Machine Intelligence for Materials Science

Hussain, Muhammad; Zhang, Tieling; Chakir, Aziza

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.

Details

Published by: Springer

Publication Date: 2026-10-31

Format: Hardcover

ISBN-13: 9789819217878

DOI:

Dimensions: 235cm x155cm

Pages: