Quantifying uncertainty in subsurface systems / Céline Scheidt, Lewis Li, Jef Caers.

Includes bibliographical references and index.

Online resource; title from PDF title page (EBSCO, viewed May 4, 2018).

Intro; Title Page; Copyright Page; Contents; Preface; Authors; Chapter 1 The Earth Resources Challenge; 1.1. WHEN CHALLENGES BRING OPPORTUNITIES; 1.2. PRODUCTION PLANNING AND DEVELOPMENT FOR AN OIL FIELD IN LIBYA; 1.2.1. Reservoir Management from Discovery to Abandonment; 1.2.2. Reservoir Modeling; 1.2.3. The Challenge of Addressing Uncertainty; 1.2.4. The Libya Case; 1.3. DECISION MAKING UNDER UNCERTAINTY FOR GROUNDWATER MANAGEMENT IN DENMARK; 1.3.1. Groundwater Management Challenges under Global Change; 1.3.2. The Danish Case; 1.4. MONITORING SHALLOW GEOTHERMAL SYSTEMS IN BELGIUM

1.4.1. The Use of Low-Enthalpy Geothermal Systems1.4.2. Monitoring by Means of Geophysical Surveys; 1.5. DESIGNING STRATEGIES FOR URANIUM REMEDIATION IN THE UNITED STATES; 1.5.1. Global Environmental Challenges; 1.5.2. Remediation: Decision Making Under Uncertainty; 1.5.3. Remediation: Data and Modeling; 1.5.4. Uranium Contamination in the United States; 1.5.5. Assessing Remediation Efficacy; 1.6. DEVELOPING SHALE PLAYS IN NORTH AMERICA; 1.6.1. Introduction; 1.6.2. What are Shales Reservoirs and How are They Produced?; 1.6.3. Shale Development Using Data Science

1.7. SYNTHESIS: DATA-MODEL-PREDICTION-DECISIONREFERENCES; Chapter 2 Decision Making Under Uncertainty; 2.1. INTRODUCTION; 2.2. INTRODUCTORY EXAMPLE: THE THUMBTACK GAME; 2.3. CHALLENGES IN THE DECISION-MAKING PROCESS; 2.3.1. The Decision Analyst; 2.3.2. Organizational Context; 2.4. DECISION ANALYSIS AS A SCIENCE; 2.4.1. Why Decision Analysis Is a Science; 2.4.2. Basic Rules; 2.4.3. Definitions; 2.4.4. Objectives; 2.4.5. Illustrative Example; 2.5. GRAPHICAL TOOLS; 2.5.1. Decision Trees; 2.5.2. Influence Diagrams; 2.6. VALUE OF INFORMATION; 2.6.1. Introduction; 2.6.2. Calculations; REFERENCES

Chapter 3 Data Science for Uncertainty Quantification3.1. INTRODUCTORY EXAMPLE; 3.1.1. Description; 3.1.2. Our Notation Convention; 3.1.3. Variables; 3.2. BASIC ALGEBRA; 3.2.1. Matrix Algebra Notation; 3.2.2. Eigenvalues and Eigenvectors; 3.2.3. Spectral Decomposition; 3.2.4. Quadratic Forms; 3.2.5. Distances; 3.3. BASICS OF UNIVARIATE AND MULTIVARIATE PROBABILITY THEORY AND STATISTICS; 3.3.1. Univariate Transformations; 3.3.2. Kernel Density Estimation; 3.3.3. Properties of Multivariate Distributions; 3.3.4. Characteristic Property; 3.3.5. The Multivariate Normal Distribution

3.4. DECOMPOSITION OF DATA3.4.1. Data Spaces; 3.4.2. Cartesian Space of Size L: The Sample Size; 3.4.3. Cartesian Space of Size N: Sample Dimension; 3.4.4. Relationship Between Two Spaces; 3.5. ORTHOGONAL COMPONENT ANALYSIS; 3.5.1. Principal Component Analysis; 3.5.2. Multidimensional Scaling; 3.5.3. Canonical Correlation Analysis; 3.6. FUNCTIONAL DATA ANALYSIS; 3.6.1. Introduction; 3.6.2. A Functional Basis; 3.6.3. Functional PCA; 3.7. REGRESSION AND CLASSIFICATION; 3.7.1. Introduction; 3.7.2. Multiple Linear Regression; 3.7.3. Support Vector Machines

Under the Earth's surface is a rich array of geological resources, many with potential use to humankind. However, extracting and harnessing them comes with enormous uncertainties, high costs, and considerable risks. The valuation of subsurface resources involves assessing discordant factors to produce a decision model that is functional and sustainable. This volume provides real-world examples relating to oilfields, geothermal systems, contaminated sites, and aquifer recharge.