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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Performance Evaluation of Engineered Geothermal Systems Using Discrete Fracture Network Simulations

Electrical power production from geothermal energy has a solid record of success for permeable reservoirs such as The Geysers in northern California and geothermal systems in Iceland and New Zealand, among other places. Such permeable reservoirs, however, represent only a small fraction of the available heat energy in the earth’s shallow crust. Most of the available energy resides in rocks with insufficient permeability and storage to produce commercial volumes of heated fluids.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Introduction to this special section: Geothermal energy

Geothermal energy is a global renewable resource that has the potential to provide a significant portion of baseload energy in many regions. In the United States, it has the potential to provide 8.5% of the electric generation capacity by the middle of the century. In general, geothermal systems require heat, permeability, and water to be viable for energy generation. However, with current technologies, only heat is strictly necessary in a native system. Engineered geothermal systems (EGS) introduce water into the subsurface at elevated pressures and reduced temperatures and enhance permeability through hydraulic and/or shear fracturing. Additionally, although moderate- to high-temperature resources currently dominate geothermal energy production, low-temperature resources have been utilized for direct-use cases. When well balanced and maintained, geothermal resources can produce significant amounts of heat and achieve long-term sustainability on the order of an estimated tens to hundreds of years.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Utah FORGE Well 16A(78)-32 Simplified Discrete Fracture Network Data

The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 16A(78)-32. This is a simplified DFN (discrete fracture network) dataset, that was generated using FracMan, for Utah FORGE well 16A(78)-32. A short, well-illustrated, report describing the data is also included in the provided archive file.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO 2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO 2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO 2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO 2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Geochemistry & Geophysics↗
DOE OSTI2025

Newberry SHR Demonstration Project – Bipartisan Infrastructure Law Enhanced Geothermal (EGS) Pilot Demonstration (Abstract)

This project will create an Engineered Geothermal System (EGS) comprising two or more wells drilled to a depth of 4.25 km into superhot rock (SHR) with a temperature of 425 °C at Newberry Volcano in Central Oregon. An EGS is a manufactured heat exchanger in which water is injected in a deep injection well, or injector, to extract heat from the hot rock at depth and steam is returned to the surface in a production well, or producer, to generate electricity. In this project, the SHR EGS will be made using new methods and technologies to stimulate and connect hydraulic and natural fractures to enable multiple flow pathways between wells, allowing for optimal heat mining from the reservoir rock. The new technologies are designed to operate at rock temperatures much higher than those encountered in traditional geothermal. Following EGS completion, water will be injected into the injector well and steam extracted from the producer well in a long-term connectivity flow test demonstrating SHR reservoir evolution with time and use. Success will be measured by demonstrating the efficacy of new technologies and by producing economic quantities of steam (>40 MWth).

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Project Phase 1 Report: Reducing Data Center Peak Cooling Demand and Energy Costs With Cold Underground Thermal Energy Storage (Cold UTES)

Cold Underground Thermal Energy Storage (Cold UTES) is an ultra-long duration grid energy storage technology. With Cold-UTES, low-cost grid power is converted to cold thermal energy and stored in the native subsurface rock at the point of use. Cold UTES is one approach within the general category of engineered geothermal systems. Cold UTES for peak-hour cooling of data centers (DCs) was studied for deployment in Maricopa County, Arizona and Loudoun County, Virgina using thermal storage capacities from 4 GWh-th to over 1,000 GWh-th (>1 Terawatt-hour). The two sites have different power and transmission systems, available grid energy resources, daily and seasonal load profiles, weather conditions, and grid regulatory requirements. The study results indicate high value for both locations and because of this, likely indicates value across most of the US and the world. The basis of the study was a 1,000 MW-e hourly electric use of DC computing and auxiliary loads, which was modeled as 1 GW-th of thermal load to a dry cooled heat rejection system - i.e. a cooling system that does not consume water. The electric power required for cooling the DC varies as the air temperature changes. In cold weather only the dry-coolers are used, with an electrical load for cooling load as low as 10 MW-e. In hot summer hours, chillers and dry- coolers are required, which raises the electrical load for cooling load to as much as 300 MW-e. The continuous and peak cooling electrical loads result in a grid interconnection requirement of no less than 1,300 MW-e. From both a grid and thermal design modeling perspective the 1.3 GW-e could either be a single facility or result from the total load at multiple sites.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models

This dataset includes the data and a report on the large upscaled discrete fracture network modeling done for the Utah FORGE project in 2023. The FORGE modeling team is making five discrete fracture network (DFN) realizations of a large reservoir model available to researchers. These models have been upscaled to a continuum mesh or grid at resolutions of 10 meters and 20 meters providing reservoir properties for fracture porosity, permeability, and compressibility. The models are available in both the reference global coordinate frame and a local coordinate frame aligned with principal stress directions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Geochemical evolution in Cacapon member: Fluid-rock interaction experiments and model insights for Appalachian Basin geothermal development

Here, this study combines recirculated flow-through experimental results conducted for 17 days at 90C and 200 PSI with reactive transport modeling to estimate fluid-rock interactions occurring in a sandy mudstone using an interbedded sandstone-shale sample from the Cacapon Member of the upper Tuscarora Sandstone/lower Rose Hill Formation for the purpose of geothermal exploration. Results suggest that the fluid and rock are likely to be in or near partial-equilibrium after approximately one year. In addition, after >400 h of continuous injection at 0.05 mL/min (5*10 −8 m 3 /min) the reactive front is restricted to the first ∼13% of the 4 cm*1.6 cm experimental rock length, whereas after >9100 h (∼1 year), the reactive front extends to 30% total length. The rate of changes in dissolution or precipitation are however, very minimal, with all major rock-forming minerals having rates <10 −11 mol/L porous media/s. Reservoir rock in the presence of dilute brine as may be the case during operation of an enhanced geothermal system would experience little alteration during the shut-in phase, and possibly up to one year. These results have utility in geothermal exploration for reservoirs at similar temperatures as well as general fluid-sandy mudstone rock interaction geochemistry.

Appalachian Basin↗
DOE OSTI2021

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Unsupervised learning from three-component accelerometer data to monitor the spatiotemporal evolution of meso-scale hydraulic fractures

Enhanced geothermal systems can provide a substantial share of the global energy demand. There exist several hurdles in the engineering implementations of such geothermal systems. One such hurdle is the accurate monitoring of the fracture networks created in subsurface through hydraulic stimulation of these systems. Micro seismicity associated with the stimulation is the primary means to locate the event hypocenters for estimating the stimulated rock volume. Existing methods for location the hypocenters are restricted to only the highest amplitude impulsive signals that are simultaneously detected on several sensors. Consequently, a large portion (usually ~99%) of the measurements are left unused. In this paper, an unsupervised manifold-approximation followed by clustering of 3-component accelerometer data is used to analyze the seismicity recorded on a monitoring well. With this method, a larger portion of the measured signal is used for the monitoring of the hydraulic fracture network. We analyze the EGS Collab experiment 1 microseismic data, recorded at the Sanford Underground Research Facility, South Dakota. Using the data from a single three-component accelerometer, the polarization features viz. Azimuth, incidence, rectilinearity, and planarity are used as inputs for the unsupervised manifold approximation followed by clustering. Our study shows that density-based clusters in the projected 3D space correspond to distinct types of hydraulically fractured zones around the injection point. Finally, we show that the temporal evolution of these clusters can be used to track fracture creation and propagation.

58 GEOSCIENCES↗
DOE OSTI2023

TReactMech v4.217

TReactMech couples geomechanical processes (poroelasticity, failure, and inelastic strain) with multiphase nonisothermal flow (derived from TOUGH2) and reactive geochemical transport. At its core is the reactive-transport code TOUGHREACT v4.13. TReactMech is an efficient hybrid parallel simulator, solving the geomechanics using finite elements and MPI/PetSc, the multiphase flow using integrated finite difference and MPI/PETSc, and the reactive chemistry using OpenMP. The advantages of TReactMech are in its multiphase flow capabilities (e.g., supercritical CO2, supercritical water, air) and parallel geomechanics including full 3-D stress tensor, shear and tensile failure, coupled to porosity and permeability changes. It is backwardly compatible with TOUGH2 and TOUGHREACT v4.13, allowing for easier transitions between the codes. TReactMech can be used to simulate many natural and engineered subsurface systems, including geothermal reservoirs, borehole heat exchangers, geologic carbon sequestration, geologic storage of nuclear waste, groundwater resources, weathering, sediment diagenesis, seafloor hydrothermal circulation, hydrofracturing in unconventional reservoirs, and injection/production-induced surface deformation.

Sonnenthal, Eric↗
DOE OSTI2022

Coupling subsurface and above-surface models for optimizing the design of borefields and district heating and cooling systems in the presence of varying water-table depth. In: Proceedings, 46th Workshop on Geothermal Reservoir Engineering

Dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange. Current modeling approaches in building and district energy simulation tools typically consider heat conduction through the ground between boreholes, without flow of groundwater. On the other hand, detailed simulation tools for subsurface heat and mass transfer exist, but these fall short in simulating above-surface energy systems. To support the design and operation of such systems, we have developed a coupled model including a software package for building and district energy simulation, and software for detailed heat and mass transfer in the ground. For the first, we use the open-source Modelica Buildings Library, which includes dynamic simulation models for building and district energy and control systems. For the heat and mass transfer in the soil, we use the TOUGH simulator. TOUGH can model heat and multi-phase, multi-component mass transport for a variety of fluid systems, as well as chemical reactions, in fractured porous media. In previous work, we described the coupling of these software packages, including how time-dependent boundary conditions for the borehole walls are synchronized for use in Modelica and TOUGH. We verified that the coupled Modelica/TOUGH code produced consistent results with the original Modelica code for an idealized problem in which heat transfer was purely by conduction in a uniform geologic medium. Here, we examine less idealized problems for which TOUGH’s advanced capabilities for modeling fluid flow are required. The first problem has a shallow vadose zone, and the second problem has a thicker vadose zone with a water-table depth that varies in time, which requires a fine vertical grid discretization for the TOUGH model.

Doughtry, Christine↗
DOE OSTI2024

Community Geothermal: Mechanical, Electrical, and Plumbing Design Report and Drawings - Wallingford, CT

Included here are the mechanical, electrical, and plumbing design report and drawings for the proposed community geothermal system at an affordable housing complex in Wallingford, Connecticut. The report and drawings were developed by LN Consulting, in partnership with the University of Connecticut, which completed the energy modeling that formed the basis of the design work. The drawings can be used as a basis for a Request for Proposals to procure entities to complete construction-ready design documents.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting relatively cold water into subsurface fractures, which are in contact with hot dry rock, and brought back to surface through production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. In this short conference paper, we present a reproducible workflow for modeling EGS. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and high-performance computing. This GTC framework is currently being made open-source, user-friendly, and reproducible through python scripts as well as Google Colab/Jupyter Notebooks. This GTC for EGS modeling scripts are made available at https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS and will constantly be updated to cater for geothermal community. Current GTC framework provides scripts to train deep learning (DL) models for techno-economics and data worth analysis. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. This short paper provides details on the scripts to curate, process, and train DL models. The scripts can easily be modified to train on databases generated by other popular open-source simulators such as PFLOTRAN, STOMP, TOUGH, and GEOSX or commercial software such as ResFrac and COMSOL.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Deep Learning for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting cold water into a subsurface fractures, which are in contact with the hot dry rock, and pulled through the production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. To develop economically viable EGS reservoirs, significant technical barriers (e.g., better stimulation technologies without adequate water and/or permeability) and non-technical barriers (e.g., land access and permitting) must be overcome. In this short conference paper, we present a workflow to address a part of this challenge – “How to develop economically viable EGS using existing technologies?”. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and cloud computing. This GTC framework is open-source and available at https://github.com/SmartTensors/GeoThermalCloud.jl. The GTC framework provides trained deep learning (DL) models to estimate the net present value of a given EGS design scenario. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. The database consists of EGS design parameters (inputs to DL model) and their net present value (output of DL model) in uncertain geologic systems. The EGS design parameters for constructing this training database are based on Utah FORGE but include the options of more wells and deeper depths. The DL models are trained by ingesting the EGS design parameters and estimating the corresponding net present value. Such an emulation allows us to screen various EGS designs quickly and identify good development strategies by coupling them with optimization techniques. Our preliminary results show promise in DL emulation of net present value. However, a lot more work is needed to improve the predictive capability of DL models (i.e., extensive hyperparameter tuning is necessary). This will be the primary focus of our future work.

artificial neural networks, geothermal↗