TY - JOUR A1 - Ringel, Lisa Maria A1 - Somogyvári, Márk A1 - Jalali, Mohammadreza A1 - Bayer, Peter T1 - Comparison of hydraulic and tracer tomography for discrete fracture network inversion JF - Geosciences N2 - Fractures serve as highly conductive preferential flow paths for fluids in rocks, which are difficult to exactly reconstruct in numerical models. Especially, in low-conductive rocks, fractures are often the only pathways for advection of solutes and heat. The presented study compares the results from hydraulic and tracer tomography applied to invert a theoretical discrete fracture network (DFN) that is based on data from synthetic cross-well testing. For hydraulic tomography, pressure pulses in various injection intervals are induced and the pressure responses in the monitoring intervals of a nearby observation well are recorded. For tracer tomography, a conservative tracer is injected in different well levels and the depth-dependent breakthrough of the tracer is monitored. A recently introduced transdimensional Bayesian inversion procedure is applied for both tomographical methods, which adjusts the fracture positions, orientations, and numbers based on given geometrical fracture statistics. The used Metropolis-Hastings-Green algorithm is refined by the simultaneous estimation of the measurement error’s variance, that is, the measurement noise. Based on the presented application to invert the two-dimensional cross-section between source and the receiver well, the hydraulic tomography reveals itself to be more suitable for reconstructing the original DFN. This is based on a probabilistic representation of the inverted results by means of fracture probabilities. KW - hydraulic tomography KW - tracer tomography KW - DFN KW - Bayesian inversion KW - heterogeneity KW - fracture KW - hydrogeophysics Y1 - 2019 U6 - https://doi.org/10.3390/geosciences9060274 SN - 2076-3263 VL - 9 IS - 6 PB - MDPI CY - Basel 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 - TY - JOUR A1 - De Biase, Cecilia A1 - Maier, Uli A1 - Baeder-Bederski, Oliver A1 - Bayer, Peter A1 - Oswald, Sascha Eric A1 - Thullner, Martin T1 - Removal of volatile organic compounds in vertical flow filters - predictions from reactive transport modeling JF - Ground water monitoring & remediation N2 - Vertical flow filters are containers filled with porous medium that are recharged from top and drained at the bottom, and are operated at partly saturated conditions. They have recently been suggested as treatment technology for groundwater containing volatile organic compounds (VOCs). Numerical reactive transport simulations were performed to investigate the relevance of different filter operation modes on biodegradation and/or volatilization of the contaminants and to evaluate the potential limitation of such remediation mean due to volatile emissions. On the basis of the data from a pilot-scale vertical flow filter intermittently fed with domestic waste water, model predictions on the systems performance for the treatment of contaminated groundwater were derived. These simulations considered the transport and aerobic degradation of ammonium and two VOCs, benzene and methyl tertiary butyl ether (MTBE). In addition, the advective-diffusive gas-phase transport of volatile compounds as well as oxygen was simulated. Model predictions addressed the influence of depth and frequency of the intermittent groundwater injection, degradation rate kinetics, and the composition of the filter material. Simulation results show that for unfavorable operation conditions significant VOC emissions have to be considered and that operation modes limiting VOC emissions may limit aerobic biodegradation. However, a suitable combination of injection depth and composition of the filter material does facilitate high biodegradation rates while only little VOC emissions take place. Using such optimized operation modes would allow using vertical flow filter systems as remediation technology suitable for groundwater contaminated with volatile compounds. Y1 - 2012 U6 - https://doi.org/10.1111/j.1745-6592.2011.01374.x SN - 1069-3629 VL - 32 IS - 2 SP - 106 EP - 121 PB - Wiley-Blackwell CY - Malden ER - 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 -