Magnetic flux leakage in-line inspection data analysis and integration

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The non-destructive inspection of oil & gas pipelines is essential for pipeline integrity management and criticalfor environmental protection from pipeline leakage and failure. The in-line inspection (ILI) with magnetic fluxleakage (MFL) method is a key technology. The use of differently configured inspection systems, such as axialMFL and circumferential MFL, can offer a comprehensive assessment of pipeline conditions. However, theuncertainty associated with inspection results remains a challenge for maintenance decision making due to theinherent heterogeneities with varied inspection techniques.This research project is to develop machine learning algorithms to identify the consistency between the ILI databy exploring the common feature patterns from multiple inspections. A comprehensive evaluation with leastuncertainty can be achieved by integrating multi-modal in-line inspection data. Thus, this research will enableefficient pipeline integrity management for pipeline operation. The research outcomes will be in the form ofsoftware tools, which can be delivered to the industrial partner for commercializing and better serving theneeds of Canada's oil & gas industry.