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: https:
//eos.org/editors-vox/quantifying-uncertainty-about-earths-resources
Reviews, The Leading Edge, SEG, May 2020 The subsurface medium
created by geologic processes is not always well understood. The
data we collect in an attempt to characterize the subsurface can be
incomplete and inaccurate. However, if we understand the
uncertainty of our data and the models we generate from them, we
can make better decisions regarding the management of subsurface
resources. Modeling and managing subsurface resources, and properly
characterizing and understanding the uncertainties, requires the
integration of a variety of scientific and engineering disciplines.
Five case studies are outlined in the introductory chapter, which
are used to demonstrate various methods throughout the book. The
second chapter introduces the basic notions in decision analysis.
Uncertainty quantification is only relevant within the decision
framework used. Models alone do not quantify uncer-tainty, but do
allow the determination of key variables that influ-ence models and
decisions. Next, an overview of the various data science methods
relevant to uncertainty quantification in the subsurface is
provided. Sensitivity analysis is then covered, specifi-cally Monte
Carlo-based sensitivity analysis. The next three chapters develop
the Bayesian approach to uncertainty quantifica-tion, and this is
the focus of the book. All of this is brought together in Chapter
8, which describes a solution regarding quantifying the
uncertainties for each of the problems presented in the first
chapter. The authors admit that it is not the only solution. No
single solution fits all problems of uncertainty quantification.
The results in this chapter allow the reader to see the previously
described methods applied and how choices influence models and
decisions. The final two chapters discuss various software
components necessary to implement the strategies presented in the
book and challenges faced in the future of uncertainty
quantification. The book uses a number of relevant subsurface
problems to explore the various aspects of uncertainty
quantification. Understanding uncertainty, and how it affects
modeling and decision outcomes, is not always straightforward.
However, it is necessary in order to make good, consistent
decisions. The book is not an easy read. Some portions require good
mathematical understanding of the underlying principles. However,
the book is well documented and organized. I would say that is not
a good book for a beginner, but it is a good resource for someone
to get a grounding to go further into the subject. I appreciate the
authors putting together this book on a complex problem that is
important to our industry. -- David Bartel, Houston, Texas
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