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Quantifying Uncertainty in Subsurface Systems

Quantifying Uncertainty in Subsurface Systems

Céline Scheidt, Lewis Li, Professor Jef Caers

ISBN: 978-1-119-32588-8

May 2018

288 pages


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. Quantifying Uncertainty in Subsurface Systems will provide a multi-disciplinary treatment of uncertainty quantification towards practical engineering applications and decision making.

Volume highlights include:

  • Five substantial case studies on decision making under uncertainty for subsurface systems
  • Forecasting total volume and future revenue stream in giant oilfields
  • Designing heat pumps for heating buildings in shallow geothermal systems
  • Optimizing data acquisition for uranium contaminated sites
  • Managing aquifer recharge in the groundwater system
  • Characterizing the risk of misclassification of mining blocks in function of drill-hole quality and spacing

Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields of science to tackle real-world problems covering information science, decision science, geosciences, data science and computer science. The volume presents actual field cases with actual data that will appeal to methodology developers e.g. doctoral students, as well as practitioners e.g. industry professionals, and will also be a valuable resource for geoscientists, engineers and applied mathematicians.

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Chapter 1: The Geological 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