TY - JOUR A1 - Afshari Moein, Mohammad J. A1 - Somogyvári, Márk A1 - Valley, Benoît A1 - Jalali, Mohammadreza A1 - Löw, Simon A1 - Bayer, Peter T1 - Fracture network characterization using stress-based tomography JF - Journal of geophysical research : JGR N2 - 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. KW - fracture network KW - Bayesian inversion KW - stress variability KW - rock mechanics Y1 - 2018 U6 - https://doi.org/10.1029/2018JB016438 SN - 2169-9313 SN - 2169-9356 VL - 123 IS - 11 SP - 9324 EP - 9340 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Kong, Xiang-Zhao A1 - Deuber, Claudia A. A1 - Kittilä, Anniina A1 - Somogyvári, Márk A1 - Mikutis, Gediminas A1 - Bayer, Peter A1 - Stark, Wendelin J. A1 - Saar, Martin O. T1 - Tomographic Reservoir Imaging with DNA-Labeled Silica Nanotracers: The First Field Validation JF - Environmental science & technology N2 - This study presents the first field validation of using DNA-labeled silica nanoparticles as tracers to image subsurface reservoirs by travel time based tomography. During a field campaign in Switzerland, we performed short-pulse tracer tests under a forced hydraulic head gradient to conduct a multisource-multireceiver tracer test and tomographic inversion, determining the two-dimensional hydraulic conductivity field between two vertical wells. Together with three traditional solute dye tracers, we injected spherical silica nanotracers, encoded with synthetic DNA molecules, which are protected by a silica layer against damage due to chemicals, microorganisms, and enzymes. Temporal moment analyses of the recorded tracer concentration breakthrough curves (BTCs) indicate higher mass recovery, less mean residence time, and smaller dispersion of the DNA-labeled nanotracers, compared to solute dye tracers. Importantly, travel time based tomography, using nanotracer BTCs, yields a satisfactory hydraulic conductivity tomogram, validated by the dye tracer results and previous field investigations. These advantages of DNA-labeled nanotracers, in comparison to traditional solute dye tracers, make them well-suited for tomographic reservoir characterizations in fields such as hydrogeology, petroleum engineering, and geothermal energy, particularly with respect to resolving preferential flow paths or the heterogeneity of contact surfaces or by enabling source zone characterizations of dense nonaqueous phase liquids. Y1 - 2018 U6 - https://doi.org/10.1021/acs.est.8b04367 SN - 0013-936X SN - 1520-5851 VL - 52 IS - 23 SP - 13681 EP - 13689 PB - American Chemical Society CY - Washington ER -