Machine learning for subsurface characterization /
| Main Authors: | , , |
|---|---|
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
San Diego :
Gulf Professional Publishing, an imprint of Elsevier,
2019.
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| Edition: | First edition. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Front Cover; Machine Learning for Subsurface Characterization; Copyright; Dedication; Contents; Contributors; Preface; Machine learning-driven success stories; Challenges and precautions when using machine learning; Recommendations for harnessing the power of machine learning; Concluding remarks; References; Further reading; Acknowledgment; Chapter 1: Unsupervised outlier detection techniques for well logs and geophysical data; 1. Introduction; 1.1. Basic terminologies in machine learning and data-driven models; 1.2. Types of machine learning techniques; 1.3. Types of outliers
- 2. Outlier detection techniques3. Unsupervised outlier detection techniques; 3.1. Isolation forest; 3.2. One-class SVM; 3.3. DBSCAN; 3.4. Local outlier factor; 3.5. Influence of hyperparameters on the unsupervised ODTs; 4. Comparative study of unsupervised outlier detection methods on well logs; 4.1. Description of the dataset used for the comparative study of unsupervised ODTs; 4.2. Data preprocessing; 4.2.1. Feature transformation: Convert R to log(R); 4.2.2. Feature scaling: Use of robust scaler; 4.3. Validation dataset; 4.3.1. Dataset #1: Containing noisy measurements
- 4.3.2. Dataset #2: Containing bad holes4.3.3. Dataset #3: Containing shaly layers and bad holes with noisy measurements; 4.3.4. Dataset #4: Containing manually labeled outliers; 4.4. Metrics/scores for the assessment of the performances of unsupervised ODTs on the conventional logs; 4.4.1. Recall; 4.4.2. Specificity; 4.4.3. Balanced accuracy score; 4.4.4. Precision; 4.4.5. F1 score; 4.4.6. Receiver operating characteristics (ROC) curve and ROC-AUC score; 4.4.7. Precision-recall (PR) curve and PR-AUC score; 5. Performance of unsupervised ODTs on the four validation datasets
- 5.1. Performance on Dataset #1 containing noisy measurements5.2. Performance on Dataset #2 containing measurements affected by bad holes; 5.3. Performance on Dataset #3 containing shaly layers and bad holes with noisy measurements; 5.4. Performance on Dataset #4 containing manually labeled outliers; 6. Conclusions; Appendix A. Popular methods for outlier detection; Appendix B. Confusion matrix to quantify the inlier and outlier detections by the unsupervised ODTs; Appendix C. Values of important hyperparameters of the unsupervised ODT models
- Appendix D. Receiver operating characteristics (ROC) and precision-recall (PR) curves for various unsupervised ODTs on th ...Acknowledgments; References; Chapter 2: Unsupervised clustering methods for noninvasive characterization of fracture-induced geomechanical alterations; 1. Introduction; 2. Objective of this study; 3. Laboratory setup and measurements; 4. Clustering methods for the proposed noninvasive visualization of geomechanical alterations; 4.1. K-means clustering; 4.2. Agglomerative clustering; 4.3. DBSCAN