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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Coso Geothermal Spectral Library for Rocks and Minerals

An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and pertinent information obtained from Coso geothermal field (Coso) in California. The ASD FieldSpec portable spectrometer was utilized for collecting spectral data, which was subsequently analyzed using the THOR Material Identification tool in ENVI, The Spectral Geologist (TSG) software by CSIRO, and the Fully Constrained Linear Spectral Unmixing algorithm (FCLSU) in MATLAB. Scanning Electron Microscopy (SEM) with a mineralogy-analyzing function was employed to assess the mineral composition of samples, ensuring precise mineralogical analysis. A portable X-ray fluorescence (pXRF) spectrometer was also utilized to gather information on elemental enrichment. A framework for developing spectra data and establishing spectral libraries for various geological cases was proposed within this study. The characteristic spectra of six alteration minerals - alunite, chalcedony, epidote, hematite, kaolinite, and opal - were acquired from Coso samples. The spectral library for the Coso alteration minerals was introduced for further application in academic study or industrial exploration. To browse the Coso Geothermal Spectral data and related figures from spreadsheets: #1 Unzip and store the following items in the same folder. 'Contact Probe Data.zip', 'Sample Photos.zip', and 'Coso spectra of higher-certainity minerals.xlsx'. #2 Open 'Coso spectra of higher-certainity minerals.xlsx'. The hyperlinks in the spreadsheet lead to the folders or figures of: spectra .asd file, spectra ASC II file, spectra plots, and sample photos. The spectra data is raw data without splice correction. Spectra .asd files require particular software to open. (These cannot be opened in GIS software such as ArcGIS.) Spectra ASC II files can be opened in a text editor or spread sheet program.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Well-Controlled Hydro-thermo-chemical Stimulation of a Coso Geothermal Well to Enhance Productivity

The goal of this project was to demonstrate controlled fracturing and permeability increase to enhance resource production and power generation at Coso. It was planned to stimulate at least two intervals in the Well 73-18RD with zonal isolation using advanced high-temperature packer technology. The anticipation was that a hydraulic fracture would form in one zone and in the second zone natural fractures would reopen, shear and extend, thereby increasing fluid communication in the field. In both cases, the intent was to carefully design the stimulation to take advantage of the relative roles of hydraulic pressure, thermal stress and chemical (“hydro-thermo-chemical”) dissolution. The project had planned to use the Welltec Annular Barrier (WAB) system developed as part of another DOE-funded project which was on schedule to be ready for field deployment. If successful, the Coso Operating Company would incorporate this approach into their ongoing well maintenance in other wells increasing the total MWs produced. The project had multiple tasks and the objective of Task 1 was to develop a workover plan, to build a geologic model, to design a stimulation program, and to work with the operating and services companies to develop a detailed budget and obtain the necessary permits and sundry notices. In Phase 2 the workover plan would be implemented, the designed stimulation would be carried out, and subsequently the well would logged and tested to measure the impact of stimulation. Per sponsor request, a Pre-Task 1 activity with a Go/No Go decision point was scheduled in Task 1.

02 PETROLEUM↗
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 OSTI2023

DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

DEEPEN Data Catalog for Magmatic Geothermal Systems in the United States

This data catalog contains information related to the Training Site Analysis for the Geothermica project "DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN)." The DEEPEN project aims to reduce exploration risk for geothermal fluids in magmatic systems by developing improved an improved framework for interpretation of exploration data using the Play Fairway Analysis (PFA) methodology. The Training Site Analysis performed for DEEPEN leverages existing datasets to develop a customized PFA approach to exploration for multiple geothermal resource types in magmatic systems (conventional hydrothermal resources, supercritical fluid and superheated steam resources, and superhot EGS resources). This data catalog contains links to publicly available data files related to 8 training sites in the United States. US training sites are: the Cascades/Aleutians PFA project; the Hawaii PFA project, the Oregon Cascades PFA project, the Snake River Plain, Idaho PFA project, the Washington State PFA project, Newberry Volcano, Coso Geothermal Field, and the Geysers Geothermal field. This database contains an overview of these training sites, data sources, and links to publicly available exploration datasets. For the five PFA projects, details on exploration data related to PFA components (heat, fluid, permeability, sometimes seal) are provided, including a summary of data weighting methodologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

58 GEOSCIENCES↗
DOE OSTI2021

Regional seismic velocity changes following the 2019 M w 7.1 Ridgecrest, California earthquake from autocorrelations and P / S converted waves

SUMMARY We examine regional transient changes of seismic velocities generated by the 2019 Mw 7.1 Ridgecrest earthquake in California, using autocorrelations of moving time windows in continuous waveforms recorded at regional stations. We focus on traveltime differences in a prominent phase generated by an interface around 2 km depth, associated with transmitted Pp waves and converted Ps waves from the ongoing microseismicity. Synthetic tests demonstrate the feasibility of the method for monitoring seismic velocity changes. Taking advantage of the numerous aftershocks in the early period following the main shock, we obtain a temporal resolution of velocity changes up to 20 min in the early post-main-shock period. The results reveal regional coseismic velocity drops in the top 1–3 km with an average value of ∼2 per cent over distances up to 100 km from the Ridgecrest event. These average velocity drops are likely dominated by larger changes in the shallow materials and are followed by rapid recoveries on timescales of days. Around the north end of the Ridgecrest rupture and the nearby Coso geothermal region, the observed coseismic velocity drops are up to ∼8 per cent. The method allows monitoring temporal changes of seismic velocities with high temporal resolution, fast computation and precise spatial mapping of changes. The results suggest that significant temporal changes of seismic velocities of shallow materials are commonly generated on a regional scale by large events.

58 GEOSCIENCES↗
DOE OSTI2024

Data for Spatially Varying Costs of GHG Abatement with Alternative Cellulosic Feedstocks for Sustainable Aviation Fuels

Cellulosic biomass-based sustainable aviation fuels (SAFs) can be produced from various feedstocks. The breakeven price and carbon intensity of these feedstock-to-SAF pathways are likely to differ across feedstocks and across spatial locations due to differences in feedstock attributes, productivity, opportunity costs of land for feedstock production, soil carbon effects, and feedstock composition. We integrate feedstock to fuel supply chain economics and life-cycle carbon accounting using the same system boundary to quantify and compare the spatially varying greenhouse gas (GHG) intensities and costs of GHG abatement with SAFs derived from four feedstocks (switchgrass, miscanthus, energy sorghum, and corn stover) at 4 km resolution across the U.S. rainfed region. We show that the optimal feedstock for each location differs depending on whether the incentive is to lower breakeven price, carbon intensity, or cost of carbon abatement with biomass or to have high biomass production per unit land. The cost of abating GHG emissions with SAF ranges from $181 Mg−1 CO2e to more than $444 Mg−1 CO2e and is lowest with miscanthus in the Midwest, switchgrass in the south, and energy sorghum in a relatively small region in the Great Plains. While corn stover-based SAF has the lowest breakeven price per gallon, it has the highest cost of abatement due to its relatively high GHG intensity. Our findings imply that different types of policies, such as volumetric targets, tax credits, and low carbon fuel standards, will differ in the mix of feedstocks they incentivize and locations where they are produced in the U.S. rainfed region. Note: Column V in TableS7_DayCentSimulatedYield.csv should be labelled Corn Stover CoSo-NT-50% Max.

Geospatial↗
DOE OSTI2021

Exploring Whether Subsurface Fluid Production Can Minimize Triggered Seismicity in Geothermal Fields

Fluid injection and production related to energy recovery and other industrial operations alter the pressure and stress state of subsurface reservoirs which can lead to induced seismicity. Here, the primary goal is to investigate the hypothesis that the modulation of stress state in subsurface reservoirs through fluid injection/production operations can reduce the likelihood of inducing seismicity. Validation of this mitigation strategy will provide an active operational method to control induced seismicity in subsurface reservoir exploitations such as geothermal energy recovery and carbon storage. In this project, we analyze the relationship between extensive fluid production and the paucity of aftershock activity at the Coso Geothermal plant (CGP) following the 2019 Ridgecrest earthquake sequence, where high rates of aftershock triggering were expected. We developed a high-fidelity multiphase coupled thermo-hydro-mechanical (THM) model to simulate fluid injection/production activities at the CGP between 1986 and 2020. THM results of surface subsidence due to high rates of fluid production agree well with field observations from global positioning system (GPS) and interferometric synthetic aperture radar (InSAR). Subsequently, the variations of pore pressure, temperature and stress state were used to drive numerical earthquake simulations of the aftershock response to the 2019 Ridgecrest earthquakes. The earthquake simulations show that seismic quiescence may occur following the Ridgecrest event, depending on the initial stress state at the time of the mainshock and at the initiation of geothermal energy production. Seismic quiescence occurs in 20% of the cases we explored, where rates are decreased by 50% of the background rate in the two years following the mainshock. In circumstances where aftershock rates increase near the CGP following Ridgecrest, the average rate change is a factor of two larger than background rates from simulations with no operations. These findings indicate that unlike many other operations that bring faults closer to failure, operations at the CGP are acting to stabilize faults such that triggering in the current stress state is minimal.

58 GEOSCIENCES↗
DOE OSTI2024

Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.

Cavur, Mahmut↗
NASA NTRS1975

An evaluation of Skylab (EREP) remote sensing techniques applied to investigations of crustal structure

The author has identified the following significant results. Film positives (70mm) from all six S190A multispectral photographic camera stations for any one scene can be registered and analyzed in a color additive viewer. Using a multispectral viewer, S190A and B films can be projected directly onto published geologic and topographic maps at scales as large as 1:62,500 and 1:24,000 without significant loss of detail. S190A films and prints permit the detection of faults, fractures, and other linear features not visible in any other space imagery. S192 MSS imagery can be useful for rock-type discrimination studies and delineation of linear patterns and arcuate anomalies. Anomalous color reflectances and arcuate color patterns revealed mineralized zones, copper deposits, vegetation, and volcanic rocks in various locations such as Panamint Range (CA), Greenwater (Death Valley), Lava Mountains (CA), northwestern Arizona, and Coso Hot Springs (CA).

Bechtold, I. C.↗
DOE OSTI2020

GeoRePORT Case Study Examples: Reporting Using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT)

The Geothermal Research Portfolio Optimization & Reporting Technique (GeoRePORT) was developed with funding from the United States Department of Energy's Geothermal Technologies Office (GTO) to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and non-scientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and, (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g. temperature, permeability, permitting), a site's project grade and readiness are evaluated on twelve independent attributes pertaining to geological, technical, or socio-economic feasibility. In this paper, we present case studies illustrating how GeoRePORT can be used to compare geological, technical, and socio-economic attributes between geothermal systems. The consistent and objective assessment protocols used in GeoRePORT allow for comparison of project attributes across unique locations and geological settings. GeoRePORT case studies outline the geological, socio-economic and technical features of four individual geothermal sites: Coso, Chena, Dixie Valley, and White Sands Missile Range. The case studies presented herein illustrate the usefulness of GeoRePORT in evaluating project risk/return, identifying gaps in reported data, evaluating R&D impact, and gathering insights on successes/failures as applicable to future projects.

40 EE - Geothermal Technologies Office (EE-4G)↗