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Hawai‘i Play Fairway (Final Report)

Most of Hawai'i's geothermal resources are blind—their manifestations, such as hot springs and steam vents, do not appear on the ground surface because the heated water flows far below. With the exception of $K\bar{i}lauea East Rift Zone$, in most areas of Hawai'i, high lateral permeability in the first kilometer below ground surface prevents surface thermal features from developing. As a methodology for discovering these blind resources, Play Fairway Analysis (PFA) involves finding potential locations of blind hydrothermal systems and describing potential geothermal sources in rift-zone settings. Using the PFA to find Hawai'i's geothermal resources, the University of Hawai'i (UH) conducted the Hawai‘i Play Fairway Project, Hawai'i's first statewide geothermal resource assessment since 1985. Sponsored by the U.S. Department of Energy, the Hawai'i Play Fairway Project provided an updated resource assessment, a roadmap for additional exploration activities, and the identification of areas for further exploration. Benefitting from UH's core competency in earth sciences and experienced geothermal researchers, the project comprised three phases. During the first phase, the team identified, compiled, and ranked existing geologic, groundwater, and geophysical datasets relevant to subsurface heat, fluid and permeability. Using a Bayesian statistical approach, the team developed a statistical methodology to integrate these data into a resource probability map. The team evaluated the confidence in the probability value and considered development viability of areas with geothermal resources. With these analyses, the team identified 10 locations in the Hawaiian Islands for exploration activities. For the second phase, the team collected new groundwater data in 10 locations across the state and new geophysical data on $L\bar{a}na‘i, Maui$, and central Hawai'i Island and modeled topographically induced stress to better characterize subsurface permeability. Analyzing the subsurface stresses, the team evaluated the potential for fracture-induced permeability. The team inverted the MT and gravity data to produce 3D models of resistivity and density, respectively, on $L\bar{a}na‘i$, across $Haleakal\bar{a}'s$ SW rift (Maui), and surrounding Mauna Kea (Hawai‘i Island). The team developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The team incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. Through combining data from the first and second phases, the team determined locations for further exploration during the third phase. For MT and gravity surveys, the team recommended $Kaua'i's$ $L\bar{i}hu'e$ $Basin$, the east rift of $Maui's$ $Haleakal\bar{a}$ volcano, and the southwest rift of Hawai'i Island's Mauna Loa volcano. The MT and gravity surveys aimed to enable improved confidence in the resource potential in these locations. For drilling deep groundwater well(s), the team recommended Southeast Mauna Kea and $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$. During the third phase, further exploration involved drilling a groundwater well in $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$ and performing more geophysical surveys. We deepened an existing water well proximal to our target area on $L\bar{a}na'i$ due to funding constraints that precluded us from spudding a new well that would exceed 1km depth. Drilling was preceded by a number of substantial elements including: writing an Environmental Assessment and the subsequent legal process, performance of deviation logging, lowering a camera down the well, coordinating site preparation with $P\bar{u}lama$ $L\bar{a}na'i$, shipping the UH-owned rig interisland, procuring supplies, and leading 3 community meetings on $L\bar{a}na'i$. Drilling occurred 24/7 the entire month of June 2019 over which time $L\bar{a}na'i$ $Well$ 10 was deepened from 427 m to 1057 m, with continuous core collected. We measured a roughly linear temperature gradient averaging 42°C/km and a maximum bottom hole temperature of 66°C. This gradient is more than twice the background for Hawai'i and within a range of gradients measured in this depth range for some exploration wells within KERZ. We consider these results encouraging for $L\bar{a}na'i's$ resource potential and recommend following with a slim hole within $L\bar{a}na'i's$ caldera (our target zone) to ~ 2 km. Further, the positive implications such results have for the island of O‘ahu are substantial - the shield stage of O'ahu's volcanoes ended 1-2 My earlier. However, O'ahu uses more electricity than the rest of the islands combined, and the utility recently called for 500-700MW of firm, dispatchable renewable electricity on O'ahu by 2033. In Phase 3, we also collected limited new encouraging groundwater data, and updated our thoughts on the probabilities of fluid and permeability at resource depths (Pr F = 1; Pr P = mostly unconstrained). Ultimately, we advocate for using our final probability of heat, and confidence in this probability, to drive the next phase of exploration. We contend further development of geothermal in Hawai‘i will enable the state to achieve its 100% renewable policy objective and Hawai'i to transition off of fossil fuels through geothermal discovery and development. The project not only produced a large amount of data and expanded the existing knowledge of Hawai'i's geothermal resources, but also produced publications, theses, presentations, core photos, datasets, media reports, television interviews, community events, and a blog. Students and new professionals benefitted from the project's hands-on research experiences and educational opportunities and earned awards and recognition.

15 GEOTHERMAL ENERGY↗

USAID Colombia Young Leaders Workforce Training Program Action Plans: Regulatory Road Map for Distributed Energy Resource (DER) Interconnection and Interoperability

As part of the U.S. Agency for International Development (USAID)-National Renewable Energy Laboratory (NREL) Young Leaders Workforce Training Program in Colombia, participants from Colombia's Energy and Gas Regulatory Commission (CREG) leveraged their training and professional experience to develop a regulatory roadmap for the interconnection and interoperability of increasing integration of distributed energy resources (DERs). CREG was one of four teams selected by the training program development team to receive continued technical assistance and strategic advisory support from the USAID-NREL Partnership for action plan development and implementation. This case study provides an overview of the key activities and outcomes of the technical assistance provided by NREL on the CREG Action Plan, and key next steps for implementation of Colombia's DER Integration Regulatory Roadmap.

24 POWER TRANSMISSION AND DISTRIBUTION↗

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↗

A Four-Layer Cyber-Physical Security Model for Electric Machine Drives Considering Control Information Flow

Despite the IEEE Power Electronics Society (PELS) establishing Technical Committee 10 on Design Methodologies with a focus on the cyber-physical security of power electronics systems, a holistic design methodology for addressing security vulnerabilities remains underdeveloped. This gap largely stems from the limited integration of computer science and power/control engineering studies in this interdisciplinary field. Addressing the inadequacy of unilateral cyber or control perspectives, this article presents a novel four-layer cyber-physical security model specifically designed for electric machine drives. Central to this model is the innovative control information flow (CIF) model, residing within the control layer, which serves as a pivotal link between the cyber layer's vulnerable resources and the physical layer's state-space models. By mapping vulnerable resources to control variable space and tracing attack propagation, the CIF model facilitates accurate impact predictions based on tainted control laws. The effectiveness and validity of this proposed model are demonstrated through hardware experiments involving two typical cyber-attack scenarios, underscoring its potential as a comprehensive framework for multidisciplinary security strategies.

97 MATHEMATICS AND COMPUTING↗

exRNA-eCLIP intersection analysis reveals a map of extracellular RNA binding proteins and associated RNAs across major human biofluids and carriers

Although the role of RNA binding proteins (RBPs) in extracellular RNA (exRNA) biology is well established, their exRNA cargo and distribution across biofluids are largely unknown. To address this gap, we extend the exRNA Atlas resource by mapping exRNAs carried by extracellular RBPs (exRBPs). This map was developed through an integrative analysis of ENCODE enhanced crosslinking and immunoprecipitation (eCLIP) data (150 RBPs) and human exRNA profiles (6,930 samples). Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, cerebrospinal fluid, and cell-culture-conditioned medium. exRBPs carry exRNA transcripts from small non-coding RNA biotypes, including microRNA (miRNA), piRNA, tRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), Y RNA, and lncRNA, as well as protein-coding mRNA fragments. Computational deconvolution of exRBP RNA cargo reveals associations of exRBPs with extracellular vesicles, lipoproteins, and ribonucleoproteins across human biofluids. Overall, we mapped the distribution of exRBPs across human biofluids, presenting a resource for the community.

59 BASIC BIOLOGICAL SCIENCES↗

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗

Hawaii Play Fairway Analysis: Lanai Resistivity and Density 3D Inversion Models

To prepare for its third phase, the Hawaii Play Fairway project conducted groundwater sampling and analyses in ten locations in the Hawaiian islands, magnetotelluric (MT) and gravity surveys, as well as calculations of 3D subsurface stress due to the weight of the rock underlying the topography of the volcano. The subsurface stresses were used to evaluate the potential for fracture-induced permeability. Inversions of the MT and gravity data produce 3D models of resistivity and density, respectively, on Lanai, across Haleakala's SW rift (Maui), and surrounding Mauna Kea (Hawaii Island). The project developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The project then incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. These results were used to identify target sites for exploratory drilling. Spreadsheet information: Each sheet contains data for a particular depth in kilometers. Positive depths are above sea level, and negative below. For more information, go to the Hawaii Groundwater and Geothermal Resources Center website linked in the resources.

15 GEOTHERMAL ENERGY↗

Scaling Automatic Vector Data Alignment to Satellite Imagery

Given the tremendous volume of accessible Earth Observation (EO) data, there is a need to develop scalable Geospatial Artificial Intelligence (GeoAI) solutions for time-sensitive applications. Scalability in this context refers to rapidly processing large-scale EO data using high performance computing resources. Accurate mapping of the built environment from remote sensing (RS) imagery has been one of the crucial components in GeoAI workflows for a wide spectrum of humanitarian applications. Derived vector data of built environment is often leveraged for disaster preparedness and response activities. However, factors such as differences in ortho-rectification, atmospheric conditions and human error, results in spatial misalignment between vector data and the timely available RS imagery. Model training for downstream tasks such as object detection, change analysis, etc., is negatively impacted due to such spatial misalignment. Although there has been progress towards automatic alignment of vector data, the lack of scalability remains an open research challenge. This paper proposes to leverage parallel computing to optimize an automatic vector data alignment workflow. It further employs CPU-level multi-core parallelism for improving the performance of the workflow for scalable built environment mapping. We report observations and discuss findings from the preliminary experiments performed on the Summit Supercomputer.

Potnis, Abhishek↗

Critical Materials Supply Chain White Paper (April 2020)

The DOE Office of Energy Efficiency and Renewable Energy (EERE) Advanced Manufacturing Office (AMO) partners with industry, small business, universities, and other stakeholders to identify and invest in emerging technologies with the potential to create high-quality domestic manufacturing jobs and enhance the global competitiveness of the United States

Advanced Manufacturing Office↗

Sorghum Association Panel whole‐genome sequencing establishes cornerstone resource for dissecting genomic diversity

SUMMARY Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole‐genome sequencing (WGS) of 400 sorghum ( Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25–72×), enabling the development of a high‐density genomic marker set of 43 983 694 variants including single‐nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower‐density marker sets. WGS identified and scored variants in 5‐kb bins where available genotyping‐by‐sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six F st peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

59 BASIC BIOLOGICAL SCIENCES↗

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey's 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score < 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret.

15 GEOTHERMAL ENERGY↗

Mapping potentials and bridging regional gaps of renewable resources in China

Reasonable and effective use of renewable resources can reduce dependence on traditional fossil-based energy sources and reduce carbon emissions. This study mapped the spatial potentials of renewable resources (i.e., solar radiation, precipitation, wind, and geothermal resources) in China. The results showed that China's most abundant renewable resources are located in the southwestern regions, which are significantly different from the spatial distribution patterns of population and economic development. Four southwestern provinces (Tibet, Qinghai, Sichuan, and Yunnan) make up only 7% of the national gross domestic product (GDP) and 30% of the national land area but possess 58% of the renewable resources. Furthermore, we found a weak to moderate degree of negative correlation between the emergy density of renewable resources and GDP per capita on the administrative levels of the prefecture-level cities for the whole country and in its eastern, central, and western regions. This means that the socioeconomically underdeveloped Midwest has more abundant renewable resources. A distributed energy-economic system may help to bridge the regional gaps of renewable sources in China. These findings can support policy decisions for the better development and use of renewable resources in China.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

ICED: An Integrated CGRA Framework Enabling DFVS-Aware Acceleration

oarse-grained reconfigurable arrays (CGRAs) are a promising solution to enable energy-efficient acceleration of applications from different domains. By leveraging reconfiguration at the functional level, they can adapt to significantly different computational patterns. Existing CGRA mapping approaches extract instruction-level parallelism, exploit loop-pipelining opportunities, guarantee the data dependency, and target high throughput of a given loop. However, the recurrence data-dependency in the DFG and the mismatch between required and available computing/communication resources complicate the mapping, and might lead to significant unbalances in the utilization of the CGRA's tiles. This results in wasted power for tiles with low utilization. Applying dynamic voltage and frequency scaling (DVFS) can potentially solve this challenge and improve energy efficiency by adjusting voltage and frequency of different tiles independently. CGRAs have also been successful in accelerating data-dependent streaming applications. However, in these applications, the execution time of each kernel in the pipeline might dynamically vary depending on the characteristics of the input. This also leads to under-utilization of resources for the dynamically changing kernels that do not limit the application throughput. DVFS can also improve energy efficiency for these applications by dynamically changing the voltage and frequency levels of tiles that host non performance-constraining kernels. This paper proposes ICEDTEA -- an integrated DVFS-aware framework to map applications on CGRAs that support power islands. ICEDTEA proposes a CGRA architecture supporting DVFS islands at varying granularity (from a single tile to a group of tiles) and the related DVFS-aware compilation and mapping toolchain. ICEDTEA is the first work that introduces DVFS support for spatio-temporal CGRAs at power-island levels. The experimental evaluation shows that ICEDTEA improves average utilization by 2.3$\times$ and energy-efficiency by 1.32$\times$ over a conventional CGRA. With streaming applications, ICEDTEA improves energy efficiency by 1.12$\times$ over a state-of-the-art CGRA that introduces partial dynamic reconfiguration to adapt to variations in kernels' throughput.

Tan, Cheng↗

Analysis of Geothermal Resources in Three Texas Counties

This project updates the geothermal resources beneath our oil and gas fields, as part of the research for the Texas GEO project. This report "Analysis of Geothermal Resources in Three Texas Counties" (October 2020) improves on previous mapping of the Texas resources for the counties of Crockett (West Texas), Jackson (central Gulf Coast) and Webb (South Texas). Through additional bottom-hole temperatures (BHT), the number of well sites increased from 532 to 5,410 in total for these counties. The improved methodology to calculate formation temperatures from 3.5 km (11,500 ft) to 10 km (32,800 ft) includes thermal conductivity values more closely related to the actual county geological formations, incorporated radiogenic heat production of formations, and the related mapped depth to basement. The results show deep temperatures as hotter than previously calculated, with temperatures of 150 degrees Celcius possible for Webb County between depths of 2.6 - 5.1 km, Jackson County between depths 3.0 - 5.4 km, and Crockett County between depths of 2.7 - 8.0 km.

10 km↗

Shareable FPGA compute engine

Systems, apparatuses, and methods for sharing an field programmable gate array compute engine are disclosed. A system includes one or more processors and one or more FPGAs. The system receives a request, generated by a first user process, to allocate a portion of processing resources on a first FPGA. The system maps the portion of processing resources of the first FPGA into an address space of the first user process. The system prevents other user processes from accessing the portion of processing resources of the first FPGA. Later, the system detects a release of the portion of the processing resources on the first FPGA by the first user process. Then, the system receives a second request to allocate the first FPGA from a second user process. In response to the second request, the system maps the first FPGA into an address space of the second user process.

Kegel, Andrew G.↗

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↗

Low-Temperature Geothermal Play Fairway Analysis for the Denver Basin: Preprint

This project is part of a national initiative to showcase the benefits of incorporating low-temperature geothermal resource assessment into the deployment of geothermal heating and cooling (GHC), combined heat and power (CHP), and geothermal direct use (GDU) technologies. The initiative was established to accelerate the country's decarbonization efforts by identifying potential for low-temperature geothermal resource utilization (< 150 Degrees Celsius, i.e., GHC, CHP, and GDU) in selected sedimentary basins with numerous population centers. The Play Fairway Analysis (PFA) methodologies in this study were adapted from previous PFA investigations of sedimentary basin geothermal play types (SBGPTs) that evaluated the potential for low-temperature resources (< 150 Degrees Celsius). Workflows, relevant datasets, python code, common and composite geological criteria maps are utilized to develop low-temperature geothermal resource favorability maps for the Denver Basin, a sedimentary basin spanning Colorado, Nebraska, and Wyoming. The replication of these methodologies in other SBGPTs can evaluate potential for low-temperature resources. To facilitate future assessment of low-temperature geothermal resources in SBGPTs, this project provides PFA workflows, data, tools, and favorability maps that will ultimately support the utilization of low-temperature geothermal resources in sedimentary basins.

Denver Basin↗