Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems by David A. Wood is a practical, field-focused guide for geoscientists, reservoir engineers, and data scientists working in onshore and offshore basins worldwide. Clear, authoritative, and applied, this book bridges the gap between advanced algorithms and everyday subsurface challenges—seismic interpretation, well-log analysis, reservoir characterization, and petrophysical prediction.

Begin with a compelling overview of modern ML and DL techniques tailored to geology, then move into concrete workflows that demystify preprocessing, feature engineering, model selection, and uncertainty quantification. Through real-world case studies from diverse geological settings, readers learn how to translate noisy seismic and well data into robust predictions and interpretable models that support drilling, production, and exploration decisions.

Readers will appreciate the emphasis on interpretability and best practices: how to avoid overfitting in small datasets, incorporate geological constraints, and validate models against core and production data. The writing is technical yet accessible, making complex concepts practical for teams in exploration basins across North America, the Middle East, Europe, and Asia-Pacific.

Ideal for geologists, geophysicists, reservoir engineers, and analytics professionals seeking to apply machine learning responsibly to subsurface problems, this book equips you with the know-how to deploy models that deliver actionable insights. Enhance your modeling toolbox and improve subsurface decision-making with methods proven in the field.

Add this essential resource by David A. Wood to your library—order now to advance your subsurface analytics and bring data-driven clarity to complex geological problems.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.