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The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. Experiment 1 examined hydraulic fracturing in a well-characterized fractured phyllite 1.5 km deep at the Sanford Underground Research Facility (SURF). Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests, and these data are available. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. Experiment 2 examines the potential for hydraulic shearing in amphibolite 1.25 km deep at SURF. The testbed consists of nine subhorizontal boreholes, four of which surround the testbed with grouted-in ERT, seismic sensors, CASSM and distributed fiber sensors. The test wells include a “five-spot” set with an injection well and four production/monitoring wells. Like Experiment 1, the testbed was characterized geophysically and hydrologically, and three stimulations have been performed using new tools.

Reservoir thermal energy storage (RTES) is a promising technology to balance the mismatch between energy supply and demand. In particular, high temperature (HT) RTES can stabilize the grid with increasing penetration of renewable energy generation. This paper presents the investigation of the mechanical deformation and chemical reaction influences on the performance of HT-ATES for the Lower Tuscaloosa site. Thermo-hydraulic (TH), thermo-hydro-mechanical (THM), and thermo-hydro-chemical (THC) coupled simulations were performed with different operational modes and injection rates for a fixed five-spot well configuration and a seasonal cycle. The results show that (1) geomechanical-induced porosity change is mainly contributed by effective stress change, and the porosity change is distributed through the whole system; (2) geochemistry-induced porosity change is located near the hot well, and its change is one order of magnitude higher than the geomechanical effect; (3) both the operation mode and the injection rate have a huge influence on the RTES performance and lower injection rate with push-pull operation mode has the best performance with recovery factor around 70% for this RTES system. These results shed light on the deployment of HT-RTES in the US and around the world.

Engineering a robust hydraulic connection between wells is one of the most difficult aspects of enhanced geothermal systems (EGS). Designing and constructing such hydraulic connections requires an understanding of the in-situ state of stress and the heterogeneities and discontinuities that naturally exist and may control the stimulation. Even with comprehensive stress and formation characterization programs, substantial uncertainty remains in these key parameters. This is especially the case in high-temperature EGS environments where drilling conditions are often difficult and far fewer logging and testing options are available. This paper presents a new approach for explicitly quantifying the uncertainties in the state of stress using a Bayesian Markov Chain Monte Carlo method. This approach produces a probability distribution for the stress tensor, including a general 3D orientation, that reflects the uncertainties in all the observations or indicators used to constrain the stress state. This method is demonstrated using the characterization data for the EGS Collab Experiment 2 site. The output of the analysis is used to guide the design of the planned stimulations. In the case of research projects like EGS Collab, explicitly quantifying the uncertainties in the stress state allows for more rigorous hypothesis testing by allowing conclusions drawn from the experiments to be interpreted in the context of the uncertain knowledge about conditions in the test bed.