Quantifying Uncertainty in Subsurface Systems
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Quantifying Uncertainty in Subsurface Systems

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eBook - ePub

Quantifying Uncertainty in Subsurface Systems

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About This Book

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

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Yes, you can access Quantifying Uncertainty in Subsurface Systems by Céline Scheidt, Lewis Li, Jef Caers in PDF and/or ePUB format, as well as other popular books in Sciences physiques & Géophysique. We have over one million books available in our catalogue for you to explore.

Information

Year
2018
ISBN
9781119325864

1
The Earth Resources Challenge

Co-Authored by: Troels Norvin Vilhelmsen1, Kate Maher2, Carla Da Silva3, Thomas Hermans4, Ognjen Grujic5, Jihoon Park5, and Guang Yang5
1 Department of Geoscience, Aarhus University, Aarhus, Denmark
2 Department of Geological Sciences, Stanford University, Stanford, CA, USA
3 Anadarko, The Woodlands, TX, USA
4 University of Liege, Liege, Belgium
5 Department of Energy Resources Engineering, Stanford University, Stanford, CA, USA

1.1. WHEN CHALLENGES BRING OPPORTUNITIES

Humanity is facing considerable challenges in the 21st century. Population is predicted to grow well into this century and saturate between 9 and 10 billion somewhere in the later part. This growth has led to climate change (see the latest IPCC reports), has impacted the environment, and has affected ecosystems locally and globally around the planet. Virtually no region exists where humans have had no footprint of some kind [Sanderson et al., 2002]; we now basically “own” the ecosystem, and we are not always a good Shepard. An increasing population will require an increasing amount of resources, such as energy, food, and water. In an ideal scenario, we would transform the current situation of unsustainable carbon‐emitting energy sources, polluting agricultural practices and contaminating and over‐exploiting drinking water resources, into a more sustainable and environmentally friendly future. Regardless of what is done (or not), this will not be an overnight transformation. For example, natural gas, a green‐house gas (either as methane or burned into CO2), is often called the blue energy toward a green future. But its production from shales (with vast amounts of gas and oil reserves, 7500 Tcf of gas, 400 billion barrels of oil, US Energy Information, December 2014) has been questioned for its effect on the environment from gas leaks [Howarth et al., 2014] and the unsolved problem of dealing with the waste water it generates. Injecting water into kilometer‐deep wells has caused significant earthquakes [Whitaker, 2016], and risks to contamination of the groundwater system are considerable [Osborn et al., 2011].
Challenges bring opportunities. The Earth is rich in resources, and humanity has been creative and resourceful in using the Earth to advance science and technology. Batteries offer promising energy storage devices that can be connected to intermittent energy sources such as wind and solar. Battery technology will likely develop further from a better understanding of Earth materials. The Earth provides a naturally emitting heat source that can be used for energy creation or heating of buildings. In this book, we will contribute to exploration and exploitation of geological resources. The most common of such resources are briefly described in the following:
  1. Fossil fuels will remain an important energy source for the next several decades. Burning fossil fuels is not a sustainable practice. Hence, the focus will be on the transformation of this energy, least impacting the environment as possible. An optimal exploitation, by minimizing drilling, will require a better understanding of the risk associated with the exploration and production. Every mistake (drilling and spilling) made by an oil company has an impact on the environment, direct or indirect. Even if fossil fuels will be in the picture for a while, ideally we will develop these resources as efficient as possible, minimally impacting the environment.
  2. Heat can be used to generate steam, drive turbines, and produce energy (high enthalpy heat systems). However, the exploitation of geothermal systems is costly and not always successful. Injecting water into kilometer‐deep wells may end up causing earthquakes [Glanz, 2009]. Reducing this risk is essential to a successful future for geothermal energy. In a low enthalpy system, the shallow subsurface can be used as a heat exchanger, for example through groundwater, to heat buildings. The design of such systems is dependent on how efficient heat can be exchanged with groundwater that sits in a heterogeneous system, and the design is often subject to a natural gradient.
  3. Groundwater is likely to grow as a resource for drinking water. As supply of drinking water, this resource is however in competition with food (agriculture) and energy (e.g., from shales). Additionally, the groundwater system is subject to increased stresses such as from over‐pumping and contamination.
  4. Minerals resources are exploited for a large variety of reasons. For example, the use of Cu/Fe in infrastructure, Cd/Li/Co/Ni for batteries, rare earth elements for amplifiers in fiber‐optic data transmission or mobile devices, to name just a few. An increase in the demand will require the development of mining practices that have minimal effect on the environment, such as properly dealing with waste as well as avoiding groundwater contamination.
  5. Storage of fluids such as natural gas, CO2, or water (aquifer storage and recovery) in the subsurface is an increasing practice. The porous subsurface medium acts as a permanent or temporary storage of resources. However, risks of contamination or loss need to be properly understood.
The geological resource challenge will require developing basic fields of science, applied science and engineering, economic decision models, as well as creating a better understanding regarding human behavioral aspects. The ultimate aim here is to “predict” what will happen, and based on such prediction what are best practices in terms of optimal exploitation, maximizing sustainability, and minimizing of impact on the environment. The following are the several areas that require research: (i) fundamental science, (ii) predictive models, (iii) data science, and (iv) economic and human behavior models.
Fundamental science. Consider, for example, the management of groundwater system. The shallow subsurface can be seen as a biogeochemical system where biological, chemical agents interact with the soils or rock within which water resides. The basic reactions of these agents may not yet be fully understood nor does the flow of water when such interactions take place. To understand this better, we will further need to develop such understanding based on laboratory experiments and first principles. Additionally, the flow in such system...

Table of contents

  1. COVER
  2. PREFACE
  3. AUTHORS
  4. 1 The Earth Resources Challenge
  5. 2 Decision Making Under Uncertainty
  6. 3 Data Science for Uncertainty Quantification
  7. 4 Sensitivity Analysis
  8. 5 Bayesianism
  9. 6 Geological Priors and Inversion
  10. 7 Bayesian Evidential Learning
  11. 8 Quantifying Uncertainty in Subsurface Systems
  12. 9 Software and Implementation
  13. 10 Outlook
  14. INDEX
  15. END USER LICENSE AGREEMENT