Keywords:
Earth sciences.
;
Electronic books.
Type of Medium:
Online Resource
Pages:
1 online resource (646 pages)
Edition:
1st ed.
ISBN:
9783030178604
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=5830021
DDC:
550
Language:
English
Note:
Intro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction -- 1.1 Making Descriptive and Quantitative Geosciences Work Together -- 1.2 Moving from 2D Mapping and Cross-Section Analysis to 3D Reservoir Modeling -- 1.3 Geology Is Not Random -- Why Should We Use Probability in Applied Geosciences? -- 1.4 Using Geostatistics and Statistics in Geoscience Data Analysis and Modeling -- 1.5 (Exploiting) Big Data, Not for Bigger, But for Better -- 1.6 Making Better Business Decisions with Uncertainty Analysis -- 1.7 Bridging the Great Divide in Reservoir Characterization Through Integration -- 1.8 Balancing Theory and Practicality -- 1.9 Be a Modern Geoscientist -- References -- Part I: Data Analytics -- Chapter 2: Probabilistic Analytics for Geoscience Data -- 2.1 Introduction -- 2.2 Basic Concepts -- 2.3 Probability Axioms and their Implications on Facies Analysis and Mapping -- 2.4 Conditional Probability -- 2.5 Monty Hall Problem: Importance of Understanding the Physical Condition -- 2.5.1 Lesson 1: Discerning the Non-randomness in an Indeterministic Process -- 2.5.2 Lesson 2: Value of Non-random Information -- 2.5.3 Lesson 3: Physical Process Is Important, Not Just Observed Data -- 2.6 What Is a Pure Random Process? -- 2.7 Bayesian Inference for Data Analysis and Integration -- 2.7.1 Ignoring the Likelihood Function and Relying on Prior or Global Statistics -- 2.7.2 Bayesian Inference and Interpretation Dilemma -- 2.7.3 Bayesian Inference and Base Rate or Prior Neglect -- 2.7.4 Are the Bayesian Statistics Subjective? -- 2.7.5 Bayesian Inference as a Generative Modeling Method -- 2.8 The Law of Large Numbers and Its Implications for Resource Evaluation -- 2.8.1 Remarks on the Law of Large Numbers and Spatial Data Heterogeneity -- 2.9 Probabilistic Mixture Analysis of Geoscience Data -- 2.10 Summary -- 2.11 Exercises and Problems.
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References -- Chapter 3: Statistical Analysis of Geoscience Data -- 3.1 Common Statistical Parameters and Their Uses in Subsurface Data Analysis -- 3.1.1 Mean -- 3.1.1.1 Definitions -- 3.1.1.2 Weighted Mean/Average -- 3.1.1.3 Mean, Change of Scale and Sample´s Geometries -- 3.1.2 Variance, Standard Deviation, and Coefficient of Variation -- 3.1.2.1 Definitions -- 3.1.2.2 Proportional Effect and Inverse Proportional Effect -- 3.1.2.3 Variance and Change of Scale -- 3.2 Sampling Bias in Geosciences and Mitigation Methods -- 3.2.1 Areal Sampling Bias from Vertical Wells and Mitigation Methods -- 3.2.1.1 Voronoi Polygonal Tessellation -- 3.2.1.2 Propensity-Zoning Method -- 3.2.2 Vertical Sampling Bias from Vertical Wells and Mitigation -- 3.2.3 Sampling Biases from Horizontal Wells and Mitigations -- 3.2.4 Sampling Bias, Stratigraphy and Simpson´s Paradox -- 3.3 Summary -- 3.4 Exercises and Problems -- References -- Chapter 4: Correlation Analysis -- 4.1 Correlation and Covariance -- 4.2 Geological Correlation Versus Statistical Correlation -- 4.3 Correlation and Covariance Matrices -- 4.4 Partial Transitivity of Correlations -- 4.5 Effects of Other Variables on (Bivariate) Correlations -- 4.6 Correlation, Causation and Physical Laws -- 4.6.1 Correlation, Causation and Causal Diagrams -- 4.6.2 Using Physical Laws in Correlation Analysis -- 4.7 Impact of Correlation by Missing Values or Sampling Biases -- 4.8 Spearman Rank Correlation and Nonlinear Transforms -- 4.9 Correlation for Categorical Variables -- 4.9.1 Stratigraphy-Related Simpson´s Paradox -- 4.10 Trivariate Correlation and Covariance (Can Be Skipped for Beginners) -- 4.11 Summary -- 4.12 Exercises and Problems -- Appendices -- Appendix 4.1 Probabilistic Definitions of Mean, Variance and Covariance -- Appendix 4.2 Graphic Displays for Analyzing Variables´ Relationships -- References.
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Chapter 5: Principal Component Analysis -- 5.1 Overview -- 5.1.1 Aims of PCA -- 5.1.2 Procedures of PCA -- 5.1.3 Example -- 5.2 Specific Issues -- 5.2.1 Using Correlation or Covariance Matrix for PCA -- 5.2.2 Relationship Between PCA and Factor Analysis -- 5.2.3 Interpretations and Rotations of Principal Components -- 5.2.4 Selection of Principal Components -- 5.3 PCA for Classifications -- 5.4 Performing PCA Conditional to a Geological Constraint -- 5.5 Cascaded PCAs for Characterizing 3D Volumes and Compaction of Data -- 5.6 PCA for Feature Detection and Noise Filtering -- 5.7 Summary -- 5.8 Exercises and Problems -- Appendices -- Appendix 5.1 Introduction to PCA with an Example -- A5.1.1 Introductory Example -- A5.1.2 Standardizing Data -- A5.1.3 Computing Correlation Matrix -- A5.1.4 Finding Eigenvectors and Eigenvalues -- A5.1.5 Finding the principal components -- A5.1.6 Basic Analytics in PCA -- References -- Chapter 6: Regression-Based Predictive Analytics -- 6.1 Introduction and Critiques -- 6.2 Bivariate Regression -- 6.2.1 Bivariate Linear Regression -- 6.2.2 Variations of Bivariate Linear Regression -- 6.2.3 Remarks -- 6.2.4 Nonlinear Bivariate Regression -- 6.3 Multivariate Linear Regression (MLR) -- 6.3.1 General -- 6.3.2 Effect of Collinearity -- 6.3.3 Subset Selection -- 6.3.4 Regularization -- 6.4 Principal Component Regression (PCR) -- 6.4.1 Selection of Principal Components for PCR -- 6.4.2 Comparison of Subset Selection, Ridge Regression and PCR -- 6.5 An Example -- 6.6 Summary -- 6.7 Exercises and Problems -- Appendices -- Appendix 6.1: Lord´s Paradox and Importance of Judgement Objectivity -- Appendix 6.2 Effects of Collinearity in Multivariate Linear Regression -- A6.2.1 Cooperative Suppression -- A6.2.2 Net Suppression -- References -- Chapter 7: Introduction to Geoscience Data Analytics Using Machine Learning.
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7.1 Overview of Artificial-Intelligence-Based Prediction and Classification Methods -- 7.1.1 Extensions of Multivariate Regressions -- 7.1.2 Ensemble of Algorithms or Combined Methods -- 7.1.3 Validation of Predictions and Classifications -- 7.2 Challenges in Machine Learning and Artificial Intelligence -- 7.2.1 Model Complexity -- 7.2.2 Generative Model Versus Discriminative Model -- 7.2.3 Trading Bias and Variance -- 7.2.4 Balancing the Overfitting and Underfitting -- 7.2.5 Collinearity and Regularization in Big Data -- 7.2.6 The No-Free-Lunch Principle -- 7.3 Basics of Artificial Neural Networks (ANN) -- 7.3.1 Back Propagation Algorithm for ANN -- 7.3.2 Unsupervised Learning and Supervised Learning -- 7.3.3 Advantages and Disadvantages of Using Neural Networks -- 7.4 Example Applications Using ANN and Ensembled Methods -- 7.4.1 Classification -- 7.4.2 Integration of Data for Predicting Continuous Geospatial Properties -- 7.4.3 Ensembled ANN and Geostatistical Method for Modeling Geospatial Properties -- 7.5 Summary -- References -- Part II: Reservoir Characterization -- Chapter 8: Multiscale Heterogeneities in Reservoir Geology and Petrophysical Properties -- 8.1 Introduction -- 8.2 Structural Elements -- 8.2.1 Anticlines -- 8.2.2 Faults and Fractures -- 8.3 Multiscale Heterogeneities in Sequence Stratigraphic Hierarchy -- 8.4 Depositional Environments, Facies Spatial and Geometric Heterogeneities -- 8.5 Facies and Lithology: Compositional Spatial Trends -- 8.5.1 Facies Lateral and Vertical Trends -- 8.5.2 Lithology Compositional Trends -- 8.6 Heterogeneities in Petrophysical Properties -- 8.6.1 Statistical Description of Heterogeneities in Petrophysical Properties -- 8.6.2 Other Non-spatial Measures of Petrophysical Properties´ Heterogeneities -- 8.6.3 Spatial Descriptions of Heterogeneities in Petrophysical Properties.
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8.6.4 Spatial Discontinuity in Petrophysical Properties -- 8.7 Data and Measurements for Describing Heterogeneities -- 8.8 Impact of Heterogeneities on Subsurface Fluid Flow and Production -- 8.9 Summary -- Appendices -- Appendix 8.1 Large-Scale Tectonic Settings and their Characteristics -- Appendix 8.2 Sequence Stratigraphic Hierarchy in Fluvial Setting -- References -- Chapter 9: Petrophysical Data Analytics for Reservoir Characterization -- 9.1 Porosity Characterization and Estimation -- 9.1.1 Total and Effective Porosities -- 9.1.2 Deriving Porosity Data at Wells -- 9.1.2.1 Porosity from a Single Well Log (Basic Principles) -- Density Porosity -- Sonic Porosity -- Neutron Porosity -- NMR Porosity -- 9.1.2.2 Deriving Porosity from Two or More Logs and Correlation Analysis -- 9.1.3 Correlation Analysis of Porosity-Measuring Logs and Lithology Mixture -- 9.1.4 Calibration of Core and Well-Log Porosities -- 9.1.5 Common Issues and Their Mitigations in Porosity Estimation -- 9.1.5.1 Borehole Conditions -- 9.1.5.2 Other Issues -- 9.1.6 Effects of Minerals and Other Contents -- 9.2 Clay Volume and Its Impacts on Other Petrophysical Parameters -- 9.3 Permeability Characterization -- 9.3.1 Factors Affecting Permeability -- 9.3.2 Relationships Between Permeability and Other Properties -- 9.3.2.1 Impacts of Geological Variables on Porosity-Permeability Relationship -- 9.3.2.2 Correlation Analysis -- 9.4 Water Saturation (Sw) Characterization -- 9.5 Reservoir Quality Analysis -- 9.5.1 Assessing reservoir Quality Using Static Properties -- 9.5.2 Reservoir Quality Index and Flow Zone Indicator -- 9.6 Summary -- Appendix 9.1: Common Well Logs, and Related Petrophysical and Geological Properties -- References -- Chapter 10: Facies and Lithofacies Classifications from Well Logs -- 10.1 Background and Introductory Example.
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10.1.1 Facies, Lithofacies, Petrofacies, Electrofacies, and Rock Types.
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