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DOE OSTI2024

Techno-Economic Analysis of Greenfield Geothermal Hybrid Power Plants using a Solar or Natural Gas Steam Topping Cycle

The relatively low generation costs associated with wind, solar photovoltaic (PV), and natural-gas power plants make it challenging for geothermal power plants to produce and sell the power that has the reliability and sustainability characteristics that are greatly needed in U.S. power markets. This is especially true for geothermal resources with low-to-medium temperatures, which results in relatively low-thermal efficiency and generation costs that are higher than those for wind, solar PV, and natural gas. This analysis evaluates solar thermal- and natural-gas combustion waste heat recovery-based topping cycle hybridization of geothermal binary power plants. This approach provides several benefits that may allow geothermal power plants to generate power at more competitive costs. First, the addition of solar thermal energy or natural-gas combustion waste heat input to a geothermal power plant provides additional heat input that can be converted to electrical power. Second, the temperature level of the heat obtained from concentrating solar collectors or natural-gas combustion exhaust is higher than that of geothermal heat, which provides opportunities for improving the efficiency of the conversion of thermal energy to electrical power. Third, the ease with which solar thermal systems integrate with energy storage and the flexibility of natural gas means power generation can occur during peak demand periods. The hybrid cycles are compared to equivalently sized, co-located, independent geothermal, concentrating solar, and/or natural-gas power plants. The hybrid cycle tends to produce slightly more power than the standalone plants combined. However, the hybrid plant Levelized Cost of Energy (LCOE) is slightly higher than the LCOE of the combined standalone power plants for each of the case study locations investigated. Using the steam-topping cycle, organic Rankine cycle (ORC)-bottoming cycle hybrid plant design to combine a solar thermal resource and low- temperature geothermal resource (<120 degrees C) leads to a hybrid plant with a lower LCOE than a standalone geothermal-only system. Thus, hybrid plants may enable the economic development of geothermal resources in locations with low geothermal resource temperatures. However, in areas with higher geothermal resource temperatures (>120 degrees C), the geothermal-only plant has a lower LCOE than the hybrid cycle and thus could be developed without the need for solar heat addition. iv A geothermal-natural-gas reciprocating engine hybrid plant was evaluated for an Elk Hills, California case study location. The Elk Hills case study analysis indicates that when the natural-gas engine operates for more than 12 hours per day the hybrid plant can produce power at an LCOE lower than a standalone geothermal plant, and comparable to that of the standalone natural-gas reciprocating engine, while also reducing the carbon intensity of the power generated relative to the standalone natural-gas engine. This may represent a scenario in which the hybrid plant provides an opportunity for the deployment of a low-temperature geothermal resource that otherwise may have an LCOE too high to develop and operate as a standalone resource, while also reducing the carbon intensity of natural-gas generation sources. A "triple-hybrid" plant that combines natural gas, solar thermal, thermal energy storage, and geothermal was also investigated. A natural-gas combustion turbine (NGCT) is added to the geothermal-solar hybrid such that the hot exhaust gas from the gas turbine provides an alternative source of heat to the steam turbine of the hybrid cycle. Analysis results suggest that the triple-hybrid plant has a significantly higher energy generation and revenue than a standalone NGCT or the original geothermal-solar hybrid. The triple-hybrid design benefits most from using a smaller solar field so that the solar energy can be dispatched at the most valuable times available. The triple-hybrid plant also has a lower LCOE than the standalone NGCT. The triple-hybrid plant was evaluated making simple assumptions about the dispatch profile of the gas cycle, and more nuanced and realistic dispatching schedules should be analyzed in future work.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Retrospective on Recent DOE-Funded Studies Concerning the Extraction of Rare Earth Elements & Lithium from Geothermal Brines (Final Report)

Rare earth elements (REE) and lithium are non-toxic metals that are considered critical materials due to their use in electronics, magnets, batteries, and a wide variety of industrial processes important for the economy and military preparedness. Demand for REE and lithium is increasing and these critical materials are imported, so identifying and exploiting domestic sources of REE and lithium is a national priority. The U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) has been in the forefront of sponsoring research investigating the potential recovery of REE, lithium, and other critical minerals from geothermal brines. It has been proposed that the future of geothermal energy should include “hybrid systems” that combine electricity generation with other revenue-generating activities, such as recovery of valuable and critical minerals, including REE and lithium. Two recent GTO funding opportunities have focused on the recovery of REE and other valuable minerals from geothermal brines. The research supported by the GTO’s mineral recovery program is focused on three areas: resource characterization, technology for the extraction of REE, and technology for the extraction of lithium (Tables 1 and 2). This report is a retrospective study examining the outcome of GTO’s two recent mineral recovery programs (DE-FOA-0001016 in FY 2014 and DE-FOA-0001376 in FY 2016). In this report, the knowledge, technology, and techniques that were developed by researchers funded by GTO are summarized and discussed. Four projects were funded to assess the concentrations and amounts of REE found in geothermal brines and oil field produced waters. The GTO-funded studies compiled publically available data on REE concentrations from brines and produced water from all over the USA. In addition, new samples were collected and characterized from major geothermal and hydrocarbon basins in the Western USA. The studies examined the relationship between lithology and REE concentrations and developed models examining the influence of geology on REE concentrations in produced brines. It was determined that REE are frequently found at higher concentrations in oil field produced water than geothermal brines, but that some geothermal areas had significant REE resources. Significant reservoirs of REE were identified in the Western USA. In some cases, concentrations of REE were more than 1000 times the concentrations found in seawater. Collectively, these studies represent a comprehensive picture of REE resources associated with geothermal and hydrocarbon systems in the USA. The studies did not examine lithium resources, but in some cases, lithium concentration data was collected. Data from these studies are housed in the Geothermal Data Repository (GDR) and represent a significant information resource and it is recommended that these data be further analyzed in a future study. Eight projects were funded to develop new technology for REE extraction from geothermal fluids. These projects investigated sorption as an approach for removal and recovery of REE from geothermal brines. The projects investigated cutting-edge technology for selective sorption of ions from complex solutions, including the application of metal-organic frameworks and biosorbent proteins. The REE sorption studies tested different combinations of metal-binding ligands and solid supports. The most promising metal-binding ligands for REE included phosphonic acid, thiol, and carboxylic acid functional groups. Ligands were attached or incorporated into a wide variety of solid supports. In most cases, attachment was via covalent bonding to organic resins, polymers, or silica-based supports. Most of the REE projects were conducted at a low technology readiness level (TRL) and showed promise, but direct comparison between technologies was not possible based on the available information. It is recommended that testing and reporting be standardized to the extent possible to facilitate comparisons between technologies. Two projects were directed at novel lithium extraction technology. Both projects investigated the use of inorganic sorbents, including manganese oxides. One study also examined the use of metal- ion imprinted polymers as selective ion-exchange resins for the separation of lithium and manganese from brines. Both approaches showed promise for the selective extraction of lithium from brines, including potentially geothermal brines. Results from these GTO studies indicated that selective REE and lithium extraction is possible, but interference from co-occurring solutes, such as calcium, magnesium, or heavy metals, will interfere with process efficiency and negatively impact process economics. Techno-economic analysis conducted as part of the resource and technology studies suggest extraction of REE from geothermal brines is unlikely to be economically viable, especially since non-geothermal produced waters frequently have higher REE concentrations. It is recommended that benchmarks for techno-economic analysis be established to the extent possible for future studies, to facilitate direct comparison of various technologies. Based on the collective results of this program, it appears that hybrid geothermal power would benefit more from recovery of lithium and other metals, rather than REE. It is recommended that future studies be conducted at a higher- TRL and that sorbents be tested against actual geothermal fluid samples. Prior higher-TRL efforts to extract metals from geothermal brines should be further evaluated for lessons learned.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop: Preprint

Geothermal cost and performance evaluation implemented via technoeconomic assessment (TEA) modeling is critical for the Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for the Annual Technology Baseline (ATB). The ATB data are inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL’s reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the US generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on technoeconomic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next generation technologies such as closed loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

Annual Technology Baseline↗

DOE OSTI2025

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop

Geothermal cost and performance evaluation implemented via techno-economic assessment (TEA) modeling is critical for the U.S. Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for NREL's Annual Technology Baseline (ATB), which provides inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL's reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the U.S. generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on techno-economic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next-generation technologies such as closed-loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

annual technology baseline↗

DOE OSTI2024

An Evaluation of AI Models’ Performance for Three Geothermal Sites

Current artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites—Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals.

Energy & Fuels↗

DOE OSTI2021

Geothermal Representation in Power System Models

Power system models generally fail to capture the range of characteristics geothermal resources provide and the value they potentially contribute to decarbonization and reliability of future electricity grids as firm, dispatchable, non-combustion power resources. This study reviews the results of power system modeling efforts to investigate geothermal deployment potential in the United States, including the U.S. DOE GeoVision analysis and ongoing modeling and analysis efforts to support planning and development of future grids with 100% renewable energy in California. Several themes are identified that could be implemented immediately to improve the accuracy of geothermal representation in power system models: consistency of model inputs, modeling of baseload and dispatchable geothermal resources, accurate valuation of grid services, improved representation of capacity factor, use of contemporary LCOE estimates, improved understanding of the evolution of geothermal value, and use of accurate resource potential constraints. Many of the models reviewed produced significantly different amounts of geothermal resource selection - even when modeling the same region and time period. This highlights the variability of inputs and assumptions among models, so creating a consistent set of geothermal inputs is a first step toward more accurate representation of geothermal in models. Research opportunities are identified that could help improve geothermal data inputs in modeling efforts, including analyses of historical data, sensitivity to model inputs, and comparative value of geothermal generators as baseload or dispatchable resources. Outcomes of such research can inform the geothermal community about how best to guide geothermal development toward wider deployment in support of future electricity grids through improved understanding of the evolution of geothermal value over time and the characteristics that contribute to that value.

capacity expansion models↗

DOE OSTI2022

Geothermal Hydraulic Stimulation: Overview of Methods and Best Practices

This paper outlines important information related to the hydraulic stimulation of geothermal wells and considerations for regulators responsible for evaluating applications to conduct these operations. Hydraulic stimulation applied to geothermal wells is an evolving technology that is different from the hydraulic fracturing process widely used to complete unconventional oil and gas wells. Important differences relate to operating pressures, variations in rock and fracture processes, and characteristics and disposal of hydraulic fracture fluids. Hydraulic stimulation is not widely used in the geothermal industry; however, future development of enhanced geothermal systems (EGS) will require hydraulic stimulation in order to enhance and create fracture permeability to allow wells to deliver sufficient heat and fluid to power plants. Gigawatts of EGS potential have been identified (U.S. Department of Energy 2019), so it is expected that the use of hydraulic stimulation of geothermal wells will be more common in the future. At present, there are no formal regulations guiding drilling programs or sundry notices that propose hydraulic stimulation of geothermal wells. On federal lands, important constraints and oversight are embedded in the process for obtaining Geothermal Drilling Permits outlined in the Code of Federal Regulations (43 CFR Section 3262.11); in Geothermal Resource Order 2 (GRO 2) guidance for assessment and mitigation of impacts of geothermal operations as well as construction and testing of geothermal wells; and in a Bureau of Land Management (BLM) Induced Seismicity Instruction Memorandum (BLM 2018). Additionally, local knowledge from BLM field offices and expertise of BLM engineers, or that of state regulators for operations on private and state lands, will be applied to fully assess operator applications to conduct geothermal hydraulic stimulation activities. Significant research effort has been directed toward EGS, so geothermal hydraulic stimulation techniques, impacts, and results are evolving and becoming better understood, including adaptation of decades of oil and gas industry experience with hydraulic fracturing of unconventional reservoirs. Recent EGS activities in the United States provide details about hydraulic stimulation with respect to oversight, stimulation design, execution, and results. Based on experience to date, the important issues to address when hydraulically stimulating a geothermal well include the following: 1. Wellbore construction and integrity must be appropriate to protect groundwater and manage stimulation pressures. 2. Understanding of lithology, faults, fractures, and subsurface stress state is necessary to design stimulation plans and predict results. 3. Seismic monitoring allows for observation and mitigation of induced seismicity.

geothermal↗

DOE OSTI2024

GeoBridge: Connecting Communities to Geothermal Information and Opportunities: Preprint

The geothermal community is well established with long-standing events, organizations, and tools that are known across the geothermal community. But many of these tools and resources are located behind pay walls, require memberships, or are otherwise difficult to find, especially for people looking to join the geothermal community. These barriers to access can prevent outsiders from discovering valuable geothermal resources, limiting the geothermal community's potential for collaboration with other communities, such as clean energy entrepreneurs looking to expand into geothermal energy. The Department of Energy's (DOE) GeoBridge serves to bring these communities together by acting as a single, publicly accessible, searchable portal that facilitates easy access to available geothermal knowledge and information. It works to expand and diversify the pool of geothermal stakeholders by providing in-roads to geothermal information and community resources. It helps build a stronger geothermal community; one inclusive of individuals and groups from a variety of different backgrounds, including potential investors and start-up companies looking to accelerate innovation in geothermal technologies. By linking communities to geothermal information, analysis and expertise, GeoBridge serves as a launch point, directing interested parties to existing data and tools, events, educational resources, STEM programs, permitting and regulatory information, and other resources that can be used to evaluate, promote, and discover geothermal opportunities.

access↗

DOE OSTI2025

GeoBridge: Connecting Communities to Geothermal Information and Opportunities

access↗

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 OSTI2024

Techno-economic feasibility of geothermal energy production using inactive oil and gas wells for district heating and cooling systems in Tuttle, Oklahoma

Oil and gas wells have been drilled in the United States and often encounter hot co-produced water possibly suitable for geothermal direct-use applications. Here this study evaluated geothermal resources, heating and cooling demand, and techno-economic potential of four oil and gas wells repurposed for geothermal energy production to serve two public schools and 250 nearby houses in Tuttle, Oklahoma, USA. Subsurface geology in the study area primarily consists of Permian to Mississippian sandstone and limestone formations approximately from 2 km to 3.5 km depth. With a geothermal gradient of 25.3 °C/km, the temperature of geothermal energy production was estimated as 68.2 °C at 2.1 km. The potential of calcite scaling within boreholes and pipes was minimal. Using the characterized reservoir properties, techno-economic analyses were conducted for evaluating levelized costs of geothermal energy production for direct-use heating and cooling and district heating systems with five different production scenarios. Heating and cooling demand in two schools and 250 houses and additional 0.3 MW and 0.6 MW base heating loads for potential geothermal direct-use applications in the study area were also incorporated in the modeling. Results indicated that the levelized cost of heating of geothermal district heating systems utilizing existing wells were significantly lower than those of conventional geothermal energy systems that include well drillings. The geothermal energy production with doublet and quartet configurations was generally sufficient to supply total annual heating demand in the district, while a peaking boiler was used to supply peak loads. Doublet configurations showed higher production temperature with a lower thermal drawdown than the quartet configurations. The doublet system at 2.1 km production depth with 9.3 kg/s flow rate represented the optimal levelized cost of $71/MWh and 91.2% utilization factor. These results imply that the Tuttle geothermal district heating system is techno-economically feasible and comparable to geothermal district heating systems in the United States and the regional natural gas rate for residentials by recycling inactive oil and gas wells with no drillings.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Low-Temperature Geothermal Geospatial Datasets: An Example from Alaska

This project is a component of a broader effort focused on geothermal heating and cooling (GHC) with the aim of illustrating the numerous benefits of incorporating GHC and geothermal heat exchange (GHX) into community energy planning and national decarbonization strategies. To better assist private sector investment, it is currently necessary to define and assess the potential of low-temperature geothermal resources. For shallow GHC/GHX fields, there is no formal compilation of subsurface characteristics shared among industry practitioners that can improve system design and operations. Alaska is specifically noted in this work, because heretofore, it has not received a similar focus in geothermal potential evaluations as the contiguous United States. The methodology consists of leveraging relevant data to generate a baseline geospatial dataset of low-temperature resources (less than 150 degrees C) to compare and analyze information accessible to anyone trying to understand the potential of GHC/GHX and small-scale low-temperature geothermal power in Alaska (e.g., energy modelers, communities, planners, and policymakers). Importantly, this project identifies data related to (1) the evaluation of GHC/GHX in the shallow subsurface, and (2) the evaluation of low-temperature geothermal resource availability. Additionally, data is being compiled to assess repurposing of oil and gas wells to contribute co-produced fluids toward the geothermal direct use and heating and cooling resource potential. In this work we identified new data from three different datasets of isolated geothermal systems in Alaska and bottom-hole temperature data from oil and gas wells that can be leveraged for evaluation of low-temperature geothermal resource potential. The goal of this project is to facilitate future deployment of GHC/GHX analysis and community-led programs and update the low-temperature geothermal resources assessment of Alaska. A better understanding of shallow potential for GHX will improve design and operations of highly efficient GHC systems. The deployment and impact that can be achieved for low-temperature geothermal resources will contribute to decarbonization goals and facilitate widespread electrification by shaving and shifting grid loads.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Salton Sea Geothermal Development: Nontechnical Barriers to Entry – Analysis and Perspectives

Geothermal energy offers an opportunity to generate baseload, renewable energy that can help support the transition to an energy economy with reduced impacts on climate change and replace older, more expensive, nonrenewable, and more resource-impacting energy-generation facilities. The United States has the largest known geothermal resource in the world, with over 31 GW of conventional geothermal potential. However, due to market conditions, an inability to properly quantify both electrical grid benefits and resource stability, and the difficulty of exploring and developing the geothermal resource, few new geothermal projects have come online over the past three decades. The Salton Sea, in Imperial County, California, provides a prime location and opportunity to develop new geothermal resources. The Salton Sea contains a robust, well-mapped, geothermal resource, with opportunities for concurrent development of lithium and other mineral resources. This report describes the history of geothermal development at the Salton Sea and compares geothermal to other renewable energy sources in the area. The report then uses a techno-economic analysis (TEA) model to analyze the relative benefits and costs of various challenges and opportunities and provides recommendations for streamlining geothermal development at the Salton Sea and elsewhere. The challenges and opportunities analyzed in the TEA model were informed by stakeholder interviews and literature reviews. Based upon the identified challenges and opportunities and the results of the TEA model, primary findings are that certain nontechnical barriers such as permitting costs play only a minor role in determining the viability of development of the geothermal resource at the Salton Sea. Other barriers such as permitting timelines, government/agency coordination, and the potential co-location of lithium extraction with a geothermal plant may result in much larger impacts on project viability.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Geothermal Play Fairway Analysis Best Practices

Play fairway analysis (PFA) is a methodology that can improve success rates for geothermal exploration drilling, thus reducing the costs of geothermal projects while facilitating development in new areas. It was originally developed for the oil and gas industry, but has been adapted for discovering geothermal resources over the last decade. The geothermal PFA methodology involves systematically screening a set geographic area for promising qualities typically related to the presence of heat, permeability, and fluid. Successful application of PFA can identify hidden hydrothermal systems. From 2014 to 2021 the U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) supported the development of PFA for geothermal resources through awards to 11 research teams across the country. The goal of these projects was to advance and adapt PFA for geothermal exploration to produce regional-scale maps that reduce exploration uncertainty. This report is an outcome of the NREL-led PFA Retrospective project, which compiled, synthesized, analyzed the results of GTO's geothermal PFA program. Ultimately, we find that these projects greatly advanced approaches to geothermal exploration and resulted in extensive new data and new discoveries of unrecognized geothermal systems. We used the results to distill best practices in this report and to provide guidance for future applications of geothermal PFA.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Assessment of Economic Impact of Permitting Timelines on Produced Geothermal Power in Imperial County, California

Despite having a large geothermal power potential in the United States, only a small fraction has been developed for power generation. Various barriers, including technical, financial, and regulatory permit delays, are attributed to lower contribution of geothermal energy in the national grid. Unpredictable environmental reviews and permitting timelines are some of the non-technical barriers that can cause delays in geothermal exploration and utilization plans. Our study shows that the geothermal permitting timelines can vary from six months to several years, depending on the presence or absence of biological resources, cultural resources, and sensitive environmental issues at the project site. The potential impacts of these permit barriers can range from investors abandoning geothermal development to making the product (i.e., electricity) more expensive and uncompetitive. In this study, we conducted economic analysis to assess the impact of permitting timelines on cost of produced electricity from geothermal resources using data from existing geothermal plants as well as prospective sites. In this paper, we present collected timelines data, approach, and results of economic impact of permitting timelines on geothermal power. We evaluated the various environmental management and permit review processes by considering a hypothetical geothermal project in the Salton Sea Known Geothermal Resource Area. Because of the variety of the biological and environmental issues and the involvement of local, state, and federal agencies with overlapping jurisdictions, this project could go through one of the many California Environmental Quality Act (CEQA) and National Environmental Policy Act (NEPA) review scenarios that range from the least to the most complex in its circumstances. The fastest CEQA/NEPA review timelines would have the project completed in six years. In contrast, the project would substantially need longer time to complete if it were located in an area with significant environmental resources or cultural issues that required permitting from various agencies. With increasing project completion timelines, the simplified levelized cost of electricity (sLCOE) can be 4 to 11% higher with longer CEQA/NEPA review timelines than the sLCOE value with the fastest CEQA/NEPA review timeline. Lengthier CEQA/NEPA review timelines could also result in loss of $64 million to $227 million in potential revenue. Such significant economic impacts could determine the success of a geothermal project.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Geothermal Energy and Resilience in Arctic Countries

The eight Arctic countries - Iceland, Canada, Denmark (Greenland and the Faroe Islands) Norway, Sweden, Finland, Russia, and the United States (Alaska) - have diverse energy systems, but can be split into two distinct groups based on energy characteristics. The first group includes systems in Europe (Finland, Norway, Sweden, and Iceland), which are heavily grid-connected. The second group includes the United States (Alaska), Canada, Russia, and Greenland, which have grid-connected energy systems in their more densely populated southern regions, but are also defined by the prevalence of remote microgrids. Energy sources for heat and power vary across grid-connected communities in the Arctic nations. The primary energy source for remote communities, on the other hand, is almost exclusively diesel. This is true for both heat and power. Despite these and other key distinctions, Arctic countries share many commonalities with regard to their energy systems. One is a fundamental need for heat. Heat and electric energy are linked in most communities - remote, rural, and urban - and those linked systems are increasingly vulnerable to disruptions. Several of the Arctic countries use baseload renewable energy resources for heat and power. Iceland uses geothermal and hydroelectric; Canada, the United States, Sweden, Norway, and Finland use hydroelectric. Utilization of baseload renewable energy resources on-site for combined heat and power appears to enhance the resilience of communities in Arctic countries with high penetration of those resources. On the other hand, reliance on diesel by remote communities in other Arctic countries may be amplifying vulnerabilities. Although geothermal energy is currently used in all eight Arctic countries, resources are poorly mapped, and details can be difficult to come by. Despite this, geothermal energy provides heat and sometimes electricity at both utility scales and at the microgrid scale. Geothermal electricity is produced in Iceland, Russia, and the United States (Alaska). Direct use of geothermal heat is used in Iceland, Russia, United States, Canada, and Norway. Geo-exchange is used in Sweden, Finland, Norway, Canada, and the United States. In this paper, we reframe geothermal heat and power systems as integrated energy systems, asking the question: are integrated geothermal energy systems - where available and economic - resilient solutions for communities in Arctic countries? We identify resilience attributes of integrated geothermal energy systems, with a focus on microgrids and small-scale applications. Based on the high-level, qualitative analysis presented in this paper, the answer appears to be yes. Further work should prioritize refining our understanding of geothermal resources in Arctic countries, because development of the most economic geothermal resources in Arctic countries has the potential to enhance the energy resilience of its residents, whether in a grid-connected or remote off-grid context.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

GeoThermalCloud: Machine Learning for Geothermal Resource Exploration

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.

machine learning (ML), geothermal energy↗