Advances in Subsurface Data Analytics: Traditional and Physics-Based Machine Learning

Description

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.    Contributors CONTENTS About the Editors Acknowledgments Preface PART 1 - Traditional machine learning approaches Chapter 1 - User vs. machine-based seismic attribute selection for unsupervised machine learning techniques: Does human in ... 1.1 Introduction 1.2 Motivation 1.3 Dataset characteristics 1.4 Seismic attributes 1.5 Principal component analysis (PCA) 1.6 Self-organizing maps (SOMs): An unsupervised technique for seismic facies identification 1.7 Methodology 1.8 Results 1.8.1 Principal component analysis 1.8.2 From the eigenspectrum to principal components and attributes 1.9 Self-organizing maps analysis 1.10 SOM results and discussion from user-selected attributes vs. machine-derived inputs 1.11 Human vs. machine comparison (upsides and downsides of PCA vs multiattribute analysis) 1.11.1 Upsides of the approaches 1.11.2 Downsides and limitations 1.12 Recommendations 1.13 Conclusion 1.14 Acknowledgments References Chapter 2 - Relative performance of support vector machine, decision trees, and random forest classifiers for predicting p ... 2.1 Introduction 2.2 Methods 2.2.1 Dataset available for the North American shale plays 2.2.2 Machine learning approach 2.2.3 Data cleaning and preprocessing 2.2.3.1 Input feature cleaning 2.2.3.2 Dimensionality reduction 2.2.4 Training machine learning algorithms 2.2.4.1 Stochastic gradient descent kernel trained support vector machine classifier 2.2.4.2 Decision tree classifier 2.2.4.3 Random forest classifier 2.2.4.4 Sample splitting and hyperparameter tuning 2.2.4.5 Evaluating performance of trained algorithms 2.3 Results 2.3.1 Comparative performance of our algorithms 2.3.1.1 SVM-SGD classifier 2.3.1.2 Decision tree classifier 2.3.1.3 Random forest classifier 2.3.1.4 Error analysis 2.3.2 Further optimization for four production classes and application to shale plays 2.3.2.1 Further optimization of the random forest classifier 2.3.2.2 Random forest prediction result for different North American shale plays 2.4 Discussion 2.4.1 Algorithm overview and comparison 2.4.2 Geological features of importance for each North American shale play 2.4.3 Data limitations 2.5 Conclusion 2.6 Acknowledgments References PART 2 - Deep learning approaches CHAPTER 3 - Recurrent neural network: application in facies classification 3.1 Introduction 3.2 Data types 3.2.1 Chronological data sets 3.2.2 Spatial data sets 3.3 Recurrent neural network (RNN) methods 3.3.1 Notation definition 3.3.2 Simple recurrent neural network (simple RNN) 3.3.3 Long short-term memory (LSTM) 3.3.4 Gated recurrent unit (GRU) 3.3.5 Convolutional recurrent neural network (ConvRNN) 3.3.6 Bidirectional recurrent neural network (BRNN) 3.4 Case study: Bi-LSTM-assisted facies classification based on well logging data 3.4.1 Geological setting 3.4.2 Problem definition and notation 3.4.3 Methods 3.4.4 Evaluation of the model performance 3.5 Results and discussion 3.6 Conclusion References Chapter 4 - Recurrent neural network for seismic reservoir characterization 4.1 Introduction 4.2 Methodolgy 4.3 Applications 4.3.1 Stanford VI-E dataset 4.3.2 Marmousi2 model 4.4 Conclusion References Chapter 5 - Convolutional neural networks: core interpretation with instance segmentation models 5.1 Introduction 5.2 Methods 5.2.1 Geological setting and data 5.2.2 Instance segmentation 5.3 Results 5.4 Discussion 5.5 Conclusion 5.6 Acknowledgments References Chapter 6 - Convolutional neural networks for fault interpretation – case study examples around the world 6.1 Introduction 6.2 Machine learning algorithms 6.3 Data and workflow 6.4 Case studies 6.4.1 Beagle Sub-Basin, Australia 6.4.2 East Shetland Basin, UK 6.4.3 Main Pass, Gulf of Mexico, USA 6.4.4 Cooper-Eromanga Basin, Australia 6.5 Discussions 6.6 Conclusions 6.7 Acknowledgments References PART 3 - Physics-based machine learning approaches Chapter 7 - Applying scientific machine learning to improve seismic wave simulation and inversion 7.1 Introduction 7.1.1 Seismic imaging 7.1.2 Computational issues for wave propagation 7.1.3 Opportunity for data analytics 7.1.4 Scientific machine learning 7.2 Related work 7.2.1 Seismic wave simulation and inversion 7.2.2 Surrogate models using machine learning 7.2.3 Dimensionality reduction 7.2.4 Differentiable programming 7.3 Wave equations and RNN 7.3.1 Wave equations 7.3.2 Recurrent neural network 7.3.3 PyTorch RNN implementation 7.3.4 Seismic wave simulation 7.4 Differentiable programming 7.4.1 Automatic differentiation and adjoint-state method 7.4.2 Extended automatic differentiation 7.5 Seismic inversion 7.5.1 Seismic inversion using neural network 7.5.2 Autoencoder for dimensionality reduction 7.5.3 Results 7.6 Discussion 7.7 Conclusions 7.8 Acknowledgment References Chapter 8 - Prediction of acoustic velocities using machine learning and rock physics 8.1 Introduction 8.2 Conventional rock physics modeling 8.3 Machine learning methods 8.3.1 Support vector regression 8.3.2 Random forest 8.3.3 Multilayer perceptron 8.3.4 Metrics for model performance evaluation 8.4 Synthetic data test 8.5 Real data test 8.6 Discussion 8.7 Conclusions 8.8 Acknowledgments References Chapter 9 - Regularized elastic full-waveform inversion using deep learning 9.1 Introduction 9.2 Methodology 9.2.1 Correlation elastic FWI 9.2.2 Deep neural networks 9.2.3 Facies constraints 9.3 Numerical examples 9.3.1 A synethetic Marmousi example 9.3.2 The North Sea field data example 9.3.2.1 Facies extraction 9.3.2.2 Inversion results 9.4 Discussion 9.5 Conclusions 9.6 Acknowledgments 9.7 Appendix example training code References Chapter 10 - A holistic approach to computing first-arrival traveltimes using neural networks 10.1 Introduction 10.2 Theory 10.2.1 Eikonal equations 10.2.2 Approximation property of neural networks 10.2.3 Automatic differentiation 10.2.4 Solving eikonal equations 10.3 Numerical tests 10.4 Discussion and conclusions References PART 4 - New directions Chapter 11 - Application of artificial intelligence to computational fluid dynamics 11.1 Introduction 11.1.1 Structure of the work 11.2 Background 11.2.1 NETL’s high-pressure combustor facility (B6 combustor) 11.2.2 Ansys fluent 11.2.2.1 Turbulence model 11.2.2.2 CFD reaction Eddy-dissipation model 11.2.2.3 CFD heat transfer model 11.2.2.3.1 Radiation model 11.2.3 Machine learning 11.2.3.1 Fuzzy clustering 11.2.3.2 Artificial neural networks 11.2.3.3 Artificial neural network performance evaluation metrics 11.2.3.4 Data batching 11.2.4 Previous work 11.3 B6 combustor model 11.3.1 B6 combustor problem definition 11.3.2 B6 combustor CFD simulation model 11.3.3 B6 smart proxy development overview 11.3.3.1 Data received from CFD simulation runs 11.3.3.2 Data visualization tool 11.3.3.3 Descriptive analytics 11.3.3.3.1 B6 combustor model sectioning 11.3.3.4 Predictive analytics 11.3.3.4.1 Data partitioning 11.3.3.4.2 Fuzzy clustering 11.3.3.4.3 Artificial neural network setup 11.3.3.4.4 Data batching 11.4 Smart proxy development steps 11.4.1 Model development step 1: Cell geometry and distances to wall boundaries 11.4.1.1 Model development step 1 – model training information 11.4.1.2 Model development step 1 – presentation of results 11.4.2 Model development step 2: Cell neighborhood, location, and Euclidian distances to wall boundaries 11.4.2.1 Model development step 2 – model training information 11.4.2.2 Model development step 2 – presentation of results 11.4.3 Model development step 3: Swirler distances 11.4.3.1 Model development step 3 – model training information 11.4.3.2 Model development step 3 – presentation of results 11.4.4 Model development step 4: Fuzzy clustering 11.4.4.1 Model development step 4 – model training information 11.4.4.2 Model development step 4 – presentation of results 11.5 Conclusions 11.5.1 Recommendations 11.6 Appendix 11.6.1 Model development step 3: Q1 combustor results – nitrogen 11.7 Acknowledgments 11.8 Disclaimer References Index