Artificial intelligence-based corrosion management and failure prediction in oil and gas facilities

Corrosion of carbon steel equipment and piping systems remains one of the most critical integrity challenges in oil and gas facilities. The continuous demand for safer operations, reduced maintenance costs, and improved asset reliability has increased the need for advanced corrosion management techniques. This presentation explores the integration of Artificial Intelligence (AI) with corrosion engineering practices to enhance corrosion prediction, monitoring, and decision-making. The study discusses how AI and machine learning models can utilize historical inspection data, corrosion monitoring results, operating conditions, chemical treatment records, and environmental parameters to identify corrosion patterns and predict future degradation rates. By analysing large datasets, AI tools can support early detection of corrosion risks, optimize inspection intervals, and improve Risk-Based Inspection (RBI) strategies.

Common corrosion mechanisms including CO₂ corrosion, microbiologically influenced corrosion (MIC), corrosion under insulation (CUI), and localized corrosion are evaluated with an AI-assisted approach. The application of predictive analytics helps engineers move from reactive maintenance toward proactive integrity management by identifying potential failures before they occur.

The presentation also highlights challenges associated with implementing AI in corrosion control, including data quality, model reliability, and the importance of combining AI results with engineering judgment. The integration of AI with traditional corrosion monitoring techniques, such as corrosion probes, inspection data, and failure analysis, provides a powerful approach to improving asset performance and reducing operational risks. AI-driven corrosion management represents a significant advancement in the oil and gas industry by enabling smarter decisions, extending equipment life, and enhancing safety and reliability.