Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis

Comprehensive Overview: Explores the latest advancements and applications of machine learning in subsurface energy resource management, covering topics such as reservoir characterization, production optimization, and risk assessment. State-of-the-Art Techniques: Delves into cutting-edge machine learning algorithms and their application to real-world challenges in the energy industry, including deep learning, reinforcement learning, and unsupervised learning. Future Prognosis: Provides insights into emerging trends and future directions in machine learning for subsurface energy resource management, helping readers stay ahead of the curve. Practical Case Studies: Includes real-world case studies that demonstrate the successful implementation of machine learning techniques in various aspects of subsurface energy resource management.* Expert Contributors: Written by leading researchers and practitioners in the field, ensuring the accuracy and reliability of the information presented.