This book presents a comprehensive and accessible guide to how data, mathematical modeling, control engineering, and intelligent systems work together to create safer, more efficient, and increasingly autonomous drilling operations. Modern drilling operations are undergoing a profound transformation driven by digital technology, automation, and artificial intelligence. 

Rather than treating drilling as a purely mechanical process, the book introduces a systems-based perspective. It explains how raw sensor data is converted into reliable information, how engineering models are developed and validated, and how advanced control strategies such as PID, optimal control, and model predictive control are implemented in real time. Building on this foundation, the book explores modern developments including digital twins, machine learning, reinforcement learning, and AI-assisted decision support.

A distinctive feature of the book is its layered architecture approach, which clearly connects data quality, modeling, control, optimization, and intelligent automation into a unified framework. Rich illustrations, structured workflows, practical examples, and carefully designed problems support understanding across disciplines.

By bridging traditional engineering with emerging AI technologies, this book provides readers with both foundational knowledge and a forward-looking roadmap toward intelligent and autonomous energy.

Dan Sui is a professor at the University of Stavanger, Norway, and an elected Fellow of the Norwegian Academy of Technological Sciences (NTVA). Her research focuses on drilling automation, digital drilling systems, artificial intelligence, modeling, and control for energy engineering. Professor Sui has extensive academic and industrial experience in drilling automation and intelligent drilling systems. Her research spans drilling dynamics, real-time monitoring, automated drilling control, managed pressure drilling, drilling robotics, digital twins, physics-informed artificial intelligence, engineering decision support, and autonomous drilling systems. She is particularly interested in integrating first-principles engineering models with artificial intelligence to develop reliable, explainable, and autonomous solutions for complex drilling operations. She has led and participated in numerous collaborative research projects with universities, research institutes, and major energy companies. Her work has resulted in a broad portfolio of scientific publications, industrial innovations, and practical solutions for drilling automation and digital transformation.