Quantifying Uncertainty in Subsurface Systems

ISBN-13: 9781119325833 / Angielski / Twarda / 2018 / 304 str.

Jef Caers; C. Line Scheidt; Lewis Li

Opis

The integration of subsurface systems involves weighing the enormous uncertainties of harnessing the Earth's geological resources with high costs and considerable risks. Such valuation involves assessing discordant factors to produce a decision model that is functional and sustainable.

Spis treści

Chapter 1: The Earth Resources Challenge

1.1 When challenges bring opportunities

1.2 Production planning and development for an oil field in Libya

1.3 Decision making under uncertainty for groundwater management in Denmark

1.4 Monitoring shallow geothermal systems in Belgium

1.5 Designing strategies for uranium remediation in the USA

1.6 Developing shale plays in North America

1.7 Synthesis: Data–Model–Prediction–Decision

1.8 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

2.4 Decision analysis as a science

2.5 Graphical tools

2.6 Value of information

2.7 References

Chapter 3: Data Science for Geoscience

3.1 Introductory example

3.2 Basic Algebra

3.3 Basics of univariate & multi–variate probability theory & statistics

3.4 Decomposition of data

3.5 Orthogonal component analysis

3.6 Functional data analysis

3.7 Regression and Classification

3.8 Kernel methods

3.9 Cluster analysis

3.10 Monte Carlo & quasi Monte Carlo

3.11 Sequential Monte Carlo

3.12 Markov chain Monte Carlo

3.13 The bootstrap

3.14 References

Chapter 4: Sensitivity Analysis

4.1 Introduction

4.2 Notation and application example

4.3 Screening techniques

4.4 Global SA methods

4.5 Quantifying impact of stochasticity in models

4.6 Summary

4.7 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 vs deduction

5.6 Falsificationism

5.7 Paradigms

5.8 Bayesianism

5.9 Bayesianism in geological sciences

5.10 References

Chapter 6: Geological priors & inversion

6.1 Introduction

6.2 The general discrete inverse problem

6.3 Prior model parameterization

6.4 Deterministic inversion

6.5 Bayesian inversion with geological priors

6.6 Geological priors in geophysical inversion

6.7 Geological priors in ensemble filtering methods

6.8 References

Chapter 7: Bayesian Evidential Learning

7.1 The prediction problem revisited

7.2 Components of statistical learning

7.3 Bayesian Evidential Learning in Practice

7.4 References

Chapter 8: Quantifying uncertainty in subsurface systems

8.1 Introduction

8.2 Production planning and development for an oil field in Libya

8.3 Decision making under uncertainty for groundwater management in Denmark

8.4 Monitoring shallow geothermal systems in Belgium

8.5 Designing uranium contaminant remediation in the USA

8.6 Developing shale plays in North America

8.7 References

Chapter 9: Software & Implementation

9.1 Introduction

9.2 Model Generation

9.3 Forward Simulation

9.4 Post–Processing

9.5 References

Chapter 10: Outlook

10.1 Introduction

10.2 Seven questions

O autorze

Céline Scheidt, Lewis Li, Jef Caers, Stanford University, USA

Blurb

Quantifying Uncertainty in Subsurface Systems

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. Quantifying Uncertainty in Subsurface Systems provides real–world examples relating to oilfields, geothermal systems, contaminated sites, and aquifer recharge.

Volume highlights include:

• A multidisciplinary treatment of uncertainty quantification
• Case studies with actual data that will appeal to methodology developers
• A Bayesian evidential learning framework that reduces computation and modeling time

Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science, and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers, and applied mathematicians.