Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume.
1. Advances in Subsurface Data Analytics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front Matter - p. Page iv 6. Copyright - p. Page v 7. CONTENTS - p. Page vi 8. Contributors - p. Page vii 9. About the Editors - p. Page viii 10. Preface - p. Page ix 11. Acknowledgments - p. Page x 12. List of Illustrations - p. Page xi 13. List of Tables - p. Page xii 14. PART 1 : Traditional machine learning approaches - p. Page xii 15. Chapter 1 : User vs. machine-based seismic attribute selection for unsupervised machine learning techniques: Does human insight provide better results than statistically chosen attributes? - p. Page xii 16. Chapter 2 : Relative performance of support vector machine, decision trees, and random forest classifiers for predicting production success in US unconventional shale plays - p. Page xii 17. PART 2 : Deep learning approaches - p. Page xii 18. CHAPTER 3 : Recurrent neural network: application in facies classification - p. Page xii 19. Chapter 4 : Recurrent neural network for seismic reservoir characterization - p. Page xii 20. Chapter 5 : Convolutional neural networks: core interpretation with instance segmentation models - p. Page xii 21. Chapter 6 : Convolutional neural networks for fault interpretation – case study examples around the world - p. Page xii 22. PART 3 : Physics-based machine learning approaches - p. Page xii 23. Chapter 7 : Applying scientific machine learning to improve seismic wave simulation and inversion - p. Page xii 24. Chapter 8 : Prediction of acoustic velocities using machine learning and rock physics - p. Page xii 25. Chapter 9 : Regularized elastic full-waveform inversion using deep learning - p. Page xii 26. Chapter 10 : A holistic approach to computing first-arrival traveltimes using neural networks - p. Page xii 27. PART 4 : New directions - p. Page xii 28. Chapter 11 : Application of artificial intelligence to computational fluid dynamics - p. Page xii 29. Index - p. Page xii
Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume.
1. Advances in Subsurface Data Analytics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front Matter - p. Page iv 6. Copyright - p. Page v 7. CONTENTS - p. Page vi 8. Contributors - p. Page vii 9. About the Editors - p. Page viii 10. Preface - p. Page ix 11. Acknowledgments - p. Page x 12. List of Illustrations - p. Page xi 13. List of Tables - p. Page xii 14. PART 1 : Traditional machine learning approaches - p. Page xii 15. Chapter 1 : User vs. machine-based seismic attribute selection for unsupervised machine learning techniques: Does human insight provide better results than statistically chosen attributes? - p. Page xii 16. Chapter 2 : Relative performance of support vector machine, decision trees, and random forest classifiers for predicting production success in US unconventional shale plays - p. Page xii 17. PART 2 : Deep learning approaches - p. Page xii 18. CHAPTER 3 : Recurrent neural network: application in facies classification - p. Page xii 19. Chapter 4 : Recurrent neural network for seismic reservoir characterization - p. Page xii 20. Chapter 5 : Convolutional neural networks: core interpretation with instance segmentation models - p. Page xii 21. Chapter 6 : Convolutional neural networks for fault interpretation – case study examples around the world - p. Page xii 22. PART 3 : Physics-based machine learning approaches - p. Page xii 23. Chapter 7 : Applying scientific machine learning to improve seismic wave simulation and inversion - p. Page xii 24. Chapter 8 : Prediction of acoustic velocities using machine learning and rock physics - p. Page xii 25. Chapter 9 : Regularized elastic full-waveform inversion using deep learning - p. Page xii 26. Chapter 10 : A holistic approach to computing first-arrival traveltimes using neural networks - p. Page xii 27. PART 4 : New directions - p. Page xii 28. Chapter 11 : Application of artificial intelligence to computational fluid dynamics - p. Page xii 29. Index - p. Page xii
