
Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems
Prediction Models Exploiting Well-Log Information
- 1st Edition - February 18, 2025
- Imprint: Elsevier
- Author: David A. Wood
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 6 5 1 0 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 6 5 1 1 - 2
Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and de… Read more

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Request a sales quoteImplementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and deep learning models for subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. The book provides insights into how the performance of ML/DL models can be optimized—and sparse datasets of input variables enhanced and/or rescaled—to improve prediction performances. A variety of topics are covered, including regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and more.
Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic.
Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic.
- Addresses common applied geological problems focused on machine and deep learning implementation with case studies
- Considers regression, classification, and clustering machine learning methods and how to optimize and assess their performance, considering suitable error and accuracy metric
- Contrasts the pros and cons of multiple machine and deep learning methods
- Includes techniques to improve the identification of geological carbon capture and storage reservoirs, a key part of many energy transition strategies
Geoscience and subsurface engineering are the main disciplines. Petroleum and energy operating companies, such as exploration and development geologists (for modeling, data mining, and interpretation), petrophysicists and well-log analysts (for dataset interpretation), and machine and deep learning analysts (for geological and model implementation insight). Industry service providers, such as energy service-company modelers and data analysts (for product optimization and development). Graduate students, post-doctoral researchers, and other academic researchers
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Chapter 1. Overview of artificial intelligence methods and data analysis techniques suitable for subsurface datasets
- Abstract
- 1.1 Introduction
- 1.2 Types and purpose of artificial intelligence models
- 1.3 Statistical characterization of AI datasets
- 1.4 Dataset preprocessing and the identification of extreme data-record values
- 1.5 Feature influence and selection
- 1.6 Model configurations and control variable optimization
- 1.7 Prediction assessment methods and metrics
- 1.8 Potential future directions
- 1.9 Summary and conclusions
- Nomenclature
- Appendix 1.1 Python code snippet for summarizing the fundamental characterization data for a structured dataset
- Appendix 1.2 Python code snippet for displaying useful graphics for visualizing key characteristics of a structured dataset
- References
- Chapter 2. Regression models to estimate total organic carbon (TOC) from well-log data
- Abstract
- 2.1 Introduction
- 2.2 Empirical methods for calculating TOC from petrophysical well logs
- 2.3 Machine learning approaches to estimating TOC from well-log data
- 2.4 Types of well-log attributes worth calculating
- 2.5 Regression error prediction assessment
- 2.6 Multi-K-fold cross-validation analysis for SML model selection
- 2.7 Normalizing data variable distributions
- 2.8 SML control/ hyperparameter optimization
- 2.9 Feature selection using a suite of optimizers
- 2.10 Case study: SML applied to predict TOC from Barnett Shale Well A
- 2.11 Summary and conclusions
- Declarations
- Nomenclature
- Appendix 2.1 Python code for leave-one-out loop applied with TOB Stage 1 model
- References
- Chapter 3. Predicting brittleness indexes in tight formation sequences
- Abstract
- 3.1 Introduction
- 3.2 Mineralogical and geomechanical influences on brittleness of tight formations
- 3.3 Brittleness indexes and how they are calculated
- 3.4 Value of well-log data for predicting brittleness
- 3.5 Overcoming the challenges of sparse well-log data
- 3.6 Supervised machine learning methods applied to predict BI
- 3.7 Outlier detection and interpretation
- 3.8 Case study: assessing outlying data records in a BIm prediction dataset
- 3.9 Feature importance analysis to specific supervised machine learning model solutions
- 3.10 Case study: supervised machine learning applied to predict BIm from a sparse suite of well logs
- 3.11 Case study: supervised machine learning applied to predict BIg in Appalachian Basin shale gas wells
- 3.12 Summary and conclusions
- Declarations
- Nomenclature
- Appendix 3.1 Python code for calculating Pearson’s (R) and Spearman’s (p) correlation coefficients simultaneously
- Appendix 3.2 Python code for Permutation Feature Importance Calculation
- References
- Chapter 4. Classifying lithofacies from well logs using supervised machine learning, cluster, and principal component analysis plus stacking model combinations
- Abstract
- 4.1 Introduction
- 4.2 Lithofacies variations observed in clastic sedimentary sequences
- 4.3 Lithofacies variations observed in carbonate sedimentary sequences
- 4.4 Machine learning methods applied to lithofacies classification using well logs
- 4.5 Prediction performance metrics for classification/ misclassification assessment
- 4.6 Annotated confusion matrices assist in the visualization of classification prediction performance
- 4.7 Case study: lithofacies prediction methods for synthetic clastic sequence
- 4.8 Case Study: Lithofacies prediction of mixed clastic and nonclastic sequences
- 4.9 Summary and conclusions
- Declarations
- Nomenclature
- Appendix 4.1 Python code for confusion matrix and classification accuracy metrics
- Appendix 4.2 Python code for conducting Bartlett’s test of equal variance
- References
- Chapter 5. Permeability, porosity, and water saturation relationships and distributions in complex reservoirs
- Abstract
- 5.1 Introduction
- 5.2 Definition of hydraulic flow units of complex reservoirs
- 5.3 Relevance of pore size and specific surface area to permeability distributions
- 5.4 Convolutional neural networks prediction models configured to use image inputs
- 5.5 Case Study 1: Outlier analysis considering the Mahalanobis distance
- 5.6 Case study 2: Robustness testing of machine learning models by applying noise
- 5.7 Case Study 3: Partial dependence between model target and input variables
- 5.8 Case Study 4: Shapley additive explanations for feature importance and selection
- 5.9 Summary and conclusions
- Declarations
- Nomenclature
- Appendix 5.1 Python code for Mahalanobis distance calculation
- Appendix 5.2 Python code for applying Gaussian noise for SML robustness testing
- Appendix 5.3 Python code for SML partial dependence analysis
- References
- Chapter 6. Trapping mechanisms in potential subsurface carbon storage reservoirs and their prediction by machine learning
- Abstract
- 6.1 Introduction
- 6.2 Subsurface geologic reservoirs for storing captured CO2
- 6.3 The role of supervised machine and deep learning in evaluating potential CCS/CCUS reservoirs
- 6.4 Prediction of CO2 residual and solubility trapping indexes in saline reservoirs
- 6.5 Case Study 1. Multifold cross-validation for insight into RTI prediction models
- 6.6 Case Study 2: aFr-index prediction performance and data mining metric
- 6.7 Case Study 3: Regression error characteristic curves for improved visualization of model performance
- 6.8 Case Study 4: Techniques and metrics able to reliably assess SML model overfitting/underfitting performance
- 6.9 Summary and conclusions
- Nomenclature
- Appendix 6.1 Excel cell formula for calculating aFr-indexes
- Appendix 6.2 : Python code snippet for calculating regression error characteristic (REC) curve and area under curve (AUC)
- References
- Chapter 7. Autocorrelation and accurate picking of formation boundaries using well-log data from multiple wells drilled in complex geological sequences
- Abstract
- 7.1 Introduction
- 7.2 Autocorrelation of well-logged sections across multiple wellbores
- 7.3 Deep learning methods for accelerating well-log correlation
- 7.4 Exploiting well-log attributes for formation boundary/thickness determinations
- 7.5 Case Study: applying binary classification to predict a formation boundary
- 7.6 Summary and conclusions
- Nomenclature
- Appendix 7.1 1D convolutional autoencoder Python configuration
- Appendix 7.2 Reconfiguring XGB models in Python to combine with RF capabilities
- References
- Chapter 8. Assessing formation loss of circulation risks with mud-log datasets: resampling and classifying imbalanced datasets
- Abstract
- 8.1 Introduction
- 8.2 Machine and deep learning approaches to predicting lost circulation
- 8.3 Class imbalance challenges for lost circulation predictions
- 8.4 Synthetic minority class oversampling techniques
- 8.5 Local outlier factor (LOF) technique with data mining applications
- 8.6 Classification prediction performance metrics appropriate for imbalanced datasets
- 8.7 Case Study 1. Prediction of an imbalanced lost circulation dataset with regression and classification models
- 8.8 Case Study 2: Prediction of large imbalanced lost circulation classification dataset
- 8.9 Summary and conclusions
- Nomenclature
- Appendix 8.1 Python code snippet for dataset resampling algorithm configurations
- Appendix 8.2 Python code snippet for applying local outlier factor (LOF) algorithm
- References
- Chapter 9. Predicting formation fracture characteristics derived from borehole image data with petrophysical well-log variables
- Abstract
- 9.1 Introduction
- 9.2 Formation characteristics that can be detected and quantified with BHI data
- 9.3 BHI logging tool features and configurations
- 9.4 Disparities between feature distributions identified in cores and BHI
- 9.5 SML/SDL research studies addressing the prediction of reservoir fracture properties
- 9.6 Leverage and influence metrics to identify atypical data records
- 9.7 Case study: two-step prediction of reservoir fracture density from BHI and petrophysical logs
- 9.8 Summary and conclusions
- Nomenclature
- Appendix 9.1 Python code snippet for calculating leverage and influence metrics
- References
- Chapter 10. Quantifying reservoir microfacies characterization using thin-section, scanned, computed tomography, and electron microscope image data
- Abstract
- 10.1 Introduction
- 10.2 Thin-section image analysis
- 10.3 Preparation and enhancement steps for thin-section image analysis
- 10.4 High-resolution thin-section scan images of core plugs and cuttings samples
- 10.5 Integrated reservoir characterization based on thin-section image analysis
- 10.6 Computed tomography and micro-CT (μCT) images analysis
- 10.7 SEM image analysis to provide detailed kerogen characterization
- 10.8 Case Study 10.1. 2D thin-section image characterization with cluster analysis models
- 10.9 Case Study 10.2: Pore network analysis of shales from FIB-SEM images segmented with a KiU-Net convolutional model
- 10.10 Summary and conclusions
- Nomenclature
- Appendix 10.1 Python code snippet for image edge detection
- Appendix 10.2 Python code snippet for K-means cluster analysis to quantify 2D porosity from a thin-section image of a reservoir formation
- References
- Chapter 11. Diverse machine learning applications for coal property characterization of coalbed methane and mining resources
- Abstract
- 11.1 Introduction
- 11.2 Coal’s fundamental properties
- 11.3 Coal gross calorific value
- 11.4 In situ gas content of coal
- 11.5 Coal mining safety considerations
- 11.6 Distinguishing coal versus noncoal lithologies
- 11.7 Coal bed methane production forecasting
- 11.8 Coal permeability
- 11.9 Coal seams as potential CO2 storage reservoirs
- 11.10 Natural gas emissions from subsurface coal deposits
- 11.11 Surface environmental contamination associated with coal mines
- 11.12 Coal geomechanical properties and stress conditions
- 11.13 Coal pore-scale networks, fractal dimensions and gas adsorption isotherms
- 11.14 Interpolation of spatial value distributions for coal seams
- 11.15 Case study: applying a geographic quantile regression forest interpolation model to predict the spatial distribution of coal-seam properties
- 11.16 Summary and conclusions
- Nomenclature
- Appendix 11.1 Python code snippet for applying the Scipy interpolate griddata function
- Appendix 11.2 Python code snippet for generating a semivariogram and applying it for ordinary kriging
- References
- Index
- Edition: 1
- Published: February 18, 2025
- Imprint: Elsevier
- No. of pages: 475
- Language: English
- Paperback ISBN: 9780443265105
- eBook ISBN: 9780443265112
DW
David A. Wood
David A. Wood has more than forty years of international gas, oil, and broader energy experience since gaining his Ph.D. in geosciences from Imperial College London in the 1970s. His expertise covers multiple fields including subsurface geoscience and engineering relating to oil and gas exploration and production, energy supply chain technologies, and efficiencies. For the past two decades, David has worked as an independent international consultant, researcher, training provider, and expert witness. He has published an extensive body of work on geoscience, engineering, energy, and machine learning topics. He currently consults and conducts research on a variety of technical and commercial aspects of energy and environmental issues through his consultancy, DWA Energy Limited. He has extensive editorial experience as a founding editor of Elsevier’s Journal of Natural Gas Science & Engineering in 2008/9 then serving as Editor-in-Chief from 2013 to 2016. He is currently Co-Editor-in-Chief of Advances in Geo-Energy Research.
Affiliations and expertise
Owner/Consultant, DWA Energy Limited, UKRead Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems on ScienceDirect