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

  • Book
  • © 2019

Overview

Authors:
  1. Y. Z. Ma

    1. Schlumberger, Denver, USA

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  • Presents a unique fusion of multidisciplinary geosciences: geology, petrophysics, geophysics, geostatistics and integrated data analytics
  • Emphasizes the integration of descriptive geology and quantitative geosciences
  • Connects research to industry problems
  • Contains many original methods and their applications to resource evaluation and reservoir studies
  • Provides essential materials for both researchers and practitioners in the digital world

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Table of contents (25 chapters)

  1. Front Matter

    Pages i-xxv
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  2. Introduction

    • Y. Z. Ma
    Pages 1-17

  3. Data Analytics

    1. Front Matter

      Pages 19-19
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    2. Probabilistic Analytics for Geoscience Data

      • Y. Z. Ma
      Pages 21-48

    3. Statistical Analysis of Geoscience Data

      • Y. Z. Ma
      Pages 49-75

    4. Correlation Analysis

      • Y. Z. Ma
      Pages 77-102

    5. Principal Component Analysis

      • Y. Z. Ma
      Pages 103-121

    6. Regression-Based Predictive Analytics

      • Y. Z. Ma
      Pages 123-149

    7. Introduction to Geoscience Data Analytics Using Machine Learning

      • Y. Z. Ma
      Pages 151-171

  5. Reservoir Modeling and Uncertainty Analysis

    1. Front Matter

      Pages 331-331
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    2. Introduction to Geological and Reservoir Modeling

      • Y. Z. Ma
      Pages 333-349

    3. Constructing 3D Model Framework and Change of Support in Data Mapping

      • Y. Z. Ma
      Pages 351-371

    4. Geostatistical Estimation Methods: Kriging

      • Y. Z. Ma
      Pages 373-401
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Keywords

About this book

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.

Authors and Affiliations

  • Schlumberger, Denver, USA

    Y. Z. Ma

About the author

Dr. Ma is a scientific advisor for geosciences at Schlumberger, specialized in reservoir characterization, modeling and resource evaluation. In his over 30 years of experience, he has worked on research and application of statistics, data analytics, and geostatistics to integrated reservoir studies for major oil companies in Europe and the US and has provided technical consultancies and training worldwide. Dr. Ma has published over 100 technical papers or book chapters in petroleum geology, geophysics, engineering, geostatistics, applied statistics and economics, and has received numerous awards, including the Schlumberger's Gold Award and Chairman Award, and the Mathematical Geosciences’ Best Paper. Dr. Ma has earned a PhD in Mathematical Geology and Geoinformatics from Université de Lorraine (previously Institute National Polytechnique de Lorraine), France, and MSc in Geostatistics from École des Mines de Paris, France, and a BSc in Geology from China University of Geosciences.

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