Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods

Abstract

Corrosion in oil and gas pipelines results in billions of dollars in annual losses and poses serious safety, environmental, and operational risks. Conventional detection methods are often labor-intensive, intermittent, and insufficiently accurate, hindering early identification of critical degradation. This study proposes an intelligent, SCADA-driven corrosion detection framework that integrates advanced machine learning (ML) and artificial intelligence (AI) techniques for real-time pipeline integrity monitoring. Operational parameters, including gas and liquid flow rates (up to 250 m³/h), pressure drops (0.3–0.8 MPa), and phase densities (600–900 kg/m³), were acquired from SCADA systems, pre-processed in MATLAB, and divided into training (30%), validation (30%), and testing (40%) datasets. Supervised ML models, Support Vector Machines (SVM), Random Forests, Boosted Trees, and Neural Networks, were optimized through feature selection and hyperparameter tuning for corrosion detection and rate prediction. The Linear SVM achieved the highest performance, with a ROC-AUC of 0.96 and a prediction tolerance of ± 0.02 mm/year. The integrated SCADA–AI framework achieved detection accuracies above 90%, enabling proactive maintenance scheduling and extending pipeline service life by 12–15%. These findings demonstrate a scalable, data-driven approach for predictive corrosion management and enhanced asset reliability in energy infrastructure.