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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Play Fairway analysis of geothermal resources across the State of Hawai‘i: 4. Updates with new groundwater chemistry, subsurface stress analysis, and focused geophysical surveys

This paper is the fourth in a series on a play fairway analysis of geothermal resources across the State of Hawai‘i. Here we describe recent exploration activities that include groundwater sampling in ten locations statewide, as well as geophysical surveys on Lana‘i, across the SW rift of Haleakala Volcano (Maui), and surrounding Mauna Kea Volcano (Hawai‘i Island). We derive a first-order method for computing topographic stress using Green’s functions, finding that topography can induce appreciable crustal stress. We develop a new method for incorporating depth information about resistivity, density, and topographic stresses into our previously published equations to calculate resource probability and confidence. We incorporate newly collected and modeled data into our calculations to update statewide maps of probability and confidence. Lana‘i Island and southeast Mauna Kea are identified as the top targets for exploratory drilling. The east rift of Haleakala Volcano, the southern-most region of Mauna Loa’s SW rift (Hawai‘i Island), and central Kauai are identified as targets for geophysical surveying.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Modeling study of deep direct use geothermal on the West Virginia university campus-morgantown, WV

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use (DDU) system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. Consequently, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided only the single permeability model is needed; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the prediction, therefore, heterogeneity is included in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis will be used for further economic studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Rheology characterization of ionic liquids under high pressure and high temperature

Ionic liquids (ILs) are liquid salts that exist at or below ambient temperatures and are composed of ion pairs. They offer promising alternatives to toxic, hazardous, highly flammable, and volatile solvents in various applications such as solution preparation, dispersion, gel formation, composites, and polymer melts. ILs possess unique and interesting characteristics, including excellent chemical and thermal stability and low vapor pressures. Understanding the rheological properties of ILs is essential to optimizing IL performance. This paper presents a comparative analysis of the rheological properties of two ionic liquids, Nsingle bondHexylpyridinium tetrafluoroborate (HPyBF4) and Nsingle bondHexylpyridinium bromide (HPyBr), under different shear rates, temperatures, and pressures. Rheological measurements were performed under varying controlled pressure and temperature conditions. The experimental investigation covered a pressure range of 689–12,411 kPa [100–1800 psi] and a temperature range from room temperature up to 522 kelvin (K) [480°F]. The primary objective is to explore and compare the flow behavior and viscoelastic characteristics of HPyBF4 and HPyBr under high-pressure and high-temperature conditions. The experimental data showed that HPyBF4 and HPyBr exhibited shear-thinning behavior, and pressure had an insignificant effect on rheology compared to the temperature effect. Under the same testing conditions, HPyBr showed higher shear stress and viscosity than HPyBF4. This research significantly contributes to the improved understanding of the rheological behavior of these specific ionic liquids and their suitability for diverse industrial and scientific applications, particularly in high-pressure and high-temperature environments.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Enhanced Microseismicity During Production Pumping Cessation at the San Emidio Geothermal Field (Nevada, USA) in December 2016

Abstract Tectonic activity, geothermal fluids, and microseismic events (MSEs) tend to occur in similar locations as a result of spatiotemporal changes in the subsurface stress state. To quantify this association, we analyze data from a dense seismic array deployed at the San Emidio geothermal field, Nevada for 1 week in December 2016 to coincide with a 19.45‐hr shutdown of all injection and production pumping operations. 123 MSEs were detected, of which 101 occurred during the shutdown. The spatial association of the MSEs with the production wells suggests a causal relationship between the production cessation and the MSEs. Here we performed a detailed analysis to investigate reservoir material properties, distribution of seismically activated faults, and local stress state. We determined the hypocenters, magnitudes, and focal mechanisms for the MSEs, P‐wave tomographic velocity model, and local stress tensor. The results show that most MSEs occurred near the production wells. Magnitudes fall between −2.2 and 0.0 with larger events located closer to the production wells. Most MSEs occurred within a westward‐dipping normal fault zone in the reservoir associated with anomalously low P‐wave velocity values. The focal mechanism and stress inversion results show predominantly normal faulting with the maximum horizontal stress oriented north‐south. We suggest that the MSEs during shutdown were triggered on pre‐existing, small‐scale, critically stressed fault patches in the reservoir as the pore pressure increased around the production wells when the production pumping ceased. We interpret the larger MSE magnitudes closer to the production wells as a result of higher pore pressure increase.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Fracture length data for geothermal applications

Fracture lengths govern permeability and are unknowns in geothermal assessment. Along their lengths, fracture widths vary due to growth by linkage. Under the influence of diagenesis, narrow widths seal, breaking porosity continuity and reducing open length. The largest range of widths and thus susceptibility to fill occurs where fractures are linked by narrow segments. Outcrops of a geothermal target, Cambrian Potsdam quartz arenite, contain opening-mode fractures having lengths spanning five orders of magnitude from 0.082 mm to 17.9 m. Combined lengths measured at a range of scales can be described by power laws, but at a given image resolution, lengths are best fit by exponential functions. Owing to preferential sealing of small fractures, open fractures follow exponential functions, but values depend on rules for designating fractures as continuous. En échelon segments, offset 10 mm, are connected by narrow fractures or microfractures (hard linked) not evident on outcrop 1 m-elevation LiDAR or 30 m-height drone images. A rule that identifies where narrow and likely connected segments are located can yield lengths meaningful for flow simulation. Depending on diagenesis, continuity rules can halve or double average and maximum length values. Length values from outcrop for geothermal applications should be adjusted based on wellsite-specific diagenesis information.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Environmental Impact Calculations for Geothermal DDU system on WVU Campus

This dataset contains all the inputs used and output produced from Matlab for the environmental analysis of an improved hybrid geothermal district heating and cooling (GDHC) system with a heat pump, without a heat pump, and for a hot water GDHC. Detailed descriptions of the contents of this repository are provided below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

DEEPEN Data Catalog for Magmatic Geothermal Systems in the United States

This data catalog contains information related to the Training Site Analysis for the Geothermica project "DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN)." The DEEPEN project aims to reduce exploration risk for geothermal fluids in magmatic systems by developing improved an improved framework for interpretation of exploration data using the Play Fairway Analysis (PFA) methodology. The Training Site Analysis performed for DEEPEN leverages existing datasets to develop a customized PFA approach to exploration for multiple geothermal resource types in magmatic systems (conventional hydrothermal resources, supercritical fluid and superheated steam resources, and superhot EGS resources). This data catalog contains links to publicly available data files related to 8 training sites in the United States. US training sites are: the Cascades/Aleutians PFA project; the Hawaii PFA project, the Oregon Cascades PFA project, the Snake River Plain, Idaho PFA project, the Washington State PFA project, Newberry Volcano, Coso Geothermal Field, and the Geysers Geothermal field. This database contains an overview of these training sites, data sources, and links to publicly available exploration datasets. For the five PFA projects, details on exploration data related to PFA components (heat, fluid, permeability, sometimes seal) are provided, including a summary of data weighting methodologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Utah FORGE Well 16A(78)-32 Stimulation DFN Fracture Plane Evaluation and Data

This dataset includes files used to fit planar fractures through the preliminary earthquake catalogs of the three stages of the April 2022 well 16A(78)-32 stimulation which is linked bellow. These planar features have been used to update the FORGE reference Discrete Fracture Network (DFN) model. The files are provided to encourage other modelers to use additional workflows to find additional/alternative features. To this end, the dataset includes the cleaned earthquake catalog data translated to the FORGE reference model global reference frame, the well trajectory of 16A(78)-32 in those same coordinates, the fit 15 planar features in csv format, and a pdf file with slides illustrating the process used to fit the features. A recorded presentation of this material is available from the October 2022 FORGE Modeling and Simulation Forum which is also linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Utah FORGE 2439: A Multi-Component Approach to Characterizing In-Situ Stress

Core-based in-situ stress estimation, Triaxial Ultrasonic Velocity (labTUV) data, and Deformation Rate Analysis (DRA) data for Utah FORGE well 16A(78)-32 using triaxial ultrasonic velocity and deformation rate analysis. Report documenting a multi-component approach to characterizing in-situ stress at the U.S. DOE FORGE EGS site: laboratory, modeling and field measurement. Core-based methods for in-situ stress estimation were applied using samples from 5 intervals within the Utah FORGE 16A(78)-32 well. At three of these locations, Triaxial Ultrasonic Velocity (labTUV) tests were performed, resulting in experimentally-determined relationships between wave velocities and stresses. Non-monotonic increase in the velocity-stress relationships are inferred provide evidence of stress history and are therefore used to estimate in-situ stress magnitudes. Additionally, Deformation Rate Analysis (DRA) tests were run on core plugs from various orientations at each of the 5 sampling locations. These, too, provide evidence of stress history based on stress-strain behavior. A novel Weight of Evidence (WoE) method was developed as a means of synthesizing in-situ stress evidence from these two types of tests. Results indicate the minimum horizontal stress gradient ranges from 0.58 psi/ft to 0.69 psi/ft, with 4 of the 5 values between 0.66 psi/ft and 0.69 psi/ft. The vertical stress gradient ranges from 1.05 psi/ft to 1.12 psi/ft, with 4 of the 5 zones given results between 1.09 psi/ft and 1.12 psi/ft. The maximum horizontal stress gradient ranges from 0.98 psi/ft to 1.34 psi/ft, with 4 of the 5 zones falling between 0.98 psi/ft and 1.24 psi/ft. The stress regime thus appears to be on the edge between normal faulting and strike-slip faulting, potentially flipping back and forth between the two regimes due to variability of rock properties, structures such as faults, and/or thermal anomalies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Laboratory Experiments Examining the Effect of Thermal and Mechanical Processes on Hydraulic Transmissivity Evolution

Using laboratory slide-hold-slide experiments, at temperatures from 22 to 200 degrees C, to examine effects of fracture reactivation and quasi-static loading on the evolution of fluid transport properties of simulated fractures in Westerly granite. At all temperatures, the in-plane hydraulic transmissivity consistently decays during hold periods resulting in an overall reduction in transmissivity. During the first three to fifteen hours of an experiment, transmissivity decreases rapidly due to the generation of wear products, development of a sliding surface, and compaction of the resulting gouge. Once the sliding surface has developed, the long-term transmissivity decay rate at 22 and 100 degrees C is significantly lower than the transmissivity decay rate during the initial 3-15 hours of the experiment. However, at 200 degrees C, the decay of hydraulic transmissivity remains high throughout the experiment. The long-term decay of hydraulic transmissivity can be fitted with a power law model with more rapid reduction of hydraulic transmissivity at higher temperature. Periods of sliding on the fracture surface result in transient increases in the transmissivity, due to shear dilation, as is expected for Coulomb materials. These transients are superimposed on the long-term decay. When sliding ceases and a new hold period commences, there is a rapid reduction in transmissivity and return to the long-term rate of transmissivity decay. The rate of decay of the transmissivity transients is inversely correlated with temperature, in contrast to the long-term decay and the expected behavior for processes like subcritical crack growth and indentation creep. The higher decay rates that are observed during the initial 3-15 hours of the tests and following sliding, are associated with times that the porosity of the gouge is expected to be high. The difference in decay rates suggests that when the gouge is driven far from equilibrium by active shearing, densification may be dominated by a different mechanism from long-term compaction.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

EGS Collab Experiment 2: Testbed Well Orientation Data

This dataset contains the north seeking gyro data for each of the 11 boreholes drilled at the Experiment 2 testbed on the 4100 foot level of the SURF (Sanford Underground Research Facility). Each gyro file is in individual folders for each well. A single folder called 'E2 All Well Trajectories' contains the trajectories for all the wells but in the mine coordinate system. An image of the well layout is provided in a PowerPoint.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE 3-2417: Fiber-Optic Geophysical Monitoring of Reservoir Evolution - Workshop Presentation

This is a presentation on the Fiber-Optic Geophysical Monitoring of Reservoir Evolution at the Utah FORGE Milford Site project by Rice University, presented by Prof. Jonathan Ajo-Franklin. The project's objective was to develop an end-to-end fiber-optic sensing approach for EGS to track the initial zone of fracture creation, zones of connected mechanically compliant fractures, and zones of flowing fractures linking the injection/production well pair. This approach would also be used to integrate data into an improved thermo-hydro-mechanical model (THM). This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE 3-2535: Joint EM-Seismic-InSAR Imaging of Fracture Properties - Workshop Presentation

This is a presentation on the Joint Electromagnetic/Seismic/InSAR Imaging of Spatial-Temporal Fracture Growth and Estimation of Physical Fracture Properties During EGS Resource Development project by Lawrence Berkeley National Laboratory, presented by Dr. David Alumbaugh, Staff Scientist. The project's objective was to develop a set of technologies and workflow to image induced fracture generation and growth for an Enhanced Geothermal System (EGS). The project anticipates imaging of the fracture generation and growth at FORGE using a combination of passive seismic, active source borehole EM, and INSAR technology. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗