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. 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. INDICE: 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

• ISBN: 978-1-119-32583-3
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 288
• Fecha Publicación: 15/06/2018
• Nº Volúmenes: 1
• Idioma: Inglés