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A Primer on Machine Learning in Subsurface Geosciences

Part of the SpringerBriefs in Petroleum Geoscience & Engineering Book Series

DOI: 10.1007/978-3-030-71768-1

ISBN: 978-3-030-71767-4

Publisher: Springer International Publishing - Springer Journals

May 4, 2021

Available

Introduction

Bhattacharya, Shuvajit

Pages: 1 –19

[In the first chapter, we will learn about big data, data analytics, and machine learning, as well as their utilities in different disciplines, including geosciences. A thorough understanding of data analytics problems, algorithms, and geoscience-specific data is critical before applying these...
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A Brief Review of Statistical Measures

Bhattacharya, Shuvajit

Pages: 21 –43

[As geoscientists, we use a variety of data types (see Chapter 1). To analyze different varieties of geodata, we must use different statistical measures. A thorough understanding of various statistical measures and their applications to data analysis methods is important. In this chapter, we will...
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Basic Steps in Machine Learning-Based Modeling

Bhattacharya, Shuvajit

Pages: 45 –79

[Data-driven machine learning (ML) approaches are becoming very popular in analyzing facies, fractures, faults, rock properties, and fluid flow in subsurface characterization and modeling. Our reservoirs are becoming more data-rich due to the advent of new drilling, completion, and sensor...
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A Brief Review of Popular Machine Learning Algorithms in Geosciences

Bhattacharya, Shuvajit

Pages: 81 –121

[In the last several decades, computer scientists and statisticians have developed and implemented a plethora of machine learning (ML) algorithms. Although the application of data-driven modeling is relatively new to geoscience, we can trace back some of its early applications to the 1980’s and...
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Summarized Applications of Machine Learning in Subsurface Geosciences

Bhattacharya, Shuvajit

Pages: 123 –165

[Geoscientists have been implementing machine learning (ML) algorithms for several classifications and regression related problems in the last few decades. ML’s implementation in geosciences came in different phases, and often these broadly followed or lagged after certain advances in computer...
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The Road Ahead

Bhattacharya, Shuvajit

Pages: 167 –172

[In the last chapter, I discuss the future of data analytics (DA) and machine learning (ML) in geosciences research, instruction, community, and business, as a whole. It sets an agenda of ML-focused studies that need to be conducted to solve critical problems in geosciences. This endeavor will...

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