Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xvii 7. Preface - p. xix 8. Acknowledgment - p. xxvii 9. Chapter 1: Unsupervised outlier detection techniques for well logs and geophysical data - p. xxvii 10. Chapter 2: Unsupervised clustering methods for noninvasive characterization of fracture-induced geomechanical alterations - p. 37 11. Chapter 3: Shallow neural networks and classification methods for approximating the subsurface in situ fluid-filled pore size distribution - p. 64 12. Chapter 4: Stacked neural network architecture to model the multifrequency conductivity/permittivity responses of subsurface shale formations - p. 101 13. Chapter 5: Robust geomechanical characterization by analyzing the performance of shallow-learning regression methods using unsupervised clustering methods - p. 127 14. Chapter 6: Index construction, dimensionality reduction, and clustering techniques for the identification of flow units in shale formations suitable for enhanced oil recovery using light-hydrocarbon injection - p. 155 15. Chapter 7: Deep neural network architectures to approximate the fluid-filled pore size distributions of subsurface geological formations - p. 181 16. Chapter 8: Comparative study of shallow and deep machine learning models for synthesizing in situ NMR T2 distributions - p. 217 17. Chapter 9: Noninvasive fracture characterization based on the classification of sonic wave travel times - p. 241 18. Chapter 10: Machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking - p. 287 19. Chapter 11: Generalization of machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales - p. 314 20. Chapter 12: Characterization of subsurface hydrocarbon/water saturation by processing subsurface electromagnetic logs using a modified Levenberg-Marquardt algorithm - p. 338 21. Chapter 13: Characterization of subsurface hydrocarbon/water saturation using Markov-chain Monte Carlo stochastic inversion of broadband electromagnetic logs - p. 368 22. Index - p. 403
Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xvii 7. Preface - p. xix 8. Acknowledgment - p. xxvii 9. Chapter 1: Unsupervised outlier detection techniques for well logs and geophysical data - p. xxvii 10. Chapter 2: Unsupervised clustering methods for noninvasive characterization of fracture-induced geomechanical alterations - p. 37 11. Chapter 3: Shallow neural networks and classification methods for approximating the subsurface in situ fluid-filled pore size distribution - p. 64 12. Chapter 4: Stacked neural network architecture to model the multifrequency conductivity/permittivity responses of subsurface shale formations - p. 101 13. Chapter 5: Robust geomechanical characterization by analyzing the performance of shallow-learning regression methods using unsupervised clustering methods - p. 127 14. Chapter 6: Index construction, dimensionality reduction, and clustering techniques for the identification of flow units in shale formations suitable for enhanced oil recovery using light-hydrocarbon injection - p. 155 15. Chapter 7: Deep neural network architectures to approximate the fluid-filled pore size distributions of subsurface geological formations - p. 181 16. Chapter 8: Comparative study of shallow and deep machine learning models for synthesizing in situ NMR T2 distributions - p. 217 17. Chapter 9: Noninvasive fracture characterization based on the classification of sonic wave travel times - p. 241 18. Chapter 10: Machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking - p. 287 19. Chapter 11: Generalization of machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales - p. 314 20. Chapter 12: Characterization of subsurface hydrocarbon/water saturation by processing subsurface electromagnetic logs using a modified Levenberg-Marquardt algorithm - p. 338 21. Chapter 13: Characterization of subsurface hydrocarbon/water saturation using Markov-chain Monte Carlo stochastic inversion of broadband electromagnetic logs - p. 368 22. Index - p. 403
