Fracture Network Characterization Using Stress‐Based Tomography. Issue 11 (28th November 2018)

• Record Type: Journal Article
• Authors: Afshari Moein, Mohammad Javad; Somogyvári, Márk; Valley, Benoît; Jalali, Mohammadreza; Loew, Simon; Bayer, Peter
• Is Part Of: Journal of geophysical research. Volume 123:Issue 11(2018)
• Page Start: 9324
• Page End: 9340
• Publication Date: 2018-11-28
• Subjects: fracture network -- Bayesian inversion -- stress variability -- rock mechanics; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2018JB016438
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: Information on structural features of a fracture network at early stages of Enhanced Geothermal System 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 Enhanced Geothermal System 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 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. Key Points: We introduce stress‐based tomography as a method to characterize the fracture network and its heterogeneity in deep geothermal reservoirs Stress heterogeneities at borehole scale are applied to calibrate DFN realizations The methodology is successfully tested to image simple synthetic and outcrop‐based fracture networks