Description: This white paper will showcase a machine-learning 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