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Fracture network characterization using stress-based tomography

Afshari Moein, M. and Somogyvari, M. and Valley, B. and Jalali, M. and Loew, S. and Bayer, P. (2018):
Fracture network characterization using stress-based tomography.
In: Journal of Geophysical Research - Solid Earth, Wiley - American Geophysical Union, pp. 9324-9340, 123, ISSN 2156-2202; 2169-9356, DOI: 10.1029/2018JB016438, [Article]

Abstract

Information on structural features of a fracture network at early stages of Enhanced Geothermal System (EGS) development is mostly restricted to borehole images and, if available, outcrop data. However, using this information to image discontinuities in deep reservoirs is difficult. Wellbore failure data provides only some information on components of the in‐situ stress state and its heterogeneity. Our working hypothesis is that slip on natural fractures primarily controls these stress heterogeneities. Based on this, we introduce stress‐based tomography in a Bayesian framework to characterize the fracture network and its heterogeneity in potential EGS reservoirs. In this procedure, first a random initial discrete fracture network (DFN) realization is generated based on prior information about the network. The observations needed to calibrate the DFN are based on local variations of the orientation and magnitude of at least one principal stress component along boreholes. A Markov Chain Monte Carlo (MCMC) sequence is employed to update the DFN iteratively by a fracture translation within the domain. The Markov sequence compares the simulated stress profile with the observed stress profiles in the borehole, evaluates each iteration with Metropolis‐Hastings acceptance criteria and stores acceptable DFN realizations in an ensemble. Finally, this obtained ensemble is used to visualize the potential occurrence of fractures in a probability map, indicating possible fracture locations and lengths. We test this methodology to reconstruct simple synthetic and more complex outcrop‐based fracture networks and successfully image the significant fractures in the domain.

Item Type: Article
Erschienen: 2018
Creators: Afshari Moein, M. and Somogyvari, M. and Valley, B. and Jalali, M. and Loew, S. and Bayer, P.
Title: Fracture network characterization using stress-based tomography
Language: English
Abstract:

Information on structural features of a fracture network at early stages of Enhanced Geothermal System (EGS) development is mostly restricted to borehole images and, if available, outcrop data. However, using this information to image discontinuities in deep reservoirs is difficult. Wellbore failure data provides only some information on components of the in‐situ stress state and its heterogeneity. Our working hypothesis is that slip on natural fractures primarily controls these stress heterogeneities. Based on this, we introduce stress‐based tomography in a Bayesian framework to characterize the fracture network and its heterogeneity in potential EGS reservoirs. In this procedure, first a random initial discrete fracture network (DFN) realization is generated based on prior information about the network. The observations needed to calibrate the DFN are based on local variations of the orientation and magnitude of at least one principal stress component along boreholes. A Markov Chain Monte Carlo (MCMC) sequence is employed to update the DFN iteratively by a fracture translation within the domain. The Markov sequence compares the simulated stress profile with the observed stress profiles in the borehole, evaluates each iteration with Metropolis‐Hastings acceptance criteria and stores acceptable DFN realizations in an ensemble. Finally, this obtained ensemble is used to visualize the potential occurrence of fractures in a probability map, indicating possible fracture locations and lengths. We test this methodology to reconstruct simple synthetic and more complex outcrop‐based fracture networks and successfully image the significant fractures in the domain.

Journal or Publication Title: Journal of Geophysical Research - Solid Earth
Volume: 123
Publisher: Wiley - American Geophysical Union
Divisions: 11 Department of Materials and Earth Sciences
11 Department of Materials and Earth Sciences > Earth Science
11 Department of Materials and Earth Sciences > Earth Science > Geothermal Science and Technology
Date Deposited: 31 Oct 2018 12:55
DOI: 10.1029/2018JB016438
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