The most dependable and widely used techniques to inspect and manage pipelines external corrosion are In Line Inspection (ILI) for piggable pipelines and external corrosion direct assessment (ECDA) for non-piggable pipelines. But ILI is a lagging indicator, i.e., it provides information on corrosion after metal loss has happened and some aspects of ECDA may be considered as current indictors but overall, it is a lagging indicator. Therefore, it is essential to integrate lagging indicators with prediction techniques so that pipeline operators can take the required pre-emptive steps and implement corrective actions in a timely manner.

This paper reviews concepts and capabilities of various prediction techniques including 5M-Methodology, Machine Learning (ML), Artificial Intelligence (AI) and Deep Learning (DL) in managing external corrosion of oil and gas pipelines and describes the experience of implementing the 5-M Methodology in four (4) oil and gas pipelines.

Subject
Electric current, Corrosion rate, Laboratory data, Uniform corrosion rate, Walls, Pipelines, Corrosion losses, Corrosion growth, Corrosion modeling, Corrosion data, Case studies, External corrosion, Surveys
Keywords:
5-M methodology, corrosion management, corrosion effect, coatings, cathodic protection, artificial intelligence, machine learning
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2025
Association for Materials Protection and Performance (AMPP)
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