Research on selective seam weld corrosion (SSWC) is contributing to improve the integrity management of pipelines, and better understand and mitigate localized corrosion mechanism that compromise structural reliability and lead to catastrophic failures. One of the main challenges in meeting these requirements is reliably differentiating SSWC from general corrosion that intersect or cross the longitudinal weld. Traditional approaches often use a grooving factor threshold (typically ≥ 2.0) to flag selectivity. Typically, in-line inspection (ILI) service providers do not offer detailed characterization of the grooves formed by SSWC. Evaluating the grooving factor is essential for accurate fitness-for-service assessments and remaining life predictions. This ongoing research investigates a methodology for estimating the grooving factor using high-resolution magnetic flux leakage–circumferential (MFL-C) ILI signal data, validated against a comprehensive database of field-verified samples. By integrating grooving factor analysis with ILI data and incorporating classification criteria defined in API 1176, the proposed approach aims to enhance anomaly classification accuracy, minimize false positives, and improve the probability of identification (POI) for SSWC. By refining grooving factor models and evaluation, and improving risk-based inspection strategies to support more informed integrity management decisions, ensure compliance with regulatory standards, ultimately enhancing pipeline safety and operational efficiency.
