Microscopy, Image Processing and Artificial Intelligence for Analyzing the Impact of Corrosion on Oil and Gas Pipelines: A Systematic Review

Pipeline transportation systems (PTS) are widely used maritime and land-based means of transport across several industries, including oil and gas, valued for being reliable, economical, and fast. However, operational instability from multiple interacting variables directly affects the mechanical integrity (MI) of these systems through internal and external corrosion (C). Addressing this problem increasingly relies on microscopic analysis (MA), image processing (IP), and artificial intelligence (AI). Together, these support a horizontal approach to reducing uncertainty and subjectivity in identifying, controlling, and monitoring corrosion-related risk factors. Following the PRISMA 2020 guidelines, this work presents a systematic literature review (SLR) of corrosion-related publications in oil and gas PTS spanning 1987–2025. From 1,085 records identified across two databases, 279 studies were retained after duplicate removal and quality assessment. Of these, 141 provide openly available data. A weighted evaluation matrix scoring twenty representative works against sixteen criteria across four thematic clusters—corrosion type, microscopy, image processing, and AI—shows that the number of jointly satisfied criteria ranges from 3 to 12 (mean 5.85 of 16). This indicates that integrated, multi-technique, AI-supported corrosion assessment remains uncommon across three decades of literature. Beyond consolidating the state of the art into a reusable evaluation instrument, this review establishes the theoretical and bibliometric foundation for an applied, multi-part research line that operationalizes several of these clusters on a real pipeline case study.