Description: This white paper will showcase a machine-learning (ML) approach to predict areas and the extent of metal loss corrosion in an effort to quantify qualitative risk factors such as prevention and mitigation (P&M) activities. The results showed promise with high accuracy and 90% confidence for axial location and depth of both internal and external metal loss anomalies. This, in turn, combined with the corrosion growth analysis can help pipeline operators develop robust, yet accurate long-term mitigation plans for their pipeline assets while prioritizing the risk-reduction achieved by implementing additional P&M measures. Supporting cases are discussed to help explain the intended use of this algorithm and the interpretation of the results.
Date Published: June 10, 2022
Lead Author: Mike Westlund, Principal Consultant
Author: Parth Iyer, Sr. Pipeline Integrity Engineer
Author: Wei Lie, PhD Student, University of Calgary