This paper explores the use of passive magnetic flux density (MFD) for the characterization of metal loss in steel pipelines. Since the early development of MFL tools in the 1960s, the ferromagnetic properties of steel pipelines have been harnessed to determine the condition of pipelines with continuous development improving the resolution of these tools. However, the capability of low-resolution MFD analysis to inspect pipeline condition remains underexplored.

The machine learning powered Pipers® inline inspection solution (Michael Byington 2022), employs MFD analysis to identify pipe joints with potential metal loss exceeding 30 % of the nominal wall thickness. This approach provides an integrity assessment of pipelines with sufficient resolution to understand pipeline wall condition at least equivalent to hydrostatic tests.

This document discusses the passive magnetic field's properties, the development of a model for axially magnetized pipes, and the influence of external magnetic fields and non-cylindrical geometries on the passive magnetic field. It then demonstrates how, using this understanding of the passive MFD, the Pipers® solution can function as a low-resolution inline inspection tool, aiding in the safe and compliant management of pipelines. Finally, the approach is validated through comparisons with an MFL inspection.

Conclusively, this work showcases a novel analytical method for using passive MFD to detect metal loss in pipelines. By identifying segments at risk and guiding maintenance decisions, this low-resolution technique complements traditional high-resolution ILI tools, offering a strategic approach to pipeline management that emphasizes safety, compliance, and optimized resource allocation.