Identifying fluid flow maldistribution in planar geometries is a well-established problem in subsurface science/engineering. Of particular importance to the thermal performance of Engineered (or “Enhanced”) Geothermal Systems (EGS) is identifying the existence of non-uniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a Genetic Algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two-dimensional Hele-Shaw geometry (i.e., parallel-plates). The inverse problem is solved by minimizing the L2-norm between simulated Residence Time Distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C-Dot nanoparticle). Principal Component Analysis (PCA) of spatially-correlated permeability fields enabled reduction of the parameter space by more than a factor of ten and restricted the inverse search to reservoir-scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding-plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

Cornell University aspires to fully heat and cool its main campus with renewable energy. Cornell is a 30,000-person community, in >14 million ft2 of buildings, that annually consumes ~240,000 MWth-hrs of heat. Successful geothermal district heating at Cornell would provide a model for other communities in the cold-climate Northeast U.S.. A geothermal Play Fairway Analysis (GPFA) of the sedimentary aquifer geothermal potential of the Appalachian Basin (https://bit.ly/2JHOiVH) for NY, PA, and WV demonstrated that Cornell’s campus is located in a favorable sub-region of the basin. The GPFA found that, for Cornell, the expected rock temperature at 3 km depth was 70–85°C, with suitable natural permeability in sedimentary units most likely near 2.4 km depth; indirect indicators of the propensity for induced seismicity revealed no unusual risk. In 2017-2018 we have used geological and geophysical data sets to more robustly analyze the nature of the geological resources, their potential fit to the heating needs, and risks. Data for deep wells in Tompkins County and six abutting counties, archived in New York’s Empire State Organized Geologic Information System, improve the thermal resource estimate. Within 20 km distance, one well log, assumed equilibrated, lists 118 °C rock at 3369 m depth, and another borehole lists a non-equilibrated 93 °C at 3600 m. These underpin revised Cornell temperature-at-depth estimates. Sedimentary reservoir data for units from the Ordovician Trenton Formation to the top of metamorphic basement are under evaluation based on gamma, neutron, sonic and density logs. The estimated depth to the basement is 2865 +/- 200 m. We are evaluating potential reservoirs within basement rocks for heat extraction using Enhanced Geothermal System techniques. Direct basement data are limited to petrology of cuttings from 5 wells. For pre-drill analysis, those data are supplemented by analogous metamorphic rocks in the Adirondack Mountains; we assume a mixture of granitic gneiss, marble, amphibolite and/or anorthosite. Fracture aperture, spacing, and orientation dispersion are estimated based on observations in the southern Adirondacks, to which fracture analysis based on high resolution DEMs is being added. Seismic, gravity and magnetic field studies of potential well field sites are in progress.