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Advances in Subsurface Data Analytics
Compiles case studies surrounding the application of emerging machine learning and deep learning algorithms in subsurface imaging and hydrocarbon reservoir characterization
Shuvajit Bhattacharya (Edited by), Haibin Di (Edited by)
9780128222959
Paperback, published 20 May 2022
376 pages
23.5 x 19 x 2.4 cm, 0.77 kg
Approx.356 pages
Part 1: Traditional Machine Learning Approaches 1. User Vs. Machine Seismic Attribute Selection for Unsupervised Machine Learning Techniques: Does Human Insight Provide Better Results Than Statistically Chosen Attributes? 2. Relative Performance of Support Vector Machine, Decision Trees, and Random Forest Classifiers for Predicting Production Success in US unconventional Shale Plays Part 2: Deep Learning Approaches 3. Recurrent Neural Network: application in facies classification 4. Recurrent Neural Network for Seismic Reservoir Characterization 5. Application of Convolutional Neural Networks for the Classification of Siliciclastic Core Photographs 6. Convolutional Neural Networks for Fault Interpretation – Case Study Examples around the World Part 3: Physics-based Machine Learning Approaches 7. Scientific Machine Learning for Improved Seismic Simulation and Inversion 8. Prediction of Acoustic Velocities using Machine Learning 9. Regularized Elastic Full Waveform Inversion using Deep Learning 10. A Holistic Approach to Computing First-arrival Traveltimes using Neural Networks Part 4: New Directions 11. Application of Artificial Intelligence to Computational Fluid Dynamics
Subject Areas: Enterprise software [UFL], Earth sciences [RB]
