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Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

  • Y. Z. Ma
Book

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Y. Z. Ma
    Pages 1-17
  3. Data Analytics

    1. Front Matter
      Pages 19-19
    2. Y. Z. Ma
      Pages 77-102
    3. Y. Z. Ma
      Pages 103-121
    4. Y. Z. Ma
      Pages 123-149
  4. Reservoir Characterization

  5. Reservoir Modeling and Uncertainty Analysis

  6. Back Matter
    Pages 623-640

About this book

Introduction

Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.


Keywords

Reservoir Modeling Data Analytics for E&P Reservoir Uncertainty Analysis Petroleum Geostatistics Reservoir uncertainty modeling Integrated descriptive and quantitative geology E&P Decision making

Authors and affiliations

  • Y. Z. Ma
    • 1
  1. 1.SchlumbergerDenverUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-17860-4
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Energy
  • Print ISBN 978-3-030-17859-8
  • Online ISBN 978-3-030-17860-4
  • Buy this book on publisher's site