Advances in Subsurface Data Analytics: Traditional and Physics-Based Machine Learning 1st edition

Advances in Subsurface Data Analytics: Traditional and Physics-Based Machine Learning 1st edition – Ebook Instant Download/Delivery ISBN(s): 9780128222959,0128222956,9780128223086,0128223081

Product details:

• ISBN 10: 0128223081
• ISBN 13: 9780128223086
• Author: Shuvajit Bhattacharya

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.

Table contents:

Chapter 1 User vs. machine-based seismic attribute selection for unsupervised machine learning techniques: Does human insight provide better results than statistically chosen attributes?

Chapter 2 Relative performance of support vector machine, decision trees, and random forest classifiers for predicting production success in US unconventional shale plays

Chapter 3 Recurrent neural network: application in facies classification

Chapter 4 Recurrent neural network for seismic reservoir characterization

Chapter 5 Convolutional neural networks: core interpretation with instance segmentation models

Chapter 6 Convolutional neural networks for fault interpretation – case study examples around the world

Chapter 7 Applying scientific machine learning to improve seismic wave simulation and inversion

Chapter 8 Prediction of acoustic velocities using machine learning and rock physics

Chapter 9 Regularized elastic full-waveform inversion using deep learning

Chapter 10 A holistic approach to computing first-arrival traveltimes using neural networks

Chapter 11 Application of artificial intelligence to computational fluid dynamics

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