- Review
55 Pages
Pipeline transportation systems (PTS) move oil and gas reliably and economically, but internal and external corrosion threaten their mechanical integrity. This systematic review examines how microscopic analysis, image processing, and artificial intelligence (AI) have been used to identify, monitor, and predict corrosion in oil and gas PTS. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, we searched Scopus and Web of Science for English- and Spanish-language records from 1987 to 2025, with the last search in August 2026. Of 1085 records, we removed 270 duplicates and excluded 536 that failed three quality-assessment questions. This left 279 studies from 1987 to 2024 for narrative synthesis. We did not apply a formal risk-of-bias tool. AI-based modeling was the largest cluster with 152 studies, followed by corrosion processes (57), microscopy (40), and image processing (30). A total of 141 studies provided open data. Monte Carlo simulation and Bayesian networks were the most commonly applied AI methods to real pipelines, appearing in 82% and 72% of studies, respectively. A sixteen-criterion matrix applied to twenty representative works showed that they met 3 to 12 criteria, with a mean of 5.85. Only two studies covered all four clusters. This result was robust to cluster-weighted scoring (Spearman ρ = 0.94). Integrated, AI-supported corrosion assessment remains uncommon. Limitations include using only two databases, consensus-based screening, and no meta-analysis. The review was not registered and received no external funding.
Corros. Mater. Degrad.
6 November 2026


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