Corrosion remains one of the most costly and persistent challenges in the oil and gas industry—silently eroding infrastructure, compromising safety, and draining billions of dollars annually from operations worldwide. Despite decades of engineering advancements, traditional corrosion management practices still rely heavily on reactive strategies, periodic inspections, and delayed corrective actions—approaches that are no longer sufficient in today’s high-risk, high-demand energy landscape.

AI Techniques for Materials Corrosion Management in the Oil and Gas Industry delivers a transformative shift in how corrosion is understood, predicted, and controlled. This book introduces a new paradigm—where artificial intelligence and machine learning enable predictive, data-driven decision-making that anticipates failures before they occur.

Bridging the gap between conventional engineering practices and cutting-edge digital innovation, this book provides a comprehensive and practical roadmap for integrating AI into corrosion and asset integrity management. Readers will discover how advanced analytics, real-time data, and intelligent algorithms can significantly enhance reliability, reduce downtime, optimize maintenance strategies, and improve overall operational efficiency.

More than just a technical guide, this book is a strategic resource for engineers, researchers, and industry leaders seeking to modernize corrosion management systems and future-proof their operations. By replacing reactive approaches with proactive intelligence, it empowers organizations to mitigate risk, improve safety, and unlock new levels of performance in energy infrastructure.

Dr. Muhammad Hussain is a distinguished professional specializing in asset integrity, reliability engineering, and artificial intelligence, with a distinguished career marked by seminal research, innovative solutions, and industry leadership in the energy sector. Through his pioneering work, he has successfully integrated data-driven methodologies, machine learning, and predictive analytics to revolutionize corrosion management and pipeline integrity practices, thereby bridging the divide between traditional engineering approaches and cutting-edge digital technologies. This synergy enables energy companies to proactively anticipate potential failures, optimize maintenance strategies, and substantially mitigate operational risks.

Dr. Tieling Zhang is currently serving as the director of the Engineering Asset Management and Systems Engineering Research Group at the Faculty of Engineering and Information Sciences, University of Wollongong, Australia. His group has developed its capabilities to provide research and training in the areas of railway asset management, system reliability and maintenance, supply chain management and optimization, condition-based maintenance and operation of wind turbines, rail track modeling, railway vibration and noise, risk assessment of railway systems, and battery health management.

Dr. Aziza Chakir is a Senior Lecturer, Keynote Speaker and currently serves as a researcher professor in the university Hassan II. Her research interests include in the area of Computer Engineering, Information Systems, Digital culture, Software Engineering, Knowledge Transfer Technology, IT Governance, Cloud Computing, Suitable Development (Green IT..), Machine Learning, Big Data, Data mining. She is currently working on many projects such as "Green computing" and "Ecological frameworks". Her disciplines include Architectural Engineering Information Technology, Politics Information Systems (Business Informatics) and Artificial Intelligence Distributed Computing. Her skills and expertise fall into the fields of Risk Management, Business Intelligence, IT Governance, Corporate Governance, Information Technology, Cloud Computing, and E-Government.