Quantifying Uncertainty in Subsurface Systems
Geophysical Monograph Series, Band 236 1. Aufl.
|Verlag:||American Geophysical Union|
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. Volume highlights include: • A multi-disciplinary 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.
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
Céline Scheidt is senior research engineer at Stanford University with 10 years of experience in this field. She is known for her work on uncertainty quantification using machine learning methods and has published several impactful papers in that area. She will be the keynote speaker of the next international Geostatistics congress. Lewis Li is 3rd year PhD student at Stanford University. He has published three papers, with three more in the pipeline. With an Electrical Engineering degree from Stanford University, he has considerable expertise in software engineering and addressing computational challenges, in Jef Caers is a world-leading expert in quantifying uncertainty in the subsurface, has closely worked on 100+ projects with a variety of industries in this area and has been leading the Stanford Center for Reservoir Forecasting for 15 years, he has been Professor at Stanford University for 19 years.
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