Kooperativer Bibliotheksverbund

UID:

Umfang: 1 online resource (252 pages) : , illustrations (some color)

ISBN: 9780128032794 , 0128032790 , 9780128032800 , 0128032804

Anmerkung: Front Cover -- Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1: Basic Concepts -- 1.1. Background and Scope -- 1.1.1. What Is Statistics? -- 1.1.2. What Is Big Data Analytics? -- 1.1.3. Data Analysis Cycle -- 1.1.4. Some Applications in the Petroleum Geosciences -- 1.2. Data, Statistics, and Probability -- 1.2.1. Outcomes and Events -- 1.2.2. Probability -- 1.2.3. Conditional Probability and Bayes Rule -- 1.3. Random Variables -- 1.3.1. Discrete Case -- 1.3.2. Continuous Case -- 1.3.3. Indicator Transform -- 1.4. Summary -- Exercises -- References -- Chapter 2: Exploratory Data Analysis -- 2.1. Univariate Data -- 2.1.1. Measures of Center -- 2.1.2. Measures of Spread -- 2.1.3. Measures of Asymmetry -- 2.1.4. Graphing Univariate Data -- 2.2. Bivariate Data -- 2.2.1. Covariance -- 2.2.2. Correlation and Rank Correlation -- 2.2.3. Graphing Bivariate Data -- 2.3. Multivariate Data -- 2.4. Summary -- Exercises -- References -- Chapter 3: Distributions and Models Thereof -- 3.1. Empirical Distributions -- 3.1.1. Histogram -- 3.1.2. Quantile Plot -- 3.2. Parametric Models -- 3.2.1. Uniform Distribution -- 3.2.2. Triangular Distribution -- 3.2.3. Normal Distribution -- 3.2.4. Lognormal Distribution -- 3.2.5. Poisson Distribution -- 3.2.6. Exponential Distribution -- 3.2.7. Binomial Distribution -- 3.2.8. Weibull Distribution -- 3.2.9. Beta Distribution -- 3.3. Working With Normal and Log-Normal Distributions -- 3.3.1. Normal Distribution -- 3.3.2. Normal Score Transformation -- 3.3.3. Log-Normal Distribution -- 3.4. Fitting Distributions to Data -- 3.4.1. Probability Plots -- 3.4.2. Parameter Estimation Techniques -- Linear Regression Analysis -- Method of Moments -- Nonlinear Least-Squares Analysis. , 3.5. Other Properties of Distributions and Their Evaluation -- 3.5.1. Central Limit Theorem and Confidence Limits -- 3.5.2. Bootstrap Sampling -- 3.5.3. Comparing Two Distributions -- Q-Q Plot -- Testing for Difference in Mean -- Testing for Difference in Distributions -- Other Methods for Comparing Distributions -- 3.6. Summary -- Exercises -- References -- Chapter 4: Regression Modeling and Analysis -- 4.1. Introduction -- 4.2. Simple Linear Regression -- 4.2.1. Formulating and Solving the Linear Regression Problem -- 4.2.2. Evaluating the Linear Regression Model -- 4.2.3. Properties of the Regression Parameters and Confidence Limits -- 4.2.4. Estimating Confidence Intervals for the Mean Response and Forecast -- 4.2.5. An Illustrative Example of Linear Regression Modeling and Analysis -- 4.3. Multiple Regression -- 4.3.1. Formulating and Solving the Multiple Regression Model -- 4.3.2. Evaluating the Multiple Regression Model -- 4.3.3. How Many Terms in the Regression Model? -- 4.3.4. Analysis of Variance (ANOVA) Table -- 4.3.5. An Illustrative Example of Multiple Regression Modeling and Analysis -- 4.4. Nonparametric Transformation and Regression -- 4.4.1. Conditional Expectation and Scatterplot Smoothers -- 4.4.2. Generalized Additive Models -- 4.4.3. Response Transformation Models: ACE Algorithm and Its Variations -- 4.4.4. Data Correlation via Nonparametric Transformation -- 4.5. Field Application for Nonparametric Regression: The Salt Creek Data Set -- 4.5.1. Dataset Description -- 4.5.2. Variable Selection -- 4.5.3. Optimal Transformations and Optimal Correlation -- 4.6. Summary -- Exercises -- References -- Chapter 5: Multivariate Data Analysis -- 5.1. Introduction -- 5.2. Principal Component Analysis -- 5.2.1. Computing the Principal Components -- 5.2.2. An Illustrative Example of the Principal Component Analysis -- 5.3. Cluster Analysis. , 5.3.1. k-Means Clustering -- An Illustrative Example of k-Means Clustering -- 5.3.2. Hierarchical Clustering -- An Illustrative Example of Hierarchical Clustering -- 5.3.3. Model-Based Clustering -- 5.4. Discriminant Analysis -- An Illustrative Example of Discriminant Analysis -- 5.5. Field Application: The Salt Creek Data Set -- 5.5.1. Dataset Description -- 5.5.2. PCA -- 5.5.3. Cluster Analysis -- 5.5.4. Data Correlation and Prediction -- 5.6. Summary -- Exercises -- References -- Further Reading -- Chapter 6: Uncertainty Quantification -- 6.1. Introduction -- 6.1.1. Deterministic Versus Probabilistic Approach -- 6.1.2. Elements of a Systematic Framework -- 6.1.3. Role of Monte Carlo Simulation -- 6.2. Uncertainty Characterization -- 6.2.1. Screening for Key Uncertain Inputs -- 6.2.2. Fitting Distributions to Data -- 6.2.3. Maximum Entropy Distribution Selection -- 6.2.4. Generation of Subjective Probability Distributions -- 6.2.5. Problem of Scale -- 6.3. Uncertainty Propagation -- 6.3.1. Sampling Methods -- Random Sampling -- Latin Hypercube Sampling -- Correlation Control in LHS -- 6.3.2. Computational Considerations -- Number of Samples -- Visualization of Results -- 6.4. Uncertainty Importance Assessment -- 6.4.1. Basic Concepts in Uncertainty Importance -- 6.4.2. Scatter Plots and Rank Correlation Analysis -- 6.4.3. Stepwise Regression and Partial Rank Correlation Analysis -- 6.4.4. Other Measures of Variable Importance -- Entropy (Mutual Information) Analysis -- Classification Tree Analysis -- 6.5. Moving Beyond Monte Carlo Simulation -- 6.5.1. First-Order Second-Moment Method (FOSM) -- General Expressions for Mean and Variance -- Error Analysis in Additive and Multiplicative Models -- 6.5.2. Point Estimate Method (PEM) -- 6.5.3. Logic Tree Analysis (LTA) -- 6.6. Treatment of Model Uncertainty -- 6.6.1. Basic Concepts. , 6.6.2. Moment-Matching Weighting Method for Geostatistical Models -- 6.6.3. Example Field Application -- 6.7. Elements of a Good Uncertainty Analysis Study -- 6.8. Summary -- Exercises -- References -- Chapter 7: Experimental Design and Response Surface Analysis -- 7.1. General Concepts -- 7.2. Experimental Design -- 7.2.1. Factorial Designs -- Plackett-Burman -- Central Composite and Box-Behnken -- Augmented Pairs -- Comparison of Factorial Designs -- 7.2.2. Sampling Designs -- Purely Random Design -- Latin Hypercube Sampling -- Maximin LHS -- Maximum Entropy Design -- Comparison of Sampling Designs -- 7.3. Metamodeling Techniques -- 7.3.1. Quadratic Model -- 7.3.2. Quadratic Model With LASSO Variable Selection -- 7.3.3. Kriging Model -- 7.3.4. Radial Basis Functions -- 7.3.5. Metamodel Performance Evaluation Metric -- 7.4. An Illustration of Experimental Design and Response Surface Modeling -- 7.5. Field Application of Experimental Design and Response Surface Modeling -- 7.5.1. Problem of Interest -- 7.5.2. Proxy Construction and Application Strategy -- 7.5.3. Field Case Study -- 7.6. Summary -- Exercises -- References -- Further Reading -- Chapter 8: Data-Driven Modeling -- 8.1. Introduction -- 8.1.1. Preliminaries -- 8.1.2. Data-Driven Models-What and Why? -- 8.1.3. Our Philosophy -- 8.2. Modeling Approaches -- 8.2.1. Classification and Regression Trees -- 8.2.2. Random Forest -- 8.2.3. Gradient Boosting Machine -- 8.2.4. Support Vector Machine -- 8.2.5. Artificial Neural Network -- 8.2.6. Model Strengths and Weaknesses -- 8.3. Computational Considerations -- 8.3.1. Model Evaluation -- 8.3.2. Automatic Tuning of Model Parameters -- 8.3.3. Variable Importance -- 8.3.4. Model Aggregation -- 8.4. Field Example -- 8.4.1. Dataset Description -- 8.4.2. Predictive Model Building -- 8.4.3. Variable Importance and Conditional Sensitivity. , 8.4.4. Classification Tree Analysis -- 8.5. Summary -- Exercises -- References -- Chapter 9: Concluding Remarks -- 9.1. The Path We Have Taken -- 9.1.1. Recapitulation of Topics -- 9.1.2. Style and Intended Use -- 9.1.3. Resources -- 9.2. Key Takeaways -- 9.2.1. Which Variables? -- 9.2.2. Simple Model, or Complex? -- 9.2.3. One Model, or Many? -- 9.2.4. Is Past Always Prolog? -- 9.2.5. To Fit, or Overfit? -- 9.3. Final Thoughts -- References -- Index -- Back Cover.

Sprache: Englisch