Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization

1st Edition - October 12, 2019

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  • Authors: Siddharth Misra, Hao Li, Jiabo He
  • eBook ISBN: 9780128177372
  • Paperback ISBN: 9780128177365

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Description

Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface.

Key Features

  • Learn from 13 practical case studies using field, laboratory, and simulation data
  • Become knowledgeable with data science and analytics terminology relevant to subsurface characterization
  • Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support

Readership

Petroleum engineers; reservoir engineers; petrophysicts; geoscientists

Table of Contents

  • 1. Unsupervised outlier detection techniques for well logs and geophysical data
    2. Unsupervised clustering methods for noninvasive characterization of fracture-induced geomechanical alterations
    3. Shallow neural networks and classification methods for approximating the subsurface in situ fluid-filled pore size distribution
    4. Stacked neural network architecture to model themultifrequency conductivity/permittivity responses of subsurface shale formations
    5. Robust geomechanical characterization by analyzing the performance of shallow-learning regression methods using unsupervised clustering methods
    6. Index construction, dimensionality reduction, and clustering techniques for the identification of flow units in shale formations suitable for enhanced oil recovery using light-hydrocarbon injection
    7. Deep neural network architectures to approximate the fluid-filled pore size distributions of subsurface geological formations
    8. Comparative study of shallow and deep machine learning models for synthesizing in situ NMR T2 distributions
    9. Noninvasive fracture characterization based on the classification of sonic wave travel times
    10. Machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking
    11. Generalization of machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales
    12. Characterization of subsurface hydrocarbon/water saturation by processing subsurface electromagnetic logs using a modified Levenberg-Marquardt algorithm
    13. Characterization of subsurface hydrocarbon/water saturation using Markov-chain Monte Carlo stochastic inversion of broadband electromagnetic logs

Product details

  • No. of pages: 440
  • Language: English
  • Copyright: © Gulf Professional Publishing 2019
  • Published: October 12, 2019
  • Imprint: Gulf Professional Publishing
  • eBook ISBN: 9780128177372
  • Paperback ISBN: 9780128177365

About the Authors

Siddharth Misra

Siddharth Misra is currently associate professor at the Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, Texas. His research work is in the area of data-driven predictive models, machine learning, geosensors, and subsurface characterization. He earned a PhD in petroleum engineering from the University of Texas and a bachelor of technology in electrical engineering from the Indian Institute of Technology in Bombay. He received the Department of Energy Early Career Award in 2018 to promote geoscience research.

Affiliations and Expertise

Associate Professor, Harold Vance Department of Petroleum Engineering and the Department of Geology and Geophysics, Texas A&M University, USA

Hao Li

Hao Li is a PhD-degree candidate in the Mewbourne College of Earth and Energy (MCEE) at the University of Oklahoma in Norman. He interned with Facebook on improving ranking models using machine learning. His research interests include machine learning, petrophysics, and data analytics. He holds an MS degree in petroleum engineering from China University of Petroleum in Beijing.

Affiliations and Expertise

PhD-degree candidate in the Mewbourne College of Earth and Energy (MCEE) at the University of Oklahoma in Norman.

Jiabo He

Jiabo He is currently a doctoral candidate in computer science at the University of Melbourne, Australia. Jiabo’s research area includes deep learning, reinforcement learning, and imitation learning. He earned an MS in petroleum engineering from the University of Oklahoma and a BS in petroleum engineering from the China University of Petroleum in Beijing.

Affiliations and Expertise

A doctoral candidate in computer science at the University of Melbourne, Australia.

Ratings and Reviews

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  • rohans@yahoo Wed Apr 15 2020

    good one

    this book has a lot of information with good examples. It will be even better if the authors can share more such examples.

  • Jichen Mon Feb 17 2020

    Collection of great concepts

    This book is a great collection of concepts. Each paragraph has something useful and insightful. I chose the electronic version of colored content.

  • MichelKrief Thu Dec 12 2019

    Practical Expertise

    Sharing practical case studies is key to help understand the concepts behind Deep Learning. The number of cases (13) is enough to cover a wide range of applications.

  • LydiaJones Wed Dec 11 2019

    Yes

    Yes

  • Cara Tue Dec 03 2019

    Interesting case studies and concepts

    The book starts easy and gradually moves into more complex topics with each chapter. The authors have done a good job laying out a sequence of topics that help better understand the nuances of implementing machine learning.

  • Susan Sun Oct 27 2019

    ML for Subsurface

    Finally got my hands on the book. Authors nicely mix concepts and applications. I now have better clarity on using the ML methods.

  • John G. Fri Oct 18 2019

    Data is new oil

    The book has so many interesting cases. Each case is different and each chapter introduces a new data and machine learning concept. The book is a nice read.

  • Rosh Sat Oct 12 2019

    Much needed book

    Thanks for putting in a great effort to create this book on machine learning. As a petroleum engineer, I find the content of the book to be very well organized and really insightful.

  • Anna Fri Sep 27 2019

    Researcher in Geoscience

    The topics are relevant and much needed for this day and age.