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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Subsurface Characterization of Hydraulic Fracture Test Site-2 (HFTS-2), Delaware Basin

Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Wolfcamp Formation of the Permian (Delaware) Basin. This paper focuses on integration, advanced geological characterization, and 3D subsurface modeling of the comprehensive HFTS-2 dataset. The study showcases a multidisciplinary reservoir characterization approach that incorporates geology, petrophysics, geochemistry, geomechanics, microseismic, and subsurface engineering analysis. Subsurface characterization of organic-rich mudstone formations requires understanding complex hydraulic fracture network growth in relation to inherent lithology, geomechanical properties, and interaction with pre-existing natural fractures. This paper presents a characterization workflow incorporating pre- and post-stimulation subsurface data, unique to the HFTS-2 dataset. The study integrated: (1) rock properties from logs, cores, and thin sections; (2) natural and hydraulic fracture descriptions from cores and image logs; (3) local and regional stresses; (4) geomechanics; (5) microseismic; (6) fiber optic (FO) and bottomhole pressure gauge (BHPG) response; and (7) produced fluids analysis. During a stimulation treatment, creation of the stimulated rock volume (SRV) is influenced by several subsurface factors. Key contributing factors include structural context, stress conditions, lithology, facies architecture, pre-existing natural fractures, and geomechanical properties. The HFTS-2 subsurface data integration indicates that the SRV is comprised of a complex juxtaposition of hydraulic fracture swarms, as evidenced by image logs analysis, core description, and microseismic monitoring. The HFTS-2 microseismic event density was used to generate 3D heat maps that serve as a representative SRV footprint, corroborated by secondary datasets. These maps were further integrated with petrophysical and geomechanical characteristics, as well as responses from FO and BHPG, to estimate the lateral and vertical dimensions of the effective fractures. The geological characterization for the HFTS-2 dataset combined with 3D modeling for petrophysical and geomechanical properties provides a strong foundation for subsurface simulation and optimization studies. Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/1-21URTC/D011S005R001/2477501/urtec-2021-5243-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5243 The workflow improved our understanding of HFTS-2 hydraulic fracture propagation and characteristics in relation to offset pressure depletion and interaction with pre-existing natural fractures. Analysis showed that fracture geometry varies by stage and by well, and a complex fracture network is generated with varying fracture density. The multidisciplinary workflow presented herein for integration and characterization serves as a foundation to evaluate completion efficiency and estimate areal and vertical stimulation and depletion extent for the project. Furthermore, the workflow and learnings can also be transferred to other unconventional plays.

58 GEOSCIENCES↗
DOE OSTI2017

The Application of Radon for Mapping Open Fracture Networks in a Thin Vadose Zone

The use of naturally occurring tracers to elucidate subsurface flow and transport of water and gasses through fractured vadose zones represents a powerful and inexpensive methodology for understanding geologic systems. Our study was conducted at Sheply’s Hill, a highly fractured granite highland at the old Fort Devens military base. This study evaluates the applicability of using cheap, readily available passive radon detectors to identify conductive structures (e.g., fractures and fracture networks) in fracture-dominated vadose zones. Radon data from this study provides strong evidence that, even within an intensively fractured system, a relatively smaller subset of the fractures is responsible for the majority of the vertical migration of gas and water that recharges contaminated groundwater sites. Our study found that zones of high recharge potential associated with open fracture networks could be identified based on the 222 Rn flux within the highly fractured Shepley’s Hill. Based on the results of this study, 222 Rn is an environmental tracer capable of efficiently detecting major bedrock fracture sets in the vadose zone responsible for water infiltration into contaminated aquifers. Results from the Shepley’s Hill study show a clear spatial correlation of elevated 222 Rn concentrations emanating from fracture zones previously identified by independent geologic studies. At Shepley’s Hill, the 222 Rn concentrations measured from detector locations directly above the major bedrock fractures, located within the Disc Golf Fracture Zone (DGFZ) and the Nona-Shep Fracture Zone (NSFZ), were almost exclusively the highest values measured in the study. Conversely, probes located above areas of less-fractured bedrock, although highly fractured, showed relatively low 222 Rn concentrations. Furthermore, these two observations provide strong supporting evidence that not all fractures are equal when it comes to their ability to transmit gas or water into and out of the subsurface.

58 GEOSCIENCES↗
DOE OSTI2015

Carbon Storage Atlas, Fifth Edition - Updated Information on Geological Sinks and CO 2 Sources

The Carbon Storage Atlas contains the following sections: (1) Introduction to CCS; (2) DOE’s Carbon Storage Activities; (3) National Perspectives; (4) Large-Scale Field Projects; (5) Small-Scale Field Projects; and (6) American Recovery and Reinvestment Act (ARRA) Site Characterization Projects. The Introduction to CCS section is an overview of CCS. The DOE’s Carbon Storage Activities section is a summary of CCS activities including information on DOE’s Carbon Capture and Storage Programs, NETL’s Research and Development, DOE’s Systems Analysis Activities, and DOE’s Interagency and Global Collaborations, and Knowledge Sharing Efforts. The National Perspectives section contains maps showing the number, location, and magnitude of CO 2 stationary sources in the United States and other portions of North America, as well as the areal extent and estimated CO 2 prospective storage resource available in RCSP-evaluated geologic formations. The Large-Scale Field Projects section provides detailed information on various aspects of the large-scale injections conducted by the RCSPs. The Small-Scale Field Projects and Site Characterization Projects sections provide summaries of field project activities that augment the efforts of the large-scale field projects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Geothermal play-fairway analysis of Washington State prospects

Herein, analysis of existing geologic, geophysical, and geochemical data revealed areas with elevated heat and permeability, defining three promising plays along the central axis of the magmatic arc of Washington State: Mount St. Helens, Wind River valley, and the southeast flank of Mount Baker. These areas are geothermal ‘fairways’, or locations with high geothermal resource potential based on modeling. This project applies innovative data analyses (Poly3D) in conjunction with methods proven in previous geothermal favorability studies (ArcGIS and MATLAB) to extract new value from existing public data. Heat and permeability potential models for each play are weighted using the Analytical Hierarchy Process, and combined to map geothermal resource potential and locations for further data collection at each site. Heat potential is based on: (1) temperature gradients, (2) volcanic vents, (3) Quaternary intrusive rocks, (4) spring temperature, and (5) spring geothermometry. Permeability potential is based on: (1) slip and (2) dilation tendency on mapped and seismic faults (3) maximum shear strain rate, and (4) dilatational strain rate at the surface, (5) modeled fault displacement distribution, and (6) displacement gradient, (7) shear, and (8) tensile fracture density, and (9) local geology and geophysical data. This information is vital for revealing the small scale heat and permeability potential of each study area, locating areas of undiscovered or untapped resources, and reducing the risk and cost involved in greenfield exploration and development. Preliminary analysis of the three play areas shows promise for geothermal development. Uncertainty modeling is underway and the results will guide future exploration plans.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Carbon Storage Technical Viability Approach (CS TVA) Matrix

The Carbon Storage Technical Viability Approach (CS TVA) Matrix is a knowledge framework developed to outline the information needed for geologic carbon storage. The CS TVA Matrix contains 5 categories, 14 sub-categories, and 47 components. This framework can be leveraged to assess the availability of data and information needed for a carbon storage project. The information categories of the matrix are tied to a list of required data using weighted mapping, published herein.

carbon storage↗
DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Generalized fiducial inference on differentiable manifolds

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. Here, this new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a constrained generalized fiducial distribution (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Calcite Twinning in Mollusk Shells and Carrara Marble

Mollusk shells protect the animals that form and inhabit them. They are composites of minerals and organics, with diverse mesostructures, including nacre, prismatic calcite, crossed-lamellar aragonite, and foliated calcite. Twins, that is, crystals mirror symmetric with respect to their coherent interface, occurring as formation or deformation twins, are observed in all mollusk shell mesostructures but never within calcite prisms. Here, nanotwins and microwins within single calcite prisms are observed in different shells. Using Polarization-dependent Imaging Contrast (PIC) mapping with 20–60 nm resolution, twins are observed to be 0.2–3 µm thick layers of differently oriented and colored crystals with respect to the main prism crystal. Multiple twins are interspersed with the prism crystal, parallel to one another, and similarly oriented. When comparing images of calcite prisms and twins obtained by PIC mapping and by Electron Back-Scattered Diffraction (EBSD), the images correspond precisely. All twins are e-twin types, with 127° angular distance between c-axes. E-twins are the most common deformation twins in geologic calcite, as also observed here in Carrara marble. Location of all twins near the outer surface of all shells and e-twin type both suggest that twins within calcite prisms in mollusk shells result from deformation twinning.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Roughrider Carbon Storage Hub (Final Report)

The Roughrider Carbon Storage Hub was a 2-year project (October 2023 – September 2025) conducted by the Energy & Environmental Research Center (EERC) focused on advancing the feasibility of a commercial-scale carbon dioxide (CO 2 ) geologic storage hub in McKenzie County, North Dakota. The project’s objective was to investigate the potential that stacked storage complexes (multiple deep saline formations) can safely and economically store at least 50 million tonnes of CO 2 within 30 years. The captured CO 2 would be sourced from industrial emitters including project partner ONEOK, Inc.’s gas-processing plants and a planned gas-to-liquids facility. Drilling of the Roughrider 1 stratigraphic test well (14,979-ft total depth) was completed in November 2024. The wellbore intersected four candidate storage formations: Inyan Kara, Broom Creek, Mission Canyon, and Black Island–Deadwood. Operational challenges, including a stuck drill string, were resolved without long-term impact. A comprehensive logging and coring program was conducted, followed by successful well abandonment and site reclamation. Over 660 ft of 4-in. whole core was retrieved. Core plug samples were processed and analyzed for petrophysical and geochemical properties. Results confirmed promising porosity and permeability in the Inyan Kara and Broom Creek Formations and removal of the Mission Canyon and Black Island–Deadwood horizons from further investigation. Data derived from the logging and coring program were used to improve initial geologic models built from legacy data. CO 2 injection simulations showed that the Inyan Kara alone can feasibly store the target mass of CO 2 . Because of subtle differences in geologic structure and porosity trends between the formations, a stacked storage scenario using the Broom Creek and Inyan Kara Formations resulted in a larger overall plume area than using the Inyan Kara alone. Preliminary CO 2 pipeline routes from the industrial sources were mapped utilizing existing rights of way and evaluated for capacity and cost using U.S. Department of Energy Office of Fossil Energy and Carbon Management/National Energy Technology Laboratory models and U.S. Environmental Protection Agency emissions data. Integrating capture, transport, and storage cost estimates with policy incentives (e.g., 45Q credits) provided a total cost-per-ton analysis. Results indicate that the small scale of the volumes to be transported over the cumulative large distances does not support the project’s financial viability. However, the groundwork laid during this project from geological, regulatory, and social perspectives positions the Roughrider hub site as a promising candidate for commercial carbon storage in North Dakota, especially if the economy of scale is introduced for CO 2 transportation to the hub site.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Smart Proxy Modeling of SACROC CO2-EOR

Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.

Vida, Gholami↗
DOE OSTI2020

Shallow Geothermal Potential of the Snake River Plain

The Snake River Plain (SRP) terrestrial heat flow and subsurface thermal regime are not well understood but are important for assessing the local geothermal resource potential, both for conventional and for Enhanced Geothermal Systems (EGS) development in the region. Resource evaluation for the SRP is complicated by the disparate data density, along with the known lateral advection of heat in the Eastern Snake River Plain Aquifer and vertical heat transport by fluids in the bounding faults, primarily in the southwestern section. Fortunately, recent studies, e.g., the Snake River Plain Play Fairway Analysis, the Idaho FORGE site, and site-specific investigations, which included drilling within the Camas Prairie and on the Mountain Home Air Force Base, near Twin Falls, and in the Eastern Snake River Plain as part of the HOTSPOT Project, add both additional drilling and geophysical data. The SMU Geothermal Laboratory has conducted detailed studies of SRP tectonics and heat flow since the 1970's and used this knowledge as part of the EGS geothermal potential estimation for the conterminous United States in 2006 and again in 2011, calculating geothermal potential from 3.5 km to 10 km depth. Recent temperature modeling refined the calculation methodology to estimate shallow (1 km to 4 km) resource potential using an improved thermal conductivity model and incorporation of shallow groundwater flow. By incorporating the new SRP geology, geophysics, and 206 thermal data sites into the SMU thermal modeling methodology, this project updates the resource estimate for the SRP, and generates new temperature-at-depth maps for the shallow subsurface (1 km to 4 km). The project results highlight the EGS potential resource areas (=150°C) and areas with more exploration risks based on minimal and/or low-quality data. The newest temperature modeling results suggest EGS potential is near five times greater in the SRP than previously estimated.

EGS↗
DOE OSTI2019

Identification and mapping of riverbed sediment facies in the Columbia River through integration of field observations and numerical simulations

In the Hanford Reach of the Columbia River, a thin layer of recent alluvium overlies the sedimentary formations that comprise the unconfined groundwater aquifer. Experimental and modeling studies have demonstrated that this alluvial layer exerts significant control on the exchange of groundwater and surface water (hydrologic exchange flux), and is associated with elevated levels of biogeochemical activity. This layer is also observed to be strongly heterogeneous, and quantifying the spatial distribution of properties over the range of scales of interest is challenging. Facies are elements of a sediment classification scheme that groups complex geologic materials into a set of discrete classes according to distinguishing features. Facies classifications have been used as a framework for assigning heterogeneous material properties to grid cells of numerical models of flow and reactive transport in subsurface media. The usefulness of such an approach hinges on being able to relate facies to quantitative properties needed for flow and reactive transport modeling, and on being able to map facies over the domain of interest using readily available information. While aquifer facies have been used in various modeling contexts, application of this concept to riverbed sediments is relatively new. Here we describe an approach for categorizing and mapping recent alluvial (riverbed) sediments based on the integration of diverse observations with numerical simulations of river hydrodynamics. The facies have distinct distributions of sediment texture which correspond to variations in hydraulic properties, and therefore provide a useful framework for assigning heterogeneous properties in numerical simulations of hydrologic exchange flows and biogeochemical processes.

Hou, Zhangshuan↗
DOE OSTI2019

Appalachian Basin Temperature-Depth Maps and Structured Data in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy

This dataset contains shapefiles and rasters that summarize the results of a stochastic analysis of temperatures at depth in the Appalachian Basin states of New York, Pennsylvania, and West Virginia. This analysis provides an update to the temperature-at-depth maps provided in the Geothermal Play Fairway Analysis of the Appalachian Basin (GPFA-AB) Thermal Quality Analysis (GDR repository 879: https://gdr.openei.org/submissions/879). This dataset improves upon the GPFA-AB dataset by considering several additional uncertainties in the temperature-at-depth calculations, including geologic properties and thermal properties. A Monte Carlo analysis of these uncertain properties and the GPFA-AB estimated surface heat flow was used to predict temperatures at depth using a 1-D heat conduction model. In this data submission, temperatures are provided for depths from 1-5 km in 0.5 km increments. The mean, standard deviation, and selected quantiles of temperatures at these depths are provided as shapefiles with attribute tables that contain the data. Rasters are provided for the mean and standard deviation data. Figures and maps that summarize the data are also provided. For the pixel corresponding to Cornell University, Ithaca, NY, a .csv file containing the 10,000 temperature-depth profiles estimated from the Monte Carlo analysis is provided. These data are summarized in a figure containing violin plots that illustrate the probability of obtaining certain temperatures at depths below Cornell.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Play Fairway Analysis: Structurally Controlled Geothermal Systems in the Eastern Great Basin Extensional Regime, Utah

A research team with membership from the University of Utah/Energy & Geoscience Institute, the University of Utah/Dept. of Geology & Geophysics, and the Utah Geological Survey, undertook a play fairway analysis (PFA) for geothermal resources in the Eastern Great Basin (EGB) extensional tectonic regime of western Utah. This is a high-priority region for geothermal exploration because active Basin and Range (B&R) extension with volcanism having a N-S strike is superimposed upon pre-existing E-W belts of plutonic rocks and large-scale structural lineaments. Cumulative heat flow along the N-S strike of the state totals approximately 5 GWt above background stable interior. Three electricity producing power plants currently exist with substantial potential for increase. Succinctly, our PFA approach aims to resolve potential sources of heat and permeability in the region, which are the two principal criteria for establishing a geothermal resource. An initial Phase 1 was carried out using only existing geoscientific data in the area. Criteria selected for focusing heat potential include direct heat flow measurements in boreholes, magnetotelluric (MT) low resistivity anomalies, fluid/gas geochemistry, and proximity to recent volcanic eruptions. Permeability is established through geological structures (fault density, critically stressed areas, seismicity, gravity), and MT low resistivity anomalies. In Phase II of this PFA project, additional geological, geophysical and geochemical data were acquired and analysis carried out primarily over promising composite common risk (CCR) areas initially identified in Phase I in order to focus prospectivity and prepare for drilling recommendations. These prospects are near the Twin Peaks rhyolite field, high heat flow areas north of the producing Cove Fort system, and geophysical structure beneath the Crater Knoll area off the northeast flank of the Mineral Mountains. Additional data included MT site fill-in, structural mapping and analysis using high-resolution imagery, gravity and on-ground mapping, Nodal 3C passive seismic collection, and passive 3He surveying. Heat source and permeability potential are again expressed in terms of their individual common risk segment (CRS) maps, with a color scheme using green for most favorable (low risk) and red for least favorable (high risk). Diverse data types are united through the technique of probability kriging, which establishes prospectivity thresholds for each data type and then computes probability of exceeding that threshold over the PFA area. Modified CRS and CCRS maps are compared to those of Phase I to highlight tighter prospectivity focus. In doing so, the prospectivity threshold for heat was increased significantly to narrow the targeting. In the final Phase III of this project where a recommended deep thermal gradient hole was sited, additional geophysical, geochemical and geological field collection and analysis was carried out to refine drill hole targeting. This includes prospect-scale MT, gravity, structure, passive seismic deployment (Cove Fort area), and detailed 3He isotope profiling. It was the recommendation of the DOE Technical Monitoring Team (TMT) that one or more holes be sited in the north-ern Cove Fort area where legacy TG gradient holes showed high cumulative heat flow. These were to be of moderate depth, 2000-3000 feet, to reach the geothermal fluid table expected to start in excess of 1000 feet depth. The drilling organization stipulated by the DOE/GTO was that of the USGS Research Drilling Program (RDP) centered in Las Vegas, NV. A detailed well plan, appended to this report, was developed principally by Dr. Ben Barker consulting to University of Utah, Dr. Steve Pye on the DOE TMT, Mr. Steven Crawford of the USGS-RDP, and the project PI Phil Wannamaker. However, temperature and possible H 2 S at the systems lead to cancellation of the drilling last-minute as this appeared outside the experience base of the USGS-RDP. We hope to have the opportunity to revisit the test drilling and expand the Play Fairway Analysis of this region at some point in the future.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Characterization of Rare Earth Element Minerals in Coal Utilization Byproducts

The United States currently produces over 100 million tons of coal utilization byproducts (CUB) per year in the form of fly ash, bottom ash, slag, and flue gas (American Coal Ash Association (ACCA), 2015). But this “waste material” also contains potentially useful levels of rare earth elements (REE). Rare earth elements are crucial for many existing and emerging technologies, but the U.S. lacks a domestic, sustainable REE source. Our project explored the possibility of developing a supply of REEs for U.S. technologies by extracting REEs from CUBs. This work offers the potential to reduce our dependence on other countries for supply of these critical elements (NETL, REE 2016 Project Portfolio). Geologic and diagenetic history, industrial preparation methods, and the specific combustion process all play major roles in the composition of CUB. During combustion, inorganic mineral phases of coal particles are fluidized at temperatures higher than 1400oC, so inorganic mineral materials are oxidized, fused, disintegrated, or agglomerated into larger spherical and amorphous (non-crystalline) particles. The original mineralogy of the coal-containing rock and heating/cooling of the material significantly affects the composition and morphology of the particles in the combustion byproduct (Kutchko and Kim, 2006). Thus, different types of coal/refuse/ash must be characterized to better understand mineral evolution during the combustion process. Our research focused on developing a working model to address how REE minerals behave during the combustion process: this research should help determine the most effective engineering methods for extracting REEs from CUBs. We used multimodal imaging and image processing techniques to characterize six rock and ash samples from different coal power plants with respect to morphology, grain size, presence of mineral phases, and elemental composition. The results of these characterization activities provided thresholds for realizing the occurrence of REE mineral phases in CUB and allowed us to calculate structural and volumetric estimates of REE. Collectively, the rock and coal ash samples contained minerals such as quartz, kaolinite, muscovite/illite, iron oxide (as hematite or magnetite), mullite, and clinochlore. Trace minerals included pyrite, zircon, siderite, rutile, diopside, foresterite, gypsum, and barite. We identified REE phosphate minerals monazite (Ce,La,Nd,Th)(PO 4 ,SiO 4 ), xenotime (YPO 4 ,SiO 4 ), and apatite (Ca 5 (PO 4 ) 3 (F,Cl,OH) via SEM and electron microprobe analysis: these materials generally occurred as 1-10 μm-long crystals in the rock and ash samples. As has been shown in other studies, amorphous material-aluminosilicate glass or iron oxyhydroxide-are the major components of coal fly and bottom ash. Trace amounts of amorphous calcium oxide and mixed element (e.g., Al-Si-Ca-Fe) slag are also present. Quartz, mullite, hematite, and magnetite are the crystalline phases present. We found that REEs are present as monomineralic grains dispersed within the ash, as well as fused to or encapsulated by amorphous aluminosilicate glass particles. Monazite and xenotime have relatively high melting points (>1800 °C) compared to typical combustion temperatures; our observations indicate that the REE-phosphates, which presumably contribute a large percentage of REE to the bulk ash REE pool, as measured by mass spectroscopy, are largely unaltered by the combustion. Our study shows that conventional coal combustion processes sequester REE minerals into aluminosilicate glass phases, which presents a new engineering challenge for extracting REE from coal ash. The characterization work summarized in this report provides a semi-quantitative assessments of REE in coal-containing rock and CUB. The data we obtained from 2- and 3-D imaging, elemental mapping, volumetric estimates, and advanced high-resolution pixel classification successfully identified the different mineral phases present in CUB. Further, our characterization results can guide techniques for extracting REEs from CUB, or other geologic and engineered materials. Whilst, interpretations will inform future REE separation and extraction techniques and technologies practical for commercial utilization of combustion byproducts generated by power plants.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

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↗