Application of Machine Learning Approaches to Prediction of Corrosion Defects in Energy Pipelines

The integrity of energy pipelines is crucial for assuring the safe and reliable transportation of resources. Corrosion defects significantly threaten pipeline infrastructure, necessitating advanced predictive maintenance strategies. The energy industry grapples with significant financial losses attributed to corrosion, prompting a crucial need for accurate prediction and prevention measures. This book chapter amalgamates diverse studies that delve into the transformative role of artificial intelligence (AI) and machine learning (ML) in the oil and gas sector, specifically focusing on the energy pipelines sector. Furthermore, the chapter analyzes how artificial intelligence (AI) reshapes the oil and gas industry, particularly in the oil and gas pipeline domain. It discusses AI applications, algorithms, and data considerations, highlighting trends and potential future scenarios over the next few years. Non-technical challenges related to data, human factors, and collaborative models are also examined. The chapter culminates with a focus on artificial intelligence (AI) applications in oil and gas assets, including pipeline infrastructure development, elucidating its potential in predicting production dynamics, optimizing development plans, identifying residual oil, recognizing fractures, and enhancing oil recovery. A comprehensive literature review provides insights into existing AI algorithms, their pros and cons, and concludes with suggestions and potential directions for future AI applications in oil and gas development. This collective exploration underscores the transformative impact of artificial intelligence (AI) across multiple facets of the oil and gas industry, heralding a new era of efficiency, risk mitigation, and strategic decision-making. Overall, this chapter contributes to the advancement of predictive maintenance in energy pipelines, offers accuracy and precision, and identifies corrosion defects using advanced predictive analytics like machine learning (ML) and artificial intelligence (AI). Such ML-based corrosion prediction models can improve pipeline safety, reduce operational risks, and optimize maintenance schedules, ultimately contributing to energy transportation infrastructure's overall reliability and sustainability.