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 abridge 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.

Autorentext
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.

Inhalt
Preface

1. Introduction and Overview
   Part 1: Reservoir Characterization
2. Essential Reservoir Geology and Multi-Scales of Petroleum Reservoir Heterogeneities2.1 Structural Controls on Petroleum Resources2.2 Sequence Stratigraphy and Hydrocarbon Resources2.3 Depositional Environments2.4 Facies and Lithofacies2.5 Petrophysical Properties2.6 Subsurface Fluid Heterogeneities2.7 SummaryAppendix 1: Large-Scale Tectonic Settings and Their CharacteristicsAppendix 2: Hierarchy of Depositional Systems: Fluvial Architecture Example
3. Introduction to Petrophysical Reservoir Characterization3.1 Porosity Characterization and Estimation3.2 Clay Volume Analysis and Its Impacts on Other Petrophysical Parameters3.3 Permeability Characterization and Estimation3.4 Fluid Saturations Characterization and Estimation3.5 Uncertainty Analysis in Petrophysical Analysis3.6 Summary
4. Practical Seismic Reservoir Characterization4.1 Seismic Data: Resolution and Coverage4.2 Structural and Stratigraphic Interpretations4.3 Reservoir Delineation, EOD Mapping and Seismic Facies Identifications4.4 Reservoir Property Evaluation Using Seismic Data4.5 Seismic Attribute Analysis and Integration for Reservoir Characterization4.6 SummaryAppendix 1: Time-Frequency Representation of Seismic DataAppendix 2: Amplitude Versus Offset (AVO): An OverviewAppendix 3: Overview of Forward Modeling and Seismic Inversions
5. Statistical and Data Analytical Reservoir Characterization5.1 Common Descriptive Statistics for Reservoir Analysis5.2 Sampling Bias in E&P and Mitigation Methods5.3 Multivariate Statistical Data Analysis and Applications to Reservoir Analysis (Statistical Correlation, PCA, and Clustering Analysis)5.4 Overview of Artificial Neural Networks and Example Applications5.5 Bayesian Inference for Reservoir Characterization5.6 Regression for Mapping and Modeling of Reservoir Properties5.7 Advanced Regressions for Integrated Reservoir Characterization5.8 SummaryAppendix 1: Lord's Paradox and Reconciling Mathematics and Reservoir ProblemsAppendix 2: Impact of Interdependencies/collinearity on multiple linear regressions
6. Geostatistical Reservoir Characterization6.1 Variogram and Spatial Correlation6.2 Theoretical Variogram and Spatial Covariance Models6.3 Calculating and Fitting Experimental Variograms6.4 Interpreting Variograms of Reservoir Properties6.5 Lithofacies Variography and Indicator Variogram6.6 Cross-Variogram, Spatial Misalignment and Synchronization6.7 Relationships between Variogram/Covariance Function and Spectrum6.8 Summary
7. Integrated Facies and Lithofacies Analysis, Identification and Classification7.1 Geological Interpretation of Facies7.2 Lithofacies Classification Using Wireline Logs7.3 Integration of Geological Facies, Seismically Derived Facies, Well-Log-Derived Facies and Core Facies7.4 Summary
   Part 2: Geological and Reservoir Modeling
8. Constructing a Reservoir-Model Framework8.1 From Structural Elements to a Structural Model8.2 From Stratigraphic Elements to a Stratigraphic Model8.3 Building Depositional Geometrics into Geocellular Model
9. Geostatistical Modeling Methods9.1 Estimation Methods9.1.1 Simple Kriging, Ordinary Kriging, Nonstationary Kriging9.1.2 Factorial Kriging and D...