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At least 145 records · Page 8

Efficient Optimization of Energy Recovery From Geothermal Reservoirs With Recurrent Neural Network Predictive Models

Improving the long-term energy production performance of geothermal reservoirs can be accomplished by optimizing field development and management plans. Reliable prediction models, however, are needed to evaluate and optimize the performance of the underlying reservoirs under various operation and development strategies. In traditional frameworks, physics-based simulation models are used to predict the energy production performance of geothermal reservoirs. However, detailed simulation models are not trivial to construct, require a reliable description of the reservoir conditions and properties, and entail high computational complexity. Data-driven predictive models can offer an efficient alternative for use in optimization workflows. This paper presents an optimization framework for net power generation in geothermal reservoirs using a variant of the recurrent neural network (RNN) as a data-driven predictive model. The RNN architecture is developed and trained to replace the simulation model for computationally efficient prediction of the objective function and its gradients with respect to the well control variables. The net power generation performance of the field is optimized by automatically adjusting the mass flow rate of production and injection wells over 12 years, using a gradient-based local search algorithm. Two field-scale examples are presented to investigate the performance of the developed data-driven prediction and optimization framework. Furthermore, the prediction and optimization results from the RNN model are evaluated through comparison with the results obtained by using a numerical simulation model of a real geothermal reservoir.

15 GEOTHERMAL ENERGY↗

WHOLESCALE Catalog of Rock Samples at San Emidio Nevada collected in January 2021

This submission contains information on thirty-six rock samples collected from San Emidio, Nevada during January, 2021 for Subtask 2.3 of the WHOLESCALE project. The following resources include a .zip of rock sample photos taken in the field, a .zip of rock sample photos taken in the laboratory at UW-Madison, and an excel catalog of rock samples with information on sample name, rock type, coordinates of sample location, structural measurements, field notes, observations for plug preparation (e.g., weathering, ability to be cut and cored), and rock descriptions. It should be noted that not every sample was photographed in the field. Names and descriptions of rock formation units are taken from Rhodes et al. (2011). The README.txt file is a description of this submission.

15 GEOTHERMAL ENERGY↗

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives.

access↗

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives. This paper will outline the development, integration, output, and efficacy of the AskGDR LLM, including adherence to scientific rigor through improvements designed to increase the accuracy of generated answers, avoid speculation, and provide proper references for all resources used.

access↗

Monitoring spatiotemporal evolution of fractures during hydraulic stimulations at the first EGS collab testbed using anisotropic elastic-waveform inversion

The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. Here we use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.

15 GEOTHERMAL ENERGY↗

Results of Multiple Tracer Injections into Fractures in the EGS Collab Testbed-1

The EGS Collab project constructed an intermediate scale (~10-20 m) testbed at the 4850 level of the Stanford Underground Research Facility (SURF) in South Dakota for testing and validating fracture stimulation and flow/transport models. This testbed consists of eight ~200 ft (~60 m) HQ-diameter (9.6 cm) boreholes that are drilled into the crystalline rocks of the Poorman Formation from the West Access Drift tunnel. Of the eight boreholes, one borehole is used as an injection/stimulation well, while another sub-parallel borehole located about 10 m away from the injection well is used as a production well, and rest of the other boreholes are used as geophysical/fluid sampling monitoring wells. Hydraulic stimulation activities were conducted at three locations along the injection hole in an attempt to create direct fracture connections to the production hole. A flow system has been established between injection and production boreholes through a set of hydraulically stimulated fractures propagated from a notch located at 164 ft in the injection hole. Although we planned for a single production well, a set of natural fractures in the testbed is believed to have intersected the stimulated hydraulic fractures and provided additional flow paths for water to be transported to the drift through multiple monitoring boreholes and weep zones. As the flow tests continued after stimulation activities, single or a combination of two or three producers become dominant producers at different times, mostly as a response to intersection of hydraulic fracture and testbed wells, activation of natural fractures and making new leak points to the monitoring wells, resealing of leaky wells, and so on. Since late October 2018, multiple tracers were injected into the fracture system at the 164 ft location that involves both stimulated and natural fractures, and tracers were recovered from multiple locations in nearby wells and weep. The cumulative water recoveries over the time have ranged from 50 to 90%; however, the injected tracer recoveries from various production sources are much less (ranging from a few percentages to 38%). Changes in water/tracer recoveries, shifting of major producing wells from one well to the others, and other observations (e.g., microseismic, electric resistivity tomography, etc.) indicate a testbed that has undergone several changes since October 2018. In this paper, we present tracer recovery data accumulated during several tracer campaigns, provide conceptual testbed flow pathways, and provide observations that suggest a evolutionary nature of fracture volume and fracture geometry in the testbed.

15 GEOTHERMAL ENERGY↗

Coalition for Community-Supported Affordable Geothermal Energy Systems (C2SAGES)

The C2SAGES project evaluated the feasibility of a community geothermal system for the planned Windy Ridge affordable housing development in Hinesburg, Vermont. Led by GTI Energy with Vermont Gas Systems, LN Consulting, NREL, and Frontier Energy, the work assessed technical design, energy performance, costs, business models, community engagement, maintenance, workforce development, and permitting. The proposed system was designed to serve 100% of the development’s heating, cooling, and domestic hot water loads. Compared with a baseline using air-source heat pumps and natural gas water heating, the geothermal system was estimated to reduce HVAC and domestic hot water energy use by about 45% to 48%, lower operating and maintenance costs, and reduce 30-year life-cycle costs by 37% for Phase 1 and 10% for Phase 2. Technical testing and modeling indicated that the Windy Ridge site is suitable for a community-scale geothermal system. The project also developed borehole field layouts, piping concepts, pump house designs, controls, maintenance plans, and supporting engineering drawings. The business model analysis found that first cost, ownership structure, and customer affordability remain major deployment challenges. Utility-led maintenance and operation were viewed favorably, but traditional utility cost-recovery models may require subsidy or revised financing structures to be practical for affordable housing. Community engagement highlighted the need for clear public education, transparent financing, reliable long-term maintenance, trained technicians, and the potential to pair geothermal systems with weatherization. Overall, the report concludes that community geothermal is technically feasible and offers meaningful energy, emissions, and life-cycle cost benefits, but broader deployment will depend on workable financing models and workforce readiness.

15 GEOTHERMAL ENERGY↗

Hydraulic fracturing experiments at 1500 m depth in a deep mine: Highlights from the kISMET project

In support of the U.S. DOE SubTER Crosscut initiative, we established a field test facility in a deep mine and designed and carried out in situ hydraulic fracturing experiments relevant to enhanced geothermal systems (EGS) in crystalline rock to characterize the stress field, understand the effects of rock fabric on fracturing, and gain experience in monitoring using geophysical methods. The project also included pre- and post-fracturing simulation and analysis, and laboratory measurements and experiments. The kISMET (permeability (k) and Induced Seismicity Management for Energy Technologies) site was established in the West Access Drift of the Sanford Underground Research Facility (SURF) 4757 ft (1450 m) below ground (on the 4850 ft level (4850L)) in phyllite of the Precambrian Poorman Formation. We drilled and continuously cored five near-vertical boreholes in a line on 3 m (10 ft) spacing, deviating the two outermost boreholes slightly to create a five-spot pattern around the test borehole centered in the test volume 40 m below the drift invert (floor) at a total depth of ~1490 m (4890 ft). Laboratory measurements of core from the center test borehole showed P-wave velocity heterogeneity along each core indicating strong, fine-scale (~1 cm or smaller) changes in the mechanical properties of the rock. Field measurements of the stress field by hydraulic fracturing showed that the minimum horizontal stress at the kISMET site averages 21.7 MPa (3146 psi) trending approximately N-S (356 degrees azimuth) and plunging slightly NNW at 12°. The vertical and horizontal maximum stresses are similar in magnitude at 42-44 MPa (6090-6380 psi) for the depths of testing, which averaged approximately 1530 m (5030 ft). Hydraulic fractures were remarkably uniform suggesting core-scale and larger rock fabric did not play a role in controlling fracture orientation. Analytical solutions suggest that the fracture radius of the large fracture (stimulation test) was more than 6 m (20 ft), depending on the unknown amount of leak-off.

Oldenburg, C↗

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗

Geothermal Fault Zone and Fluid Imaging through Joint Airborne ZTEM and Ground MT Data Inversion Analysis

This project has aimed to achieve detailed electrical resistivity resolution at geothermal reservoir scales by combining airborne natural electromagnetic (EM) field surveying (ZTEM) with ground magnetotelluric (MT) measurements to approximate an airborne MT geophysical method. MT alone is relatively expensive and may have permitting challenges in sensitive areas. Airborne ZTEM field data contains only the magnetic field, requires a background assumption, and has been limited to relatively high frequencies, thus suffering uniqueness problems. Based on proto-type 2D simulations, ZTEM ambiguities may be reduced through formal incorporation with possibly sparse ground MT soundings, which we pursued in full 3D for this project. The methodology was tested at the high-temperature Roosevelt Hot Springs geothermal system, Utah, which was considered advantageous given the near total exposure of crystalline reservoir rocks across the project area. ZTEM and ground MT survey data were acquired in 2017, subcontracted to outside parties with which we have worked in the past. These included 80 remote-referenced tensor MT soundings over the Mineral Mountains and adjacent Roosevelt Hot Spring producing geothermal system. These MT stations abut later coverage of a similar number of MT stations taken for the Utah FORGE project providing excellent total data aperture to re-solve structure beneath both project areas better than either set alone. The airborne ZTEM survey covered 704 line kilometers in E-W flight lines with a 250 m line spacing. Although this survey was timed during a maintenance-related shutdown of power production at the Roosevelt Hot Springs, other noise sources difficult to identify but including two high-voltage state-scale transmission lines compromised the ZTEM survey badly leading to unusable responses. Thus, with DOE management concurrence, the project proceeded to emphasize inversion and interpretation of the joint SubTER-FORGE MT data sets with regard to the Roosevelt Hot Springs reservoir recharge and to deep heat sources for both it and the Utah FORGE EGS project area. We also investigated the joint ZTEM-MT sampling concept with data sets from the Eleven Mile Canyon prospect area donated by the U.S. Navy (A. Sabin, PoC). Inversion of the SubTER-FORGE MT data using the HexMT 3D finite element algorithm reveals a large, low-resistivity anomaly extending sub-vertically through the depth range of the crust beneath the western Mineral Mountains. The steep conductive zone connects in the lower crust to a more tabular conductor characteristic of much of the Great Basin that generally is ascribed to current mafic magmatic underplating, hybridization and fluid release. The location of the resolved anomaly relative to the recent (0.5-0.8 Ma) eruptive centers of the Mineral Mountains implicates it as remnants of the magma body which fed these centers. This structure appears to be currently feeding heat and fluids upward into the Roosevelt Hot Springs hydrothermal system, as well as heat laterally to the FORGE project area. Separate and joint inversion models were carried out for the donated Eleven Mile Canyon MT-ZTEM data set to demonstrate concept. ZTEM only inversion showed two main alteration zones in the western portion of the project area known from geological mapping. Joint inversion including an E-W profile of MT soundings sharpened these features considerably. It also resolved in much greater detail the graben related normal faulting structure of the central project area which lies at depths exceeding the sensitivity of ZTEM alone. The sparse number of MT da-ta relative to the ZTEM required upweighting the former by a factor of several, but an exact procedure awaits future research. Our final impression is that sparse MT data can improve resolution of the subsurface over that of ZTEM alone. However, well sampled MT data are to be preferred and offer the simplicity of interpreting just one data type, and possess the superior resolution capability coming with the electric field everywhere, and from their high bandwidth.

15 GEOTHERMAL ENERGY↗

Experimental study correlating damage and permeability in concrete using confined, flattened Brazilian disks

This research experimentally studies coupled hydro-mechanical relationships using flattened Brazilian disks under confinement, with concurrent permeability measurements and acoustic emission monitoring. A series of tests are performed on concrete, as representative samples of brittle geo-materials, under a range of confining stresses between 2.76 and 13.79 MPa. Acoustic emission data is used to quantify damage and identify damage thresholds. Damage is then correlated to pre-peak changes in permeability. The advancement of models that couple transport properties to mechanical responses are of interest in the fields of carbon sequestration, hydrofracking, production of geothermal energy, induced seismicity, and underground nuclear waste storage, and these experiments validate a novel experimental approach towards investigating hydro-mechanical relationships.

Boyce, S.↗

Seismic identification of geothermal prospecting in Harrat Rahat, Northern Arabian Shield

Harrat Rahat volcanic field lies along the Makkah-Madinah-Nafud active volcanic line. It has extensive linear weak zones of wrench faults that could provide ideal conduits for upwelling magma and hydrothermal fluids as well. Here, passive seismic data recorded by seismic stations installed within and around Harrat Rahat has been processed for the detection of microseismic events. Moreover, ambient noise was also gathered and analyzed to construct a subsurface velocity model. This model indicates low velocity zone in Harrat Rahat, and two areas with coincident basement faults and possibly with enhanced subsurface temperatures were identified. It is concluded that Harrat Rahat has high geothermal potential and promising for geothermal renewable power.

58 GEOSCIENCES↗

A microfluidic study of transient flow states in permeable media using fluorescent particle image velocimetry

Velocity fields in flow in permeable media are of great importance to many subsurface processes such as geologic storage of CO 2 , oil and gas extraction, and geothermal systems. Steady-state flow is characterized by velocity fields that do not change significantly over time. The flow field transitions to a new steady state once it experiences a disturbance such as a change in flow rate or in pressure gradient. This transition is often assumed to be instantaneous, which justifies the expression of constitutive relations as functions of instantaneous phase saturations. This work examines the evolution of velocity fields in a surrogate quasi-2D permeable medium using a microfluidic device, a microscopy system, and a high-speed camera. Tracer particles are injected into the medium along with DI water. The evolution of the velocity field is examined by tracing these particles in the captured images using the standard high-density particle image velocimetry algorithm founded on cross-correlation. The results suggest that the transition between steady states for an incompressible fluid takes a finite and non-negligible amount of time that is independent of the magnitude of the change in pressure gradient. The existence of transient states and the nature of the response during these states are readily interpreted by the principle of least action where flow gradually establishes an optimal configuration such that energy dissipation is 2 minimized. The findings provide evidence against the applicability of the assumption that flowing phases relax instantaneously to their steady states and, hence, against the accuracy of the classical multiphase extension of Darcy's law.

42 ENGINEERING↗

Utah FORGE: Phase 3 Microgravity Data

A 4D microgravity survey began in early 2019 in order to benchmark surface and subsurface elevations prior to Phase 3 drilling and stimulation campaigns. Microgravity stations are collocated with GPS deformation benchmarks and use the same location survey data. Surveys are undertaken on a quarterly basis. The focus of current activities is comparative analysis of GPS survey results and InSAR analysis (see GDR Submission 1251) to check seasonal variance and to evaluate possible changes in the groundwater elevation. The attached zip file contains a text file and an Excel spreadsheet containing Utah FORGE Phase 3 processed "final" microgravity data through time. The data are identical in both files. The spreadsheet is only supplied for convenience. There is also a ReadMe.txt file that describes the field headings.

15 GEOTHERMAL ENERGY↗

EGS Collab: Modeling and Simulation Working Group Teleconference Series (129-130)

This submission contains the presentation slides and recordings from EGS Collab Modeling and Simulation Working Group (MSWG) teleconferences number 129 through 130. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.

15 GEOTHERMAL ENERGY↗

Fracture Caging to Limit Induced Seismicity

Geothermal resources offer a stable low-carbon energy source. However, geothermal sites can collocate with the hypocenters of large-magnitude seismic events. Large seismic events pose a risk to public safety and are therefore a liability for efforts to develop geothermal resources. In this work, we propose “fracture caging” to limit induced seismic event magnitudes and present evidence from numerical model predictions, laboratory experiments, and field observations. Fracture caging involves drilling tactical production wells around a geothermal injection zone to contain fluids in fracture-dominated flow systems. Prior to our work, the effect of small wells on the growth of large fractures and on flow through fractures was subject to debate. Our work shows that production wells can impede fracture growth and contain high-pressure fluids in fracture-dominated rocks. This containment offers a mechanism to limit induced seismicity.

58 GEOSCIENCES↗

Field Experiments and Model Validation: The EGS Collab Project

Implementing enhanced geothermal systems (EGS) will require improvements in understanding stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The US Department of Energy (DOE) is addressing these and other challenges at multiple scales. The EGS Collab project, addressed here, is performing tests and modeling at the 10 m scale. The FORGE project is performing tests at the full reservoir scale. The EGS Collab team created an underground testbed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km to examine hydraulic fracturing (Experiment 1). We are currently building a second testbed at SURF at a depth of about 1.25 km aimed at investigating shear stimulation (Experiment 2). In Experiment 1, we drilled eight boreholes in a well-characterized phyllite and installed geophysical sensors in six of them to create a well-instrumented testbed to allow careful monitoring of stimulation events and flow tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results both in near-real-time and in detailed process studies. Stimulations performed in this testbed allowed quantification of processes occurring during stimulation and the examination of dynamic flow occurrences. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Our second testbed, targeted at shear stimulation, is currently being built at the SURF Facility at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1.

artificial intelligence↗

Field Test and Thermal Performance Comparison of a Novel Underground Thermal Battery with a Single U-tube Borehole Heat Exchanger for Geothermal Heat Pump Application

Geothermal Heat Pumps (GHP) are a proven technology to electrify and decarbonize space heating and cooling by utilizing the thermally consistent underground as a heat sink or source. However, the high drilling costs of conventional borehole heat exchangers (BHEs) associated with GHP systems and subsurface heterogeneity have been two major challenges to their widespread adoption. In this paper, we present results from a field-scale pilot of an Underground Thermal Battery (UTB) as a potential alternative to a conventional BHE. To benchmark the UTB performance, a side-by-side field test was conducted to compare the UTB with a conventional BHE installed at the same site. The UTB was installed in a 7.01-meter-deep borehole having a diameter of 0.9 meter, whereas the conventional BHE (with single U-tube loop) is in a borehole with 36.6-meter depth and 0.15-meter diameter. Both are integrated into a GHP system to meet the thermal energy demands of office space at the Illinois Energy Farm on the University of Illinois Urbana-Champaign campus. The system’s performance is compared in terms of heat transfer rate, outlet temperatures, and contributions to the overall thermal power of the GHP system under varying thermal demands and in different heat pump operation modes: heating only, cooling only, and hybrid (alternative, diurnal heating and cooling). Results to date indicate that the UTB delivers a more consistent response under during a time period of varying thermal demands. Its operational performance is better during short-term with higher thermal demands, while the conventional BHE performs better under continuous, long-term thermal demands.

Pokhrel, Sajjan [ORNL] (ORCID:0000000228523749)↗