Results for “geothermal network” — Page 9

DOE OSTI2024

Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation

This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to estimate the magnitude-frequency response of stimulation-induced seismicity. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Specimen for evaluating pressure pulse cavitation in rock formations

An apparatus 300 for simulating a pulsed pressure induced cavitation technique (PPCT) from a pressurized working fluid (F) provides laboratory research and development for enhanced geothermal systems (EGS), oil, and gas wells. A pump 304 is configured to deliver a pressurized working fluid (F) to a control valve 306, which produces a pulsed pressure wave in a test chamber 308. The pulsed pressure wave parameters are defined by the pump 304 pressure and control valve 306 cycle rate. When a working fluid (F) and a rock specimen 312 are included in the apparatus, the pulsed pressure wave causes cavitation to occur at the surface of the specimen 312, thus initiating an extensive network of fracturing surfaces and micro fissures, which are examined by researchers.

03 NATURAL GAS↗

DOE OSTI2024

Numerically Testing Conceptual Models of the Utah FORGE Reservoir Using July 2024 Circulation Test Data

Over the past several years, many new data sets have become available regarding the characterization of the Utah FORGE reservoir. These include, but are not limited to, the stimulation of Well 16A, the drilling and completion of Well 16B, and interwell circulation confirmatory testing. As part of the characterization efforts, conceptual models of the reservoir are re-examined as new data become available. As part of the planning for FORGE activities, numerical models are often used to predict the reservoir response to the planned testing. Stochastic methods are often employed to bound uncertainty and allow for evaluation of comprehensive ranges of key reservoir parameters. For the most recent interwell circulation confirmatory testing (July 2023), a priori numerical model predictions did bound the observed behavior (Xinj et al., 2023), but key deviations from expected behavior prompted the FORGE team to reevaluate our conceptual model of the reservoir. In early October 2023, key members of the development, testing, and monitoring teams met for 2 days to review newly collected data and discuss ‘interesting’ or ‘key’ observations. From these discussions, 15 Key Observations were documented, with several significant ones being that the discrete fracture network developed from the 16A stimulation data may not be appropriate and that the early time pressure data obtained during the summer 2023 reservoir testing were best described using radial solutions. In July 2023, two campaigns of interwell confirmatory testing were conducted, the first set of tests occurred on July 4-5 and the second set on July 18-19. The second set of circulation tests conducted at the Utah FORGE site between the injection well 16A(78)-32 and production well 16B(78)-32 on July 18 and 19, 2023 are used to calibrate material properties in a thermal-hydraulic-mechanical (THM) simulation of the discrete fracture network connecting the wells. The spatially and temporally varying reservoir properties are calibrated to match the time dependent pressure and production profiles from the circulation tests. In future work, this calibrated model will be coupled to the native state THM model of the FORGE reservoir to predict surface deformation and strains resulting from pumping schedules.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Numerically Testing Conceptual Models of the Utah FORGE Reservoir Using July 2023 Circulation Test Data

Over the past several years, many new data sets have become available regarding the characterization of the Utah FORGE reservoir. These include, but are not limited to, the stimulation of Well 16A, the drilling and completion of Well 16B, and interwell circulation confirmatory testing. As part of the characterization efforts, conceptual models of the reservoir are re-examined as new data become available. As part of the planning for FORGE activities, numerical models are often used to predict the reservoir response to the planned testing. Stochastic methods are often employed to bound uncertainty and allow for evaluation of comprehensive ranges of key reservoir parameters. For the most recent interwell circulation confirmatory testing (July 2023), a priori numerical model predictions did bound the observed behavior (Xinj et al., 2023), but key deviations from expected behavior prompted the FORGE team to reevaluate our conceptual model of the reservoir. In early October 2023, key members of the development, testing, and monitoring teams met for 2 days to review newly collected data and discuss ‘interesting’ or ‘key’ observations. From these discussions, 15 Key Observations were documented, with several significant ones being that the discrete fracture network developed from the 16A stimulation data may not be appropriate and that the early time pressure data obtained during the summer 2023 reservoir testing were best described using radial solutions. In July 2023, two campaigns of interwell confirmatory testing were conducted, the first set of tests occurred on July 4-5 and the second set on July 18-19. The second set of circulation tests conducted at the Utah FORGE site between the injection well 16A(78)-32 and production well 16B(78)-32 on July 18 and 19, 2023 are used to calibrate material properties in a thermal-hydraulic-mechanical (THM) simulation of the discrete fracture network connecting the wells. The spatially and temporally varying reservoir properties are calibrated to match the time dependent pressure and production profiles from the circulation tests. In future work, this calibrated model will be coupled to the native state THM model of the FORGE reservoir to predict surface deformation and strains resulting from pumping schedules.

58 GEOSCIENCES↗

DOE OSTI2020

The Value of Information From Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones Via Machine Learning

Abstract Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data.

Energy & Fuels↗

DOE OSTI2022

Utah FORGE - Development of a Reservoir Seismic Velocity Model and Seismic Resolution Study

This is data from and a final report on the development of a 3D velocity model for the larger FORGE area and on the seismic resolution in the stimulated fracture volume at the bottom of well 16A-32. The velocity model was developed using RMS velocities of the seismic reflection survey and seismic velocity logs from borehole measurements as an input model. To improve the accuracy of the model in the shallow subsurface, travel times phase arrivals of the direct propagating P-waves were determined from the seismic reflection data, using PhaseNet, a deep-neural-network-based seismic arrival time picking method. The travel times were subsequently inverted using the input velocity model. The seismic resolution study used borehole and surface seismic sensors as well as the seismicity observed during the April 2022 stimulation experiment to estimate the seismic resolution in the activated fracture reservoir. The data contain a 3D P- and S-wave velocity model for the larger FORGE area.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Combined Structural Analysis of Core and Image Log of TGH 76-31 South East of Mt Baker, Washington State

Despite active volcanism, few geothermal energy resources have been developed in the Cascades Range. Temperature Gradient Hole 76-31 was drilled to ~440 m measured depth to probe for zones where fractures provide fluid conduits that transport deep volcanic heat to shallow depths that could support baseload, carbon neutral electrical generation. These zones were predicted by a Play Fairway assessment (PFA) of resource potential along a zone 11 km west-southwest of the summit of Mount Baker Volcano. Rock core, temperature logs, and an acoustic image log were obtained. By comparison to outcrops, the core has been initially interpreted as the Chilliwack group comprised of partially metamorphosed basaltic to andesitic volcanics. Core mapping reveals complex, steeply dipping networks of fractures and brecciation along slickensided strike slip faults; clay alteration is common in many of these structures. The majority of fractures are thoroughly healed by layers of chlorite and calcite; preservation of pyrite indicates anoxic conditions. The majority of fracture porosity resides in very dense fractures a few centimeters or less in length. The image log provides good insights into attitude of fractures that fully transect the core, but generally underestimates fracture density. The combination of complex, non-planar fracture zones containing many short fractures and healing promote misinterpretation of natural fracture attitude and density in the image log. The healing and anoxic minerals are consistent with the conductive temperature gradient measured in the well below a shallow isothermal zone, although, several fractures are open or only partially healed and resulted in fluid entries into the well. Here, the equilibrated measured temperature gradient of 64ºC/km and calculated heat flow of 145 mW/m2 is more than twice the regional average, indicating local influence of the Mt Baker magmatic system at the Little Park Creek TGH site.

Cascades↗

DOE OSTI2023

Multiscale spatial analysis of fracture nodes in two dimensions

Spatial arrangement of fractures as a function of scale is an important component of fracture quantification for inferential and predictive modeling. Available methods that analyze fracture spatial arrangement are based on one-dimensional spacing data; therefore, they are limited to semi-parallel fractures. Such methods cannot be applied to fracture networks in higher dimensions, particularly when fractures have different orientations. Here to characterize fracture arrangements in two dimensions, we propose using Ripley’s K-function, as a method of point pattern analysis, to quantify spatial arrangement of fracture nodes. Fracture nodes, such as barycenters, intersection points, and tips, are point-based representations of fracture locations and connectivity within the fracture network. We introduce formulations for isotropic as well as directional analyses of spatial arrangement. In addition, we derive formulations for edge correction in circular and rectangular study domains. Finally, we demonstrate applications of Ripley’s K-function on two natural fracture datasets. Our proposed method supports quantification and characterization of fracture spatial arrangements that allow practitioners to build representative models of fractures in the subsurface.

02 PETROLEUM↗

DOE OSTI2025

Prediction and Analysis of Utah FORGE Injection Activities using a Coupled Thermo-hydro-mechanical and Earthquake (THM+E) Modeling Workflow

A coupled thermo-hydro-mechanical (THM) numerical workflow that is capable of modeling seismic slip is critical for the successful development of enhanced geothermal systems (EGS). By integrating key physical processes, this workflow enables accurate simulation of temperature and pressure diffusions, stress changes, and induced seismicity. As a result, it serves as a vital tool for predicting induced seismicity and optimizing reservoir stimulation strategies. The Utah FORGE (Frontier Observatory for Research in Geothermal Energy) project, located near Milford, Utah, is a U.S. Department of Energy initiative aimed at advancing EGS technology. In April 2024, eight new stimulation stages (Stages 3R-10) were conducted in well 16A (injection well) subsequent to the first series of stimulation (Stages 1-3) performed in April, 2022. To monitor the induced seismicity, geophones were deployed in wells 58-32, 56-32, and 78B-32, while fiber optic cables were also installed in wells 16B, 78-32, and 78B-32 to collect microseismic data and detect frac hits Preliminary analyses of microseismic catalogs and fiber optic data suggest that the stimulated fractures in Stages 3R–6 closely align with that generated during Stage 3, indicating that the new stimulations were likely reactivating the previously stimulated fracture. To better understand the underlying process, a comprehensive modeling approach that can accurately capture thermal, hydrological, mechanical, and seismic responses is essential. In this work, we propose and utilize a coupled thermo-hydro-mechanical and earthquake (THM+E) simulation workflow to numerically investigate the stimulation activities on well 16A. The specific objective is to confirm whether the new stimulation stages (Stages 3R–6) reactivated fractures previously stimulated during Stage 3. For this purpose, we perform THM+E simulations individually for Stages 3, 3R, 4, and 5, incorporating the discrete fracture networks (DFNs) created by the plane-fitting technique based on the microseismic catalogs. The simulation workflow consists of two separate models: a THM model and an earthquake model, coupled in a one-way manner. Detailed descriptions of the workflow are provided in Section 3. Simulation results are presented in terms of injection pressure, permeability evolution, and predicted seismic catalogs, which are then compared with field data for further analyses. This report is structured as follows. In Section 2, we present detailed analyses of the field data and propose the hypothesis that the new stimulation stages (Stages 3R–6) were probably reactivating the previously stimulated fractures in Stage 3. In Section 3, we introduce the coupled THM+E workflow and the problem setup to validate our hypothesis, followed by the simulation results for each stage in Section 4. Meanwhile, discussions are included to analyze the model predictions and their comparison with field data. Lastly, we conclude the report and outline future plans in Section 5.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Turbomachinery

Supercritical carbon dioxide (sCO 2 ) power cycles show promising potential of higher plant efficiencies and power densities for a wide range of power generation applications such as fossil fuel power plants, nuclear power production, solar power, and geothermal power generation. sCO 2 leakage through the turbomachinery has been one of the main concerns in such applications. To offer a potential solution, we propose an elastohydrodynamic (EHD) seal that can work at elevated pressures and temperatures with low leakage and minimal wear. The EHD seal has a very simple, sleeve-like structure, wrapping on the rotor with minimal initial clearance at micron levels. Here, in this work, a proof-of-concept study for the proposed EHD seal was presented by using the simplified Reynolds equation and Lame’s formula for the fluid flow in the clearance and for seal deformation, respectively. The set of nonlinear equations was solved by using both the conventional Prediction–Correction (PC) method and modern Physics-Informed Neural Network (PINN). It was shown that the physics-informed deep learning method provided good computational efficiency in resolving the steep pressure gradient in the clearance with good accuracy. The results showed that the leakage rates increased quadratically with working pressures and reached a steady-state at high-pressure values of 15~20 MPa, where Q = 300 g/s at 20 MPa for an initial seal clearance of 255 μm. This indicates that the EHD seal could be tailored to become a potential solution to minimize the sCO 2 discharge in power plants.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed

Steam piping networks are essential for optimizing performance in industrial processes and district heating systems. However, dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited. In response, this paper presents a new discretized steam pipe model based on the plug flow approach, capturing key thermo-hydraulic behaviors while simplifying steam phase change processes. Implemented in Modelica, the model accurately calculates temperature and pressure distributions along steam pipelines. To improve computational efficiency for district-scale simulations, five model simplifications are introduced: lumped thermo-hydraulic functions, empirical correlations, fluid state approximations, steady-state dynamics and inclusion of flow derivatives. These simplified models achieve 85%-98% accuracy in predicting pressure drop and condensation losses, including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models. The models support diverse network configurations, scaling effectively to systems with multiple distribution pipes and connected building loads. Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N 2.42 ). In contrast, lumped models simulate 10–28 times faster than discrete, offering quadratic scaling of simulation time O(N 1.73 ). However, they still require 6 times more computation time than a lossless network, highlighting the inherent computational challenges of modeling compressible fluid flow. In conclusion, the steady-state lumped variant, with its near-linear scalability in computational time O(N 1.01 ), emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Utah FORGE: Microseismic Surface Network Catalogs for Wells 16A(78)-32 and 16B(78)-32 Stimulation 2022 and Circulation 2023

This dataset includes microseismic surface network catalogs for Utah FORGE. Data were recorded during the stimulation of well 16A(78)-32 in 2022 and the circulation tests between wells 16A(78)-32 and 16B(78)-32 in 2023. Near-surface seismic monitoring during circulation experiments in wells 16A(78)-32 and 16B(78)-32 revealed fracture growth after shut-in. The catalog provided here offers a comprehensive list of microseismic events, including details on their spatial and temporal distribution, magnitude, and clustering behavior.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Recent seismicity at Ceboruco Volcano (Mexico)

Magma and related hydrothermal fluid movement, and their interaction with solid rock, in active volcanic regions, generate a wide variety of seismic waves whose characterization can mitigate the risk of a potential eruption. Located in the western region of the Trans-Mexican Volcanic Belt, Ceboruco Volcano, whose last eruptive period was 1870–1875, is considered to be one of the most hazardous volcanoes in Mexico. We have conducted a detailed study of the seismicity in the surroundings of Ceboruco's volcanic edifice to assess the current state of this volcano. A dense temporary seismic network with 25 seismic stations in an area of 16 km × 16 km was deployed between November 2016 and July 2017, as part of the P-24 project of the CeMIEGeo consortium; this effort has allowed the detection of 81 earthquakes concentrated beneath the crater with depths between 4 and 8 km. In this study, we observe that the recorded seismicity occurs in swarms, and we specifically identify four sequences that we characterize in detail via the first focal mechanisms available for this volcano. Our results suggest a change in the local seismicity distribution compared to earlier observations, which reported seismic activity near the volcano edifice associated with fluid migration along zones of weakness related to the extensional stresses of the Tepic-Zacoalco rift. The changes in seismic patterns and obtained focal mechanisms are consistent with observed fluid effects at many geothermal sites worldwide, but also could suggest resumption of activity at this currently dormant volcano.

58 GEOSCIENCES↗

DOE OSTI2026

Stochastic Thermo-Hydro Modeling and Neural Network Surrogate Development for Thermal Resource Assessment of the Galleries-to-Calories Geobattery

The Galleries-to-Calories Geobattery concept explores the use of abandoned coal mine workings for large-scale thermal energy transport and storage. The system involves injecting waste heat from a supercomputing facility into flooded mine galleries, where groundwater flow can store and transport thermal energy for potential recovery in downgradient district heating and cooling applications. To evaluate the feasibility and performance of the Geobattery under geological and operational uncertainty, we developed a suite of stochastic thermo-hydrological (TH) simulations using Monte Carlo sampling of key uncertain parameters (e.g., permeability, porosity, thermal conductivity, specific heat capacity) and operating conditions (e.g., injection rate, injection temperature). Results identified injection rate and temperature as the most influential parameters governing thermal front propagation, while the geometry of the room-and-pillar structure played a critical role in directing the extent and orientation of thermal advancement. Optimal combinations of material properties for maximizing heat recovery were also determined. To address the high computational cost of coupled-process stochastic modeling, we trained a neural network surrogate model on 24,000 physics-based realizations, achieving an R² > 0.99 and MAE < 0.1 for temperature predictions at monitoring locations. This surrogate enabled an additional 100,000 realizations for global sensitivity analysis and probabilistic thermal resource assessment. The integrated stochastic physics–surrogate modeling framework offers a computationally efficient tool for quantifying uncertainty, identifying key drivers, and informing early-stage design decisions for Geobattery systems.

15 - GEOTHERMAL ENERGY↗

DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗

DOE OSTI2024

Inaugural Molten Salt Technologies Workshop Powering the Future

The inaugural “Molten Salt Technologies – Powering the Future” Workshop marked a significant convergence of minds from diverse industries, each harnessing molten-salt technologies in innovative ways. Participants from sectors such as solar, geothermal, and advanced nuclear energy, as well as those involved in cutting-edge applications like thermal transport and rare-earth metals extraction, gathered to discuss their shared technical challenges and opportunities. Industry leaders in metal extraction and recycling, alongside experts in high-temperature sensor technology and advanced material manufacturing, also brought their unique perspectives to the table. This workshop served as a crucial platform for these varied industries to delve into the engineering intricacies that molten salt technologies entail. Common challenges such as understanding the thermophysical properties of salts, tackling corrosion mechanisms in harsh environments, and enhancing material resilience under extreme conditions were at the forefront of discussions. These technical sessions highlighted the critical need for cross-industry collaboration to address issues like salt life-cycle process engineering, impurity mitigation, and the development of durable, high-performance materials. By bringing together academia, industry, and representatives from national laboratories and the U.S. Department of Energy (DOE), the workshop facilitated a rich exchange of knowledge and experiences. This interaction not only fostered new partnerships but also strengthened the network among existing collaborators, setting the stage for joint solutions to the complex problems faced by all sectors using molten salt technologies. The event underscored the importance of collaborative efforts in overcoming common engineering challenges and advancing the application of molten-salt technologies across various industries. The workshop not only provided an essential forum for networking and idea exchange but also highlighted the collective drive towards innovative solutions that could benefit multiple fields.

Department of Energy↗

DOE OSTI2024

Inaugural Molten Salt Technologies Workshop Powering the Future

The inaugural “Molten Salt Technologies – Powering the Future” Workshop marked a significant convergence of minds from diverse industries, each harnessing molten-salt technologies in innovative ways. Participants from sectors such as solar, geothermal, and advanced nuclear energy, as well as those involved in cutting-edge applications like thermal transport and rare-earth metals extraction, gathered to discuss their shared technical challenges and opportunities. Industry leaders in metal extraction and recycling, alongside experts in high-temperature sensor technology and advanced material manufacturing, also brought their unique perspectives to the table. This workshop served as a crucial platform for these varied industries to delve into the engineering intricacies that molten salt technologies entail. Common challenges such as understanding the thermophysical properties of salts, tackling corrosion mechanisms in harsh environments, and enhancing material resilience under extreme conditions were at the forefront of discussions. These technical sessions highlighted the critical need for cross-industry collaboration to address issues like salt life-cycle process engineering, impurity mitigation, and the development of durable, high-performance materials. By bringing together academia, industry, and representatives from national laboratories and the U.S. Department of Energy (DOE), the workshop facilitated a rich exchange of knowledge and experiences. This interaction not only fostered new partnerships but also strengthened the network among existing collaborators, setting the stage for joint solutions to the complex problems faced by all sectors using molten salt technologies. The event underscored the importance of collaborative efforts in overcoming common engineering challenges and advancing the application of molten-salt technologies across various industries. The workshop not only provided an essential forum for networking and idea exchange but also highlighted the collective drive towards innovative solutions that could benefit multiple fields.

Department of Energy↗

DOE OSTI2023

EGS Collab Experiment 2: Hydraulic Pressure Test Results

The EGS Collab experiment 2 was focused on testing shear stimulation techniques. Shear stimulation, in this case, means using hydraulic pressure to cause shear slip on preexisting fracture or fault planes such that the hydraulic conductivity of the fracture or fault increases. The concept is to create a percolating network of permeable fractures by enhancing the permeability of a primarily preexisting network of fractures. To test this concept the hydraulic pressures for experiment 2 were kept below the the estimated magnitude of the least compressive principal stress based on a set of stress measurements in nearby well TV4100. All tests for experiment 2 were performed by applying hydraulic pressure to well E2-TC. Subsequent stimulations in both E2-TC and E2-TU where the injection pressure was increased above the least compressive principal stress are considered part of experiment 3, which is documented separately. The data are organized in directories labeled by the depth range isolated between packers.

15 GEOTHERMAL ENERGY↗