During 2017-2019, the U.S. Department of Energy funded six geothermal deep direct-use (DDU) projects to investigate feasibility of DDU for heating, cooling and thermal storage in the United States. In a follow-on study conducted at the National Renewable Energy Laboratory (NREL), findings of these six projects were reviewed and analyzed, and additional simulations were conducted using the simulator GEOPHIRES to explore technical performance and cost-competitiveness of DDU. The results of the NREL study were published in the paper "Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States." The GEOPHIRES files developed in that study are included in this submission. The reference for the paper under review is below: Beckers KF, Kolker A, Pauling H, McTigue JD, and Kesseli D (2021) ?Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States?, Submitted to Energy Conversion and Management, Under Review.

This dataset contains all the inputs used and output produced from the modified GEOPHIRES for the economic analysis of base case hybrid GDHC system, improved hybrid GDHC system with heat pump and for hot water GDHC. Software required: Microsoft Notepad, Microsoft Excel and GEOPHIRES modified source code

Paper authored by Stumpf et al. for the 2018 Geothermal Resources Council Annual Meeting held in Reno, NV USA. Included with the paper is the Microsoft PowerPoint presentation made at the GRC meeting and data tables associated with some of the figures.

Paper authored by Stumpf et al. for the 2018 Geothermal Resources Council Annual Meeting held in Reno, NV USA. Included with the paper is the Microsoft PowerPoint presentation made at the GRC meeting and data tables associated with some of the figures.

This dataset contains input data, code, ReadMe files, output data, and figures that summarize the results of a stochastic analysis of geothermal reservoir production from two potential geothermal reservoirs that were evaluated for the Cornell University Deep Direct-Use project. These potential reservoirs are the Trenton-Black River (TBR) from 2.27-2.3 km depth, and basement rocks from 3.0-3.5 km depth and 3.5-4.0 km depth. Several utilization scenarios consisting of different injection fluid temperatures and flow rates were evaluated for each reservoir. Uncertainty in geologic properties, thermal properties, economic costs, and utilization efficiencies were evaluated using a Monte Carlo analysis of the reservoir simulations. Some reservoir simulations of the TBR were completed using the TOUGH2 software, as implemented in PetraSIM. The PetraSIM run files and associated data are provided with this submission. All other reservoir simulations were completed using the GEOPHIRES software, with some modifications to complete the uncertainty analyses. ReadMe files that describe additions to GEOPHIRES, the GEOPHIRES input data, and the output data are all provided, and references are provided to the code repository. Figures that summarize the reservoir heat production, temperature drawdown, and the probability of meeting targeted building heating demands with the produced heat and fluid temperatures are provided.

This dataset contains input data, code, ReadMe files, output data, and figures that summarize the results of a stochastic analysis of geothermal reservoir production from two potential geothermal reservoirs that were evaluated for the Cornell University Deep Direct-Use project. These potential reservoirs are the Trenton-Black River (TBR) from 2.27-2.3 km depth, and basement rocks from 3.0-3.5 km depth and 3.5-4.0 km depth. Several utilization scenarios consisting of different injection fluid temperatures and flow rates were evaluated for each reservoir. Uncertainty in geologic properties, thermal properties, economic costs, and utilization efficiencies were evaluated using a Monte Carlo analysis of the reservoir simulations. Some reservoir simulations of the TBR were completed using the TOUGH2 software, as implemented in PetraSIM. The PetraSIM run files and associated data are provided with this submission. All other reservoir simulations were completed using the GEOPHIRES software, with some modifications to complete the uncertainty analyses. ReadMe files that describe additions to GEOPHIRES, the GEOPHIRES input data, and the output data are all provided, and references are provided to the code repository. Figures that summarize the reservoir heat production, temperature drawdown, and the probability of meeting targeted building heating demands with the produced heat and fluid temperatures are provided.

The purpose of this document is to describe the contents contained within Geothermal Data Repository (GDR) node of the National Geothermal Data System (NGDS) that serves as the final report for the project "Earth Source Heat: A Cascaded Systems Approach to DDU of Geothermal Energy on the Cornell Campus". Abstract: Cornell completed a comprehensive evaluation of the potential for Earth Source Heat (ESH), Cornell's specific application of Deep Direct Use (DDU) geothermal energy, to create viable heat energy for its Ithaca, NY campus district heating system. The study included assessment of the natural rock properties within and surrounding two potential reservoirs, coupled to the assessment of the thermal energy needs for a district heating system capable of supplying 20% of Cornell's building heating load. The feasibility and benefits of such a district heating system at the specific location of Cornell University's Ithaca, NY campus are evaluated from the perspectives of economic cost, environmental benefits, and economic benefits in the region external to Cornell University. The economic cost is expressed as the Levelized Cost of Heat, and comparison to the existing inexpensive fossil fuel system. The submission includes descriptions of the assumptions, analyses, data, and models that were combined to reach conclusions regarding the feasibility of a Cornell Campus project. A shortened, descriptive title for the project is "Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy."

This dataset presents the results of techno-economic simulations performed using the Distributed Geothermal Market Demand Model (dGeo) to evaluate the feasibility of Enhanced Geothermal Systems (EGS)-based district heating in the Northeastern United States. Developed by the National Renewable Energy Laboratory (NREL), dGeo is a geospatially resolved, bottom-up modeling framework designed to explore the deployment potential of geothermal distributed energy resources. The dataset, created as part of the Cornell EGS Ground-Truthing Project, provides census tract-level data that includes inputs and outputs such as thermal demand, road length, energy prices, geothermal system sizing, annual energy contributions from geothermal and natural gas peaking boilers, system capital costs (CAPEX), operation and maintenance costs (OPEX), and the levelized cost of heat (LCOH). Key simulation parameters include geothermal gradients, measured well depths, production temperatures, and district heating piping lengths based on S1400 neighborhood road lengths. The simulations assume a target bottom hole temperature of 80C and the development of new district heating networks in each census tract.

This data release documents sixteen 5-year simulations using the USGS SUTRA groundwater flow modeling software and includes the full output from one 5-year simulation for verification that the model code runs properly. The most recent version of SUTRA (version 4.0) was used to evaluate aquifer (ATES) and reservoir (RTES) thermal energy storage performance to support economic analyses by simulating three-dimensional groundwater and heat transport for layered systems in the following eight metropolitan area cities: Albuquerque, New Mexico; Charleston, South Carolina; Chicago and Decatur, Illinois; Lansing, Michigan; Memphis, Tennessee; Phoenix, Arizona; and Portland, Oregon. ATES entails storing hot or cold water directly within aquifers that are often near the surface, contain high-quality groundwater, and are typically relied upon for groundwater production. RTES is a variant of ATES that targets deeper aquifers that are poorly connected with shallower fresh aquifers and surface water bodies, resulting in lower ambient groundwater flow rates and more geochemically evolved waters that tend to be used less for groundwater production. The provided ATES and RTES models permit comparison of the performance of both storage techniques. Estimated subsurface conditions beneath airports within each city are represented to serve as demonstrative conditions for the general area; airports were chosen because of their resource development advantages, including available development space, regular and long-term space cooling demand, and availability of hydrogeologic data that is critical to accurate modeling . Climate-based supply and demand inputs, derived from National Renewable Energy Laboratory ComStock models (Parker and others, 2023), are representative of the cooling load of seven medium-sized office buildings in each city. These hourly, year-long demand profiles are used to construct the simulated energy storage and production schedule. Although city airport locations were used to derive representative hydrothermal model parameters, the airport cooling energy demand was not considered in the ComStock models. The version of SUTRA utilized in these models is summarized in Voss and others, (2024). Details on the construction of similar thermal energy storage models is provided in Burns and others (2020) and Pepin and others (2025). The SUTRA (version 4.0) executable, python scripts to administer simulations, and example model inputs and outputs are provided in this data release. Further detail on each folder’s contents, including input and output files, and running a model can be found in “readme.txt” files located in the respective folders. Burns, E.R., Bershaw, J., Williams, C.F., Wells, R., Uddenberg, M., Scanlon, D., Cladouhos, T., van Houten, B., 2020, Using saline or brackish aquifers as reservoirs for thermal energy storage, with example calculations for direct-use heating in the Portland Basin, Oregon, USA: Geothermics, v. 88, p. 101877, https://doi.org/10.1016/j.geothermics.2020.101877. Parker, A., Horsey, H., Dahlhausen, M., Praprost, M., CaraDonna, C., LeBar, A., amd Klun, L., 2023, ComStock Reference Documentation. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5500-83819. https://www.nrel.gov/docs/fy23osti/83819.pdf. Pepin, J.D., Burns, E.R., Cahalan, R.C., Hayba, D.O., Dickinson, J.E., Duncan, L.L., and Kuniansky, E.L., 2025, Reservoir Thermal Energy Storage Pre-Assessment for the United States: Geothermics, v. 129, no. 103256, 18 p., https://doi.org/10.1016/j.geothermics.2025.103256. Voss, C.I., Provost, A.M., McKenzie, J.M., and Kurylyk, B.L., 2024, SUTRA—A code for simulation of saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of the version 4.0 enhancements—Freeze-thaw capability, saturation and relative-permeability relations, spatially varying properties, and enhanced budget and velocity outputs: U.S. Geological Survey Techniques and Methods, book 6,

Preliminary geothermal reservoir simulations were performed using a homogeneous static model to evaluate and understand the effects of fluid and rock properties that could influence the delivery of thermal energy in a doublet system. A 5000 feet by 5100 feet by 500 feet homogeneous model having a constant porosity and permeability of 20% and 100 mD was used to perform preliminary geothermal reservoir simulations. The model was discretized in the x-, y-, and z-directions into 100, 101, and 100, gridblocks. Two wells were placed on the opposite ends of the central column of the discretized model. One of the wells was designated as a producer and the other an injector. Equal volumes of water was extracted and then injected into the reservoir via the production and injection wells. Water was extracted at a temperature of 109 deg F and re-injected at 50 deg F at the 1000 bbl/day. The files attached contains the input and output files of this simulation case. The input and some of the output files can be viewed in any text editor.