skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Deep geothermal: The ‘Moon Landing’ mission in the unconventional energy and minerals space

Abstract

Deep geothermal from the hot crystalline basement has remained an unsolved frontier for the geothermal industry for the past 30 years. This poses the challenge for developing a new unconventional geomechanics approach to stimulate such reservoirs. While a number of new unconventional brittle techniques are still available to improve stimulation on short time scales, the astonishing richness of failure modes of longer time scales in hot rocks has so far been overlooked. These failure modes represent a series of microscopic processes: brittle microfracturing prevails at low temperatures and fairly high deviatoric stresses, while upon increasing temperature and decreasing applied stress or longer time scales, the failure modes switch to transgranular and intergranular creep fractures. Accordingly, fluids play an active role and create their own pathways through facilitating shear localization by a process of time-dependent dissolution and precipitation creep, rather than being a passive constituent by simply following brittle fractures that are generated inside a shear zone caused by other localization mechanisms. We lay out a new paradigm for reservoir stimulation by reactivating pre-existing faults at reservoir scale in a reservoir scale aseismic, ductile manner. A side effect of the new “soft” stimulation method is that owing to the design specificationmore » of a macroscopic ductile response, the proposed method offers the potential of a safer control over the stimulation process compared to conventional stimulation protocols such as currently employed in shale gas reservoirs.« less

Authors:
 [1];  [2];  [3];  [3];  [4];  [5];  [6];  [3];  [7];  [8];  [9];  [3];  [10];  [6];  [11];  [5];  [12];  [6];  [6];  [13] more »;  [14];  [6] « less
  1. Univ. of New South Wales, Sydney, NSW (Australia). School of Petroleum Engineering; Commonwealth Scientific and Industrial Research Organization (CSIRO), Kensington WA (Australia). Earth Science and Resource Engineering; Univ. of Western Australia, Perth, WA (Australia). School of Earth and Environment
  2. Univ. of Pittsburgh, PA (United States). Dept of Civil and Environmental Engineering and Dept. of Chemical and Petroleum Engineering
  3. Univ. of New South Wales, Sydney, NSW (Australia). School of Petroleum Engineering
  4. Univ. of Edinburgh, Scotland (United Kingdom). School of Geosciences
  5. Univ. of New South Wales, Sydney, NSW (Australia). School of Petroleum Engineering; Queensland Univ. of Technology, Brisbane (Australia). School of Earth, Environmental and Biological Sciences, Earth Systems
  6. Commonwealth Scientific and Industrial Research Organization (CSIRO), Kensington WA (Australia). Earth Science and Resource Engineering
  7. Karlsruhe Inst. of Technology (KIT) (Germany)
  8. Univ. of New South Wales, Sydney, NSW (Australia). School of Petroleum Engineering; Sun Yat-Sen Univ., Guangzhou, (China). School of Earth Science and Geological Engineering
  9. Geological Survey of Israel, Jerusalem (Israel)
  10. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  11. Univ. of Western Australia, Perth, WA (Australia). School of Earth and Environment
  12. Commonwealth Scientific and Industrial Research Organization (CSIRO), Floreat Park, WA (Australia). Land and Water
  13. Univ. of Geosciences, Wuhan (China). School of Environmental Studies; Univ. of Minnesota, Minneapolis, MN (United States). Dept. of Earth Sciences and Minnesota Supercomputing Inst.
  14. RWTH Aachen Univ. (Germany). Aachen Inst. for Advanced Study in Computational Engineering Science (AICES)
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1177610
Report Number(s):
INL/JOU-14-33317
Journal ID: ISSN 1674-487X; PII: 515
Grant/Contract Number:
AC07-05ID14517
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Earth Science
Additional Journal Information:
Journal Volume: 26; Journal Issue: 1; Journal ID: ISSN 1674-487X
Publisher:
China University of Geosciences
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; CREEP; DISSOLUTION; FRACTURE MECHANICS; GEOTHERMAL ENERGY; PRECIPITATION; Creep; Dissolution; Enhanced Geothermal Systems; Fracture Mechanics; Geothermal Energy; Precipitation

Citation Formats

Regenauer-Lieb, Klaus, Bunger, Andrew, Chua, Hui Tong, Dyskin, Arcady, Fusseis, Florian, Gaede, Oliver, Jeffrey, Rob, Karrech, Ali, Kohl, Thomas, Liu, Jie, Lyakhovsky, Vladimir, Pasternak, Elena, Podgorney, Robert, Poulet, Thomas, Rahman, Sheik, Schrank, Christoph, Trefry, Mike, Veveakis, Manolis, Wu, Bisheng, Yuen, David A., Wellmann, Florian, and Zhang, Xi. Deep geothermal: The ‘Moon Landing’ mission in the unconventional energy and minerals space. United States: N. p., 2015. Web. doi:10.1007/s12583-015-0515-1.
Regenauer-Lieb, Klaus, Bunger, Andrew, Chua, Hui Tong, Dyskin, Arcady, Fusseis, Florian, Gaede, Oliver, Jeffrey, Rob, Karrech, Ali, Kohl, Thomas, Liu, Jie, Lyakhovsky, Vladimir, Pasternak, Elena, Podgorney, Robert, Poulet, Thomas, Rahman, Sheik, Schrank, Christoph, Trefry, Mike, Veveakis, Manolis, Wu, Bisheng, Yuen, David A., Wellmann, Florian, & Zhang, Xi. Deep geothermal: The ‘Moon Landing’ mission in the unconventional energy and minerals space. United States. doi:10.1007/s12583-015-0515-1.
Regenauer-Lieb, Klaus, Bunger, Andrew, Chua, Hui Tong, Dyskin, Arcady, Fusseis, Florian, Gaede, Oliver, Jeffrey, Rob, Karrech, Ali, Kohl, Thomas, Liu, Jie, Lyakhovsky, Vladimir, Pasternak, Elena, Podgorney, Robert, Poulet, Thomas, Rahman, Sheik, Schrank, Christoph, Trefry, Mike, Veveakis, Manolis, Wu, Bisheng, Yuen, David A., Wellmann, Florian, and Zhang, Xi. Fri . "Deep geothermal: The ‘Moon Landing’ mission in the unconventional energy and minerals space". United States. doi:10.1007/s12583-015-0515-1. https://www.osti.gov/servlets/purl/1177610.
@article{osti_1177610,
title = {Deep geothermal: The ‘Moon Landing’ mission in the unconventional energy and minerals space},
author = {Regenauer-Lieb, Klaus and Bunger, Andrew and Chua, Hui Tong and Dyskin, Arcady and Fusseis, Florian and Gaede, Oliver and Jeffrey, Rob and Karrech, Ali and Kohl, Thomas and Liu, Jie and Lyakhovsky, Vladimir and Pasternak, Elena and Podgorney, Robert and Poulet, Thomas and Rahman, Sheik and Schrank, Christoph and Trefry, Mike and Veveakis, Manolis and Wu, Bisheng and Yuen, David A. and Wellmann, Florian and Zhang, Xi},
abstractNote = {Deep geothermal from the hot crystalline basement has remained an unsolved frontier for the geothermal industry for the past 30 years. This poses the challenge for developing a new unconventional geomechanics approach to stimulate such reservoirs. While a number of new unconventional brittle techniques are still available to improve stimulation on short time scales, the astonishing richness of failure modes of longer time scales in hot rocks has so far been overlooked. These failure modes represent a series of microscopic processes: brittle microfracturing prevails at low temperatures and fairly high deviatoric stresses, while upon increasing temperature and decreasing applied stress or longer time scales, the failure modes switch to transgranular and intergranular creep fractures. Accordingly, fluids play an active role and create their own pathways through facilitating shear localization by a process of time-dependent dissolution and precipitation creep, rather than being a passive constituent by simply following brittle fractures that are generated inside a shear zone caused by other localization mechanisms. We lay out a new paradigm for reservoir stimulation by reactivating pre-existing faults at reservoir scale in a reservoir scale aseismic, ductile manner. A side effect of the new “soft” stimulation method is that owing to the design specification of a macroscopic ductile response, the proposed method offers the potential of a safer control over the stimulation process compared to conventional stimulation protocols such as currently employed in shale gas reservoirs.},
doi = {10.1007/s12583-015-0515-1},
journal = {Journal of Earth Science},
number = 1,
volume = 26,
place = {United States},
year = {Fri Jan 30 00:00:00 EST 2015},
month = {Fri Jan 30 00:00:00 EST 2015}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 5works
Citation information provided by
Web of Science

Save / Share:
  • The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learningmore » systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.« less