Quantifying Uncertainty in Subsurface Systems

Name: Quantifying Uncertainty in Subsurface Systems
Brand: Jossey-Bass
Price: 161.99 EUR
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Céline Scheidt Lewis Li Jef Caers(Herausgeber*in)

Jossey-Bass (Verlag)

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Erschienen am 27. April 2018

304 Seiten

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Inhalt

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 Systems
1.4.2. 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-DECISION
REFERENCES
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 Quantification
3.1. 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 DATA
3.4.1. 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
3.7.4. CART: Classification and Regression Trees
3.7.5. Gaussian Process Regression: Kriging
3.8. KERNEL METHODS
3.8.1. Introduction
3.8.2. Kernel-Based Mapping
3.8.3. Kernel PCA
3.9. CLUSTER ANALYSIS
3.9.1. k-Means
3.9.2. k-Medoids
3.9.3. Kernel Methods for Clustering
3.9.4. Choosing the Number of Clusters
3.9.5. Application
3.10 MONTE CARLO AND QUASI MONTE CARLO
3.10.1. Introduction
3.10.2. Sampling from Known Distributions
3.10.3. Variance Reduction Methods
3.11. SEQUENTIAL MC
3.11.1. Problem Formulation
3.11.2. Sequential Importance Resampling
3.12. MARKOV CHAIN MC
3.12.1. Motivation
3.12.2. Random Walk
3.12.3. Gibbs Sampling
3.12.4. Metropolis-Hastings Sampler
3.12.5. Assessing Convergence
3.12.6. Approximate Bayesian Computation
3.12.7. Multichain McMC
3.13. THE BOOTSTRAP
3.13.1. Introduction
3.13.2. Nonparametric Bootstrap
3.13.3. Bootstrap with Correlated Data
3.13.4. Bootstrap Confidence Intervals and Hypothesis Testing
REFERENCES
Chapter 4 Sensitivity Analysis
4.1. INTRODUCTION
4.2. NOTATION AND APPLICATION EXAMPLE
4.3. SCREENING TECHNIQUES
4.3.1. OAT Method
4.3.2. Morris Method
4.4. GSA METHODS
4.4.1. SA Based on Linear Models
4.4.2. Variance-Based Methods/Measures of Importance
4.4.3. Generalized/Regionalized SA
4.4.4. Tree-Based SA
4.5. QUANTIFYING IMPACT OF STOCHASTICITY IN MODELS
4.6. SUMMARY
REFERENCES
Chapter 5 Bayesianism
5.1. INTRODUCTION
5.2. A HISTORICAL PERSPECTIVE
5.3. SCIENCE AS KNOWLEDGE DERIVED FROM FACTS, DATA, OR EXPERIENCE
5.4. THE ROLE OF EXPERIMENTS: DATA
5.5. INDUCTION VERSUS DEDUCTION
5.6. FALSIFICATIONISM
5.6.1. A Reaction to Induction
5.6.2. Falsificationism in Statistics
5.6.3. Limitations of Falsificationism
5.7. PARADIGMS
5.7.1. Thomas Kuhn
5.7.2. Is Probability Theory the Only Paradigm for UQ?
5.8. BAYESIANISM
5.8.1. Thomas Bayes
5.8.2. Rationality for Bayesianism
5.8.3. Objective Versus Subjective Probabilities
5.8.4. Bayes with Ad-Hoc Modifications
5.8.5. Criticism of Bayesianism
5.8.6. Deductive Testing of Inductive Bayesianism
5.9. BAYESIANISM IN GEOLOGICAL SCIENCES
5.9.1. Introduction
5.9.2. What Is the Nature of Geological Priors?
5.9.3. Moving Forward
REFERENCES
Chapter 6 Geological Priors and Inversion
6.1. INTRODUCTION
6.2. THE GENERAL DISCRETE INVERSE PROBLEM
6.2.1. Introduction
6.2.2. Representation of Physical Variables in Probability Theory
6.2.3. Conjunction of Information
6.2.4. More Limited Formulations
6.3. PRIOR MODEL PARAMETERIZATION
6.3.1. The Prior Movie
6.3.2. Gridding
6.3.3. Process-Based Prior Model
6.3.4. Geostatistics
6.3.5. Non-Grid-Based Prior Movies
6.3.6. Dimension Reduction and Model Expansion from a Limited Prior Movie
6.4. DETERMINISTIC INVERSION
6.4.1. Linear Least Squares: General Formulation
6.4.2. Regularization
6.4.3. Resolution
6.4.4. Kalman Filter
6.4.5. Nonlinear Inversion
6.4.6. Conceptual Overview of Various Model Assumptions
6.4.7. Illustration of Deterministic Inversion
6.5. BAYESIAN INVERSION WITH GEOLOGICAL PRIORS
6.5.1. Inversion of Surface-Based Geological Structures
6.5.2. Inversion for Grid-Based Geological Structures
6.6. GEOLOGICAL PRIORS IN GEOPHYSICAL INVERSION
6.6.1. Introduction
6.6.2. Creating the Geophysical Image
6.6.3. Rock Physics: Linking the Image with Properties
6.6.4. Workflows and Role in UQ
6.7. GEOLOGICAL PRIORS IN ENSEMBLE FILTERING METHODS
REFERENCES
Chapter 7 Bayesian Evidential Learning
7.1. THE PREDICTION PROBLEM REVISITED
7.2. COMPONENTS OF STATISTICAL LEARNING
7.2.1. The Statistical Model
7.2.2. Generating the Training Set
7.2.3. Dimension Reduction and Feature Extraction
7.2.4. Regression Analysis
7.2.5. How Much Can We Trust the Statistical Model?
7.3. BAYESIAN EVIDENTIAL LEARNING IN PRACTICE
7.3.1. Predicting Dynamic from Dynamic
7.3.2. Predicting Static from Dynamic
7.3.3. Predicting Dynamic from Static
7.3.4. Predicting Static from Static
7.3.5. Summary
REFERENCES
Chapter 8 Quantifying Uncertainty in Subsurface Systems
8.1. INTRODUCTION
8.2. PRODUCTION PLANNING AND DEVELOPMENT FOR AN OIL FIELD IN LIBYA
8.2.1. Three Decision Scenarios and a Strategy for UQ
8.2.2. The Prior
8.2.3. Uncertainty Quantification
8.2.4. Decision Making
8.3. DECISION MAKING UNDER UNCERTAINTY FOR GROUNDWATER MANAGEMENT IN DENMARK
8.3.1. A Strategy for UQ
8.3.2. Designing a Monte Carlo Study
8.3.3. Sensitivity Analysis
8.3.4. Uncertainty Reduction
8.3.5. Decision Model
8.4. MONITORING SHALLOW GEOTHERMAL SYSTEMS IN BELGIUM
8.4.1. A Strategy for UQ
8.4.2. Deterministic Prediction with Local Sensitivity Analysis
8.4.3. Bayesian Prediction with Global Sensitivity Analysis
8.5. DESIGNING URANIUM CONTAMINANT REMEDIATION IN THE UNITED STATES
8.5.1. A Strategy for UQ
8.5.2. Prior Distributions
8.5.3. Monte Carlo
8.5.4. Dimension Reduction on Data and Prediction Variables
8.5.5. Global Sensitivity Analysis
8.5.6. Correlation Analysis and UQ
8.6. DEVELOPING SHALE PLAYS IN NORTH AMERICA
8.6.1. Strategy for UQ
8.6.2. Data Preprocessing
8.6.3. SC1: Functional Spatial Regression
8.6.4. SC2: Functional Spatial Regression with Covariates
REFERENCES
Chapter 9 Software and Implementation
9.1. INTRODUCTION
9.2. MODEL GENERATION
9.2.1. Monte Carlo Sampling
9.2.2. The Need for Automation
9.3. FORWARD SIMULATION
9.3.1. The Need for Parallelism
9.3.2. Proxy Simulators
9.4. POST-PROCESSING
9.4.1. Companion Code Technologies
9.4.2. Deployment Considerations
REFERENCES
Chapter 10 Outlook
10.1. INTRODUCTION
10.2. SEVEN QUESTIONS
10.2.1. Bayes or Not Bayes?
10.2.2. How to Turn Geological Knowledge into Quantitative Numerical Models?
10.2.3. What Is a Proper Model Parameterization?
10.2.4. How to Establish a Realistic Prior Distribution?
10.2.5. What Combination of Data Is Needed and for What Purpose?
10.2.6. How to Deal with the Computational and Software Issues?
10.2.7. What Educational Means Can We Design to Teach UQ?
Index
EULA

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