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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

High levels of uncertainty are a trademark of geological investigations, such as the search for oil, diamonds, and uranium. So business ventures related to geology, such as mineral exploration and mining, are naturally associated with higher risks than more traditional entrepreneurial ventures in industry and economy. There are also a number of dangerous natural hazards, e.g. earthquakes, volcanic activities, and inundations, that are the direct result of geological processes. It is of paramount interest to study them all, to describe them, to understand their origin and - if possible - to predict them. While uncertainties, geological risks and natural hazards are often mentioned in geological textbooks, conferences papers, and articles, no comprehensive and systematic evaluation has so far been attempted. This book, written at an appropriately sophisticated level to deal with complexity of these problems, presents a detailed evaluation of the entire problem, discussing it from both, the geological and the mathematical aspects.

Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of'engineering'the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.

Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics. The authors have written an expository text, emphasizing statistical methods and applications rather than the underlying mathematical theory. Analyses of datasets from a range of scientific contexts feature prominently, and simulations are used to illustrate theoretical results. Readers can reproduce most of the computational results in the book by using the authors'R-based software package, geoR, whose usage is illustrated in a computation section at the end of each chapter. The book assumes a working knowledge of classical and Bayesian methods of inference, linear models, and generalized linear models, but does not require previous exposure to spatial statistical models or methods. The authors have used the material in MSc-level statistics courses.

Shared Earth Modeling introduces the reader to the processes and concepts needed to develop shared earth models. Shared earth modeling is a cutting-edge methodology that offers a synthesis of modeling paradigms to the geoscientist and petroleum engineer to increase reservoir output and profitability and decrease guesswork. Topics range from geology, petrophysics, and geophysics to reservoir engineering, reservoir simulation, and reservoir management.Shared Earth Modeling is a technique for combining the efforts of reservoir engineers, geophysicists, and petroleum geologists to create a simulation of a reservoir. Reservoir engineers, geophysicists, and petroleum geologists can create separate simulations of a reservoir that vary depending on the technology each scientist is using. Shared earth modeling allows these scientists to consolidate their findings and create an integrated simulation. This gives a more realistic picture of what the reservoir actually looks like, and thus can drastically cut the costs of drilling and time spent mapping the reservoir. - First comprehensive publication about Shared Earth Modeling - Details cutting edge methodology that provides integrated reservoir simulations