different hierarchical levels using Dempster-Shafer (D-S) rule of combination to obtain a detailed pipeline assessment. Fourthly, a condition assessment scale associated with rehabilitation actions is introduced as a framework for professionals to plan for future inspection and rehabilitation works. Finally, an automated, user-friendly, tool is developed for the propounded model to assess pipeline condition. Multiple sources of data were reached to provide a reliable assessment of pipe condition through the use of a structured questionnaire distributed among professionals in oil and gas industry in the studied region. This proposed model is compared and validated with historical inspection reports that were obtained from a local pipeline operator in Qatar. It is found that this model delivers satisfactory outcomes and forecasts offshore gas pipeline condition with an Average Validity Percent (AVP) of 97.6%.

The developed fuzzy-based methodology is believed to offer a reliable condition assessment that optimizes data interpretation and usage of structured algorithms. Additionally, the introduced model and tool are compatible to researchers and practitioners such as pipeline engineers and consultants in order to prioritize inspection and rehabilitation for existing offshore gas pipelines. This immensely pictures the essence of infrastructure management to ameliorate cost and time optimization.