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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

West Flank Coso FORGE: Seismic Reflection

PDFs of seismic reflection profiles 101,110, 111 local to the West Flank FORGE site. 45 line kilometers of seismic reflection data are processed data collected in 2001 through the use of vibroseis trucks. The initial analysis and interpretation of these data was performed by Unruh et al. (2001). Optim processed these data by inverting the P-wave first arrivals to create a 2-D velocity structure. Kirchhoff images were then created for each line using velocity tomograms (Unruh et al., 2001).

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

West Flank Coso FORGE: Natural Fracture Data

Natural fracture data from wells 33-7, 33A-7,52A-7, 52B-7 and 83-11 at West Flank. Fracture orientations were determined from image logs of these wells (see accompanying submissions). Data files contain depth, apparent (in wellbore reference frame) and true (in geographic reference frame) azimuth and dip, respectively.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Frontier Observatory for Research in Geothermal Energy: Phase 1 Topical Report West Flank of Coso, CA

The Department of Energy (DOE) Frontier Observatory for Research in Geothermal Energy (FORGE) is to be a dedicated site where the subsurface scientific and engineering community can develop, test, and improve technologies and techniques for the creation of cost-effective and sustainable enhanced geothermal systems (EGS) in a controlled, ideal environment. The establishment of FORGE will facilitate development of an understanding of the key mechanisms controlling a successful EGS. Execution of FORGE is occurring in three phases with five distinct sub-phases (1, 2A, 2B, 2C, and 3). This report focuses on Phase 1 activities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Creation of an Enhanced Geothermal System through Hydraulic and Thermal Stimulation

This report describes a 10-year DOE-funded project to design, characterize and create an Engineered Geothermal System (EGS) through a combination of hydraulic, thermal and chemical stimulation techniques. Volume 1 describes a four-year Phase 1 campaign, which focused on the east compartment of the Coso geothermal field. It includes a description of the geomechanical, geophysical, hydraulic, and geochemical studies that were conducted to characterize the reservoir in anticipation of the hydraulic stimulation experiment. Phase 1 ended prematurely when the drill bit intersected a very permeable fault zone during the redrilling of target stimulation well 34-9RD2. A hydraulic stimulation was inadvertently achieved, however, since the flow of drill mud from the well into the formation created an earthquake swarm near the wellbore that was recorded, located, analyzed and interpreted by project seismologists. Upon completion of Phase 1, the project shifted focus to a new target well, which was located within the southwest compartment of the Coso geothermal field. Volume 2 describes the Phase 2 studies on the geomechanical, geophysical, hydraulic, and geochemical aspects of the reservoir in and around target-stimulation well 46A-19RD, which is the deepest and hottest well ever drilled at Coso. Its total measured depth exceeding 12,000 ft. It spite of its great depth, this well is largely impermeable below a depth of about 9,000 ft, thus providing an excellent target for stimulation. In order to prepare 46A-19RD for stimulation, however, it was necessary to pull the slotted liner. This proved to be unachievable under the budget allocated by the Coso Operating Company partners, and this aspect of the project was abandoned, ending the program at Coso. The program then shifted to the EGS project at Desert Peak, which had a goal similar to the one at Coso of creating an EGS on the periphery of an existing geothermal reservoir. Volume 3 describes the activities that the Coso team contributed to the Desert Peak project, focusing largely on a geomechanical investigation of the Desert Peak reservoir, tracer testing between injectors 21-2 and 22-22 and the field's main producers, and the chemical stimulation of target well 27-15.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Creation of an Enhanced Geothermal System through Hydraulic and Thermal Stimulation

This report describes a 10-year DOE-funded project to design, characterize and create an Engineered Geothermal System (EGS) through a combination of hydraulic, thermal and chemical stimulation techniques. Volume 1 describes a four-year Phase 1 campaign, which focused on the east compartment of the Coso geothermal field. It includes a description of the geomechanical, geophysical, hydraulic, and geochemical studies that were conducted to characterize the reservoir in anticipation of the hydraulic stimulation experiment. Phase 1 ended prematurely when the drill bit intersected a very permeable fault zone during the redrilling of target stimulation well 34-9RD2. A hydraulic stimulation was inadvertently achieved, however, since the flow of drill mud from the well into the formation created an earthquake swarm near the wellbore that was recorded, located, analyzed and interpreted by project seismologists. Upon completion of Phase 1, the project shifted focus to a new target well, which was located within the southwest compartment of the Coso geothermal field. Volume 2 describes the Phase 2 studies on the geomechanical, geophysical, hydraulic, and geochemical aspects of the reservoir in and around target-stimulation well 46A-19RD, which is the deepest and hottest well ever drilled at Coso. Its total measured depth exceeding 12,000 ft. It spite of its great depth, this well is largely impermeable below a depth of about 9,000 ft, thus providing an excellent target for stimulation. In order to prepare 46A-19RD for stimulation, however, it was necessary to pull the slotted liner. This proved to be unachievable under the budget allocated by the Coso Operating Company partners, and this aspect of the project was abandoned, ending the program at Coso. The program then shifted to the EGS project at Desert Peak, which had a goal similar to the one at Coso of creating an EGS on the periphery of an existing geothermal reservoir. Volume 3 describes the activities that the Coso team contributed to the Desert Peak project, focusing largely on a geomechanical investigation of the Desert Peak reservoir, tracer testing between injectors 21-2 and 22-22 and the field's main producers, and the chemical stimulation of target well 27-15.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Controlling ZIF-67 crystals formation through various cobalt sources in aqueous solution

Zeolitic imidazolate frameworks ZIF-67 were prepared under hydrothermal (120 °C) and non-hydrothermal (room temperature) from various cobalt sources and 2-methylimidazolate (Hmim) in aqueous solution within 30 min. The particle size and morphology were found to be related to the reactivity of the cobalt salt, Hmim/Co{sup 2+} molar ratios and experimental condition. Using Co(NO{sub 3}){sub 2} as cobalt source, small-sized ZIF-67 crystals with agglomeration were formed. For CoCl{sub 2}, small-sized rhombic dodecahedron were obtained. While large-sized crystals of rhombic dodecahedron structure were obtained from CoSO{sub 4} and Co(OAc){sub 2}. Under hydrothermal condition, the size of ZIF-67 crystals tended to be more uniform and the morphology were more regular comparing to non-hydrothermal condition. This study provides a simple way to control the size and morphology of ZIF-67 crystals prepared in aqueous solution. - Graphical abstract: Zeolitic imidazolate frameworks ZIF-67 were prepared under hydrothermal (120 °C) and non-hydrothermal (room temperature) from four different cobalt sources (Co(NO{sub 3}){sub 2}, CoCl{sub 2}, CoSO{sub 4} and Co(OAc){sub 2}) in aqueous solution within 30 min. The particle size and morphology were found to be related to the reactivity of the cobalt salt, Hmim/Co{sup 2+} molar ratios and experimental condition. - Highlights: • The particle size and morphology were determined by the reactivity of cobalt salt. • ZIF-67 could be prepared from CoSO{sub 4} and Co(OAc){sub 2} at Hmim/Co{sup 2+} molar ratio of 10. • Uniform and regular particles were obtained under hydrothermal condition.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Scale Resistant Heat Exchanger for Low Temperature Geothermal Binary Cycle Power Plant

Phase 1 of the investigation of improvements to low temperature geothermal power systems was completed. The improvements considered were reduction of scaling in heat exchangers and a hermetic turbine generator (eliminating seals, seal system, gearbox, and lube oil system). A scaling test system with several experiments was designed and operated at Coso geothermal resource with brine having a high scaling potential. Several methods were investigated at the brine temperature of 235 ºF. One method, circulation of abradable balls through the brine passages, was found to substantially reduce scale deposits. The test heat exchanger was operated with brine outlet temperatures as low as 125 ºF, which enables increased heat input available to power conversion systems. For advanced low temperature cycles, such as the Variable Phase Cycle (VPC) or Kalina Cycle, the lower brine temperature will result in a 20-30% increase in power production from low temperature resources. A preliminary design of an abradable ball system (ABS) was done for the heat exchanger of the 1 megawatt VPC system at Coso resource. The ABS will be installed and demonstrated in Phase 2 of this project, increasing the power production above that possible with the present 175 ºF brine outlet limit. A hermetic turbine generator (TGH) was designed and manufacturing drawings produced. This unit will use the working fluid (R134a) to lubricate the bearings and cool the generator. The 200 kW turbine directly drives the generator, eliminating a gearbox and lube oil system. Elimination of external seals eliminates the potential of leakage of the refrigerant or hydrocarbon working fluids, resulting in environmental improvement. A similar design has been demonstrated by Energent in an ORC waste heat recovery system. The existing VPC power plant at Coso was modified to enable the “piggyback” demonstration of the TGH. The existing heat exchanger, pumps, and condenser will be operated to provide the required process conditions for the TGH demonstration. Operation of the TGH with and without the ABS system will demonstrate an increase in geothermal resource productivity for the VPC from 1 MW/(million lb) of brine to 1.75 MW/(million lb) of brine, a 75% increase.

15 GEOTHERMAL ENERGY↗
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↗