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This document highlights the applications of low-temperature geothermal resources and the potential for future uses as well as current Geothermal Technologies Program-funded projects related to low-temperature resources.
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.
P and S-wave datasets and associated report studying the ability to use three-component long offset surface seismic surveys to find large aperture fractures in geothermal resources at the San Emidio geothermal resource area in Washoe County, Nevada.
The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. The assessment protocols used in GeoRePORT allow for comparison of project attributes across locations and geological settings to understand the feasibility of geothermal development. This work introduces the Resource Size Tool, a new feature within the GeoRePORT package that compiles two independent methods for estimating geothermal resource size in terms of energy capacity in MW. Energy production potential for twenty-three case studies was estimated with the Resource Size Tool in order to 1) generate a reasonable range of resource size estimates for a particular geothermal field; 2) illustrate the advantages and limitations of each methodology (such as data input requirements, estimate accuracy and precision, and the appropriate circumstances of use); and 3) test the ability of the resource size tool to provide useful and accurate information for geothermal stakeholders. The tool employs two methods widely used in the geothermal industry: (1) USGS Volumetric and (2) Power Density. Results from our case studies show general overlap between these two methods in terms of resource size estimates; however, they also reveal key differences between the two approaches that should be considered when using such estimates to drive development. First, the two methods rely on different input parameters and therefore one method may be more appropriate and/or accurate for a given project than the other. Second, the Power Density method was found to generate wider ranges of resource size predictions, more consistently aligning with actual power production of the field but with larger scales of error; whereas the USGS Volumetric method predicts narrower ranges but tends to overestimate when compared to current MW production. Future work will refine variables used in the methods with input data from other sections of GeoRePORT and modify uncertainty levels based on the particular datasets used for a given project.
The Preston Geothermal prospect is located in northern Cache Valley approximately 8 kilometers north of the city of Preston, in southeast Idaho. The Cache Valley is a structural graben of the northern portion of the Basin and Range Province, just south of the border with the Eastern Snake River Plain (ESRP). This is a known geothermal resource area (KGRA) that was evaluated in the 1970's by the State of Idaho Department of Water Resources (IDWR) and by exploratory wells drilled by Sunedco Energy Development. The resource is poorly defined but current interpretations suggest that it is associated with the Cache Valley structural graben. Thermal waters moving upward along steeply dipping northwest trending basin and range faults emanate in numerous hot springs in the area. Springs reach temperatures as hot as 84° C. Traditional geothermometry models estimated reservoir temperatures of approximately 125° C in the 1970’s study. In January of 2014, interest was renewed in the areas when a water well drilled to 79 m (260 ft) yielded a bottom hole temperature of 104° C (217° F). The well was sampled in June of 2014 to investigate the chemical composition of the water for modeling geothermometry reservoir temperature. Traditional magnesium corrected Na-K-Ca geothermometry estimates this new well to be tapping water from a thermal reservoir of 227° C (440° F). Even without the application of improved predictive methods, the results indicate much higher temperatures present at much shallower depths than previously thought. This new data provides strong support for further investigation and sampling of wells and springs in the Northern Cache Valley, proposed for the summer of 2015. The results of the water will be analyzed utilizing a new multicomponent equilibrium geothermometry (MEG) tool called Reservoir Temperature Estimate (RTEst) to obtain an improved estimate of the reservoir temperature. The new data suggest that other KGRAs and overlooked areas may need to be investigated using improved geothermal exploration methods.
Geothermal energy resources are present in the Hot Springs Bay Valley geothermal area, and the City has invested considerable money and time in the exploration of the resource for future use. Exploration activities funded primarily through a grant the City was awarded from Alaska Energy Authority and later supplemented by the U. S. Department of Energy (DOE), with cost share from the City. The exploration led to the development of a geothermal resource conceptual model that was built and refined between 2008 and 2018. During this period, geological and geophysical studies, exploration drilling and other activities were completed, including a preliminary feasibility study for a power project, investigations of the terrain for suitability for access and development, environmental and archeological review, plus TG well abandonment and a review of economic considerations. The model of the resource predicts that there is a viable geothermal resource in two general areas: on the northwest edge of Hot Springs Bay Valley going northwest of the valley (hot springs area) and southwest of the Valley (fumarole area). The resource temperature for the valley area is expected to be about 170°C (340°F) and given the size and permeability estimates (with a factor for the level of confidence in the estimates), the mean power capacity of the outflow resource is 3 MWe and the P50 (most likely) power capacity is 1 MWe. The fumarole area has an expected resource temperature of 240°C (464°F) and given the size and permeability estimates mean capacity is 20 MWe and the P50 (most likely) capacity is 9 MWe. Permeability is the most difficult factor to predict and has a significant impact on the available geothermal fluids and therefore the amount of power that can be generated. The uncertainty in this value is the main reason for lower confidence in the likelihood of exploration success and the wide range in predicted MW values. The permeability has been difficult to locate as indicated by the results of the flow test at AK-3 in 2017. The test resulted in the production of some fluid, but due to low permeability, the recharge to the wellbore was too slow to sustain flow. The greatest probability of overall MWe production is in the fumarole region, but development is hampered by poor accessibility. The extremely high cost of road building for exploratory drilling, put an end to planning at an intermediate stage of the project and refocused attention on the viability of development in the hot spring outflow part of the resource. The City will be determining what the next steps are with AK-3, which is still that is still open and maintained in a suspended status in HSBV. While AK-3 remains accessible (not plugged with cement) it will need to be maintained. The City has a bond with AOGCC that will remain in place until the well is abandoned. GRG recommends that the well be maintained open as an asset for the foreseeable future.
This report summarizes the results of a literature review in three areas: available low-temperature/coproduced geothermal resources in the United States, energy use for space conditioning in commercial buildings, and state of the art of geothermal absorption cooling.
With a potential threefold increase in geothermal electricity generation by 2035, geothermal energy is increasingly recognized for its potential to reduce carbon emissions (EIA 2011a). Energy and environmental analyses are critical to developing a robust set of geothermal energy technologies that meet future demand. Previous work has summarized what is currently known about the life-cycle freshwater requirements of geothermal power-generating systems and the water quality of geothermal waters. This report builds upon that work, presenting an assessment of freshwater demand for future growth in utility-scale geothermal power generation and an analysis of freshwater use in low-temperature geopressured geothermal power generation systems. This is part of a larger effort to compare the life-cycle impacts of geothermal electricity generation with other power generation technologies. This report is organized with an introduction followed by two primary analysis sections. The introduction gives the background of the project and its purpose and summarizes the geothermal electricity generation technologies evaluated in this study. These technologies include conventional hydrothermal flash and binary systems, enhanced geothermal systems (EGS), and geopressured geothermal systems.
This technical report from Argonne National Laboratory presents an assessment of fresh water demand for future growth in utility-scale geothermal power generation and an analysis of fresh water use in low-temperature geopressured geothermal power generation systems.
Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, the U.S. Geological Survey's resource assessments, Play Fairway Analyses (PFA), and GeoVision report by the U.S. Department of Energy's Geothermal Technologies Office provided insights on enormous untapped potential for geothermal energy to contribute to the U.S. domestic energy needs. The past studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) comprise a huge potential. These blind systems can significantly increase power generation. But a primary challenge is locating and quantifying these hidden resources, which do not have any thermal manifestations on the surface. PFA has successfully identified some blind systems in the western USA (e.g., specific locations in the Great Basin region within Nevada). However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework called GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we were able to identify hidden patterns in the geothermal field data needed for the efficient discovery of blind systems. Crucial geothermal signatures often overlooked in traditional PFA are extracted using GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology on various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work), and the results provide valuable insights on resource types within those explored regions. This ML-enhanced workflow makes GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.
Master's Thesis: Cornell University
The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the 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. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://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. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.
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The region surrounding the Modoc Plateau, encompassing parts of northeastern California, southern Oregon, and northwestern Nevada, lies at an intersection between two tectonic provinces; the Basin and Range province and the Cascade volcanic arc. Both of these provinces have substantial geothermal resource base and resource potential. Geothermal systems with evidence of magmatic heat, associated with Cascade arc magmatism, typify the western side of the region. Systems on the eastern side of the region appear to be fault controlled with heat derived from high crustal heat flow, both of which are typical of the Basin and Range. As it has the potential to host Cascade arc-type geothermal resources, Basin and Range-type geothermal resources, and/or resources with characteristics of both provinces, and because there is relatively little current development, the Modoc Plateau region represents an intriguing potential for undiscovered geothermal resources. It remains unclear however, what specific set(s) of characteristics are diagnostic of Modoc-type geothermal systems and how or if those characteristics are distinct from Basin and Range-type or Cascade arc-type geothermal systems. In order to evaluate the potential for undiscovered geothermal resources in the Modoc area, we integrate a wide variety of existing data in order to evaluate geothermal resource potential and exploration risk utilizing ‘play-fairway’ analysis. Here, we consider that the requisite parameters for hydrothermal circulation are: 1) heat that is sufficient to drive circulation, and 2) permeability that is sufficient to allow for fluid circulation in the subsurface. We synthesize data that indicate the extent and distribution of these parameters throughout the Modoc region. ‘Fuzzy logic’ is used to incorporate expert opinion into the utility of each dataset as an indicator of either heat or permeability, and thus geothermal favorability. The results identify several geothermal prospects, areas that are highly favorable for the occurrence of both heat and permeability. These are also areas where there is sufficient data coverage, quality, and consistency that the exploration risk is relatively low. These unknown, undeveloped, and under-developed prospects are well-suited for continued exploration efforts. The results also indicate to what degree the two ‘play-types,’ i.e. Cascade arc-type or Basin and Range-type, apply to each of the geothermal prospects, a useful guide in exploration efforts.
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.
A third generation geothermal resource assessment has recently been completed for the conterminous US based on a revised heat flow map using over 35,000 data sites. Improvements include: nearly doubling the number of data points used in the analysis, more detailed thermal conductivity models for temperature-at-depth and heat flow calculations, and addition of a significant number of calibration wells as checks on the bottom hole temperature (BHT) correction. The results increase the accuracy of temperature-at-depth models for the conterminous US. These temperature models are the key input required for evaluating the non-conventional geothermal resources on a regional to sub-regional basis. The new resource assessment was prepared according to a proposed global protocol for estimating Enhanced/ Engineered Geothermal Systems (EGS) resource potential. A comparison of the new assessment approach with previous studies is provided, along with a discussion of the differences associated with the revised approach.
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 that 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 highvalue data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies.