Quantifying Uncertainty in Subsurface Systems

1. Auflage 2018
ISBN: 978-1-119-32583-3
Verlag: Wiley

Buch, Englisch, 304 Seiten, Format (B × H): 225 mm x 288 mm, Gewicht: 1019 g

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.

Read the Editors' Vox: eos.org/editors-vox/quantifying-uncertainty-about-earths-resources

Autoren/Hrsg.

Scheidt, Céline

Li, Lewis

Caers, Jef

Fachgebiete

• Geowissenschaften Geologie Wirtschaftsgeologie
• Technische Wissenschaften Energietechnik | Elektrotechnik Technologien für Fossile Energieträger
• Geowissenschaften Geologie Geologie
• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik

Inhaltsverzeichnis

Preface vii

Authors xi

1. The Earth Resources Challenge 1

2. Decision Making Under Uncertainty 29

3. Data Science for Uncertainty Quantification 45

4. Sensitivity Analysis 107

5. Bayesianism 129

6. Geological Priors and Inversion 155

7. Bayesian Evidential Learning 193

8. Quantifying Uncertainty in Subsurface Systems 217

9. Software and Implementation 263

10. Outlook 267

Index 273

Über Autor(innen)

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 in addressing computational challenges.

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.