Enhancing External Corrosion Direct Assessment With Machine Learning

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Enhancing External Corrosion Direct Assessment With Machine Learning

Enhancing External Corrosion Direct Assessment With Machine Learning
Enhancing External Corrosion Direct Assessment With Machine Learning

Operators need to keep their pipelines fit for purpose, maximize life and control costs. External corrosion is one of the main threats faced by operators, costing millions annually in identification, mitigation and repair. Although many methods exist to model the growth of corrosion features, the situation is often most complicated for “unpiggable” pipelines.

Where in-line inspection (ILI) is not possible, knowledge-based models reliant on data and assumptions for multiple variables are used. Combining the variables that are believed to contribute to corrosion is known as external corrosion direct assessment (ECDA). However, ECDA can often require multiple iterations of costly excavations to get right!

This paper discusses the use of the ROSEN Virtual-ILI (V-ILI) tool to enhance the ECDA process and demonstrates where V-ILI was used as part of the ECDA process to provide additional input data and higher confidence without the need for further excavations.

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