![]() ![]() A Markov Chain Monte Carlo sequence is employed to update the DFN iteratively by a fracture translation within the domain. 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. In this procedure, first a random initial discrete fracture network (DFN) realization is generated based on prior information about the network. 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. ![]() Our working hypothesis is that slip on natural fractures primarily controls these stress heterogeneities. Wellbore failure data provides only some information on components of the in situ stress state and its heterogeneity. However, using this information to image discontinuities in deep reservoirs is difficult. 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.
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