Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey

Abstract

The applicability of intelligent techniques for the safety assessment of oil and gas pipelines is investigated in this study. Crude oil and natural gas are usually transmitted through metallic pipelines. Working under unforgiving environments, these pipelines may extend to hundreds of kilometers, which make them very susceptible to physical damage such as dents, cracks, corrosion, etc. These defects, if not managed properly, can lead to catastrophic consequences in terms of both financial losses and human life. Thus, effective and efficient systems for pipeline safety assessment that are capable of detecting defects, estimating defects sizes, and classifying defects are urgently needed. Such systems often require collecting diagnostic data that are gathered using different monitoring tools such as ultrasound, magnetic flux leakage, and Closed Circuit Television (CCTV) surveys. The volume of the data collected by these tools is staggering. Relying on traditional pipeline safety assessment techniques to analyze such huge data is neither efficient nor effective. Intelligent techniques such as data mining techniques, neural networks, and hybrid neuro-fuzzy systems are promising alternatives. In this paper, different intelligent techniques proposed in the literature are examined; and their merits and shortcomings are highlighted.