Advances in Subsurface Data Analytics

Herausgeber: Di, Haibin; Bhattacharya, Shuvajit

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science Publishing Co Inc
• Seitenzahl: 376
• Erscheinungstermin: 20. Mai 2022
• Englisch
• Abmessung: 192mm x 235mm x 24mm
• Gewicht: 806g
• ISBN-13: 9780128222959
• ISBN-10: 0128222956
• Artikelnr.: 62479324

Inhaltsangabe

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