Advanced Corrosion Monitoring and Predictive Integrity Assessment of Oil and Gas Pipelines in Saudi Arabia: Integrating Risk-Based Inspection, Degradation Modeling, and Preventive Maintenance

Saudi Arabia's oil and gas pipeline systems operate across environments where saline soils, coastal exposure, variable water handling, and long asset lives can complicate corrosion control. Although inspection, corrosion prediction, and maintenance optimisation have each progressed, their integration into defensible operational decisions remains uneven. This critical review evaluates evidence published between 2020 and 2025 on pipeline monitoring, degradation modelling, risk-based inspection, and preventive maintenance, with emphasis on transferability to Saudi operating conditions. A structured thematic synthesis distinguishes direct measurements from inferred corrosion states, compares deterministic, probabilistic, and machine-learning approaches, and examines how uncertainty should influence intervention timing. The evidence indicates that high-resolution inspection alone cannot establish future integrity without sound defect matching, exposure histories, and calibrated growth models. Likewise, apparently accurate predictions can be misleading when validation ignores temporal dependence, geographic transfer, measurement error, or rare severe defects. This review develops an integrated evidence-to-decision framework linking sensing, data assurance, mechanism-informed forecasting, failure probability, consequences, and maintenance selection. A staged implementation pathway is proposed for operators managing heterogeneous pipeline networks and unequal data maturity. The central argument is that predictive integrity should be evaluated through decision reliability and risk reduction rather than predictive accuracy alone. The framework is conceptual; it does not report newly collected Saudi pipeline measurements or claim field-validated performance.