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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Quantifying Community Assembly Processes and Identifying Features that Impose Them

Across a set of ecological communities connected to each other through organismal dispersal (a ‘meta-community’), turnover in composition is governed by (ecological) Drift, Selection, and Dispersal Limitation. Quantitative estimates of these processes remain elusive, but would represent a common currency needed to unify community ecology. Using a novel analytical framework we quantitatively estimate the relative influences of Drift, Selection, and Dispersal Limitation on subsurface, sediment-associated microbial meta-communities. The communities we study are distributed across two geologic formations encompassing ~12,500m3 of uranium-contaminated sediments within the Hanford Site in eastern Washington State. We find that Drift consistently governs ~25% of spatial turnover in community composition; Selection dominates (governing ~60% of turnover) across spatially-structured habitats associated with fine-grained, low permeability sediments; and Dispersal Limitation is most influential (governing ~40% of turnover) across spatially-unstructured habitats associated with coarse-grained, highly-permeable sediments. Quantitative influences of Selection and Dispersal Limitation may therefore be predictable from knowledge of environmental structure. To develop a system-level conceptual model we extend our analytical framework to compare process estimates across formations, characterize measured and unmeasured environmental variables that impose Selection, and identify abiotic features that limit dispersal. Insights gained here suggest that community ecology can benefit from a shift in perspective; the quantitative approach developed here goes beyond the ‘niche vs. neutral’ dichotomy by moving towards a style of natural history in which estimates of Selection, Dispersal Limitation and Drift can be described, mapped and compared across ecological systems.

ecological drift↗
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Physical, Hydraulic, and Transport Properties of Sediments and Engineered Materials Associated with Hanford Immobilized Low-Activity Waste

Current plans for treatment and disposal of immobilized low-activity waste (ILAW) from Hanford’s underground waste storage tanks include vitrification and storage of the glass waste form in a nearsurface disposal facility. This Integrated Disposal Facility (IDF) is located in the 200 East Area of the Hanford Central Plateau. Performance assessment (PA) of the IDF requires numerical modeling of subsurface flow and reactive transport processes over very long periods (thousands of years). The models used to predict facility performance require parameters describing various physical, hydraulic, and transport properties. This report provides updated estimates of physical, hydraulic, and transport properties and parameters for both near- and far-field materials, intended for use in future IDF PA modeling efforts. Previous work on physical and hydraulic property characterization for earlier IDF PA analyses is reviewed and summarized. For near-field materials, portions of this document and parameter estimates are taken from an earlier data package. For far-field materials, a critical review is provided of methodologies used in previous data packages. Alternative methods are described and associated parameters are provided.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Selected Area Manipulation of MoS 2 via Focused Electron Beam-Induced Etching for Nanoscale Device Editing

Here we demonstrate direct-write patterning of single and multilayer MoS 2 via a focused electron beam-induced etching (FEBIE) process mediated with the XeF 2 precursor. MoS 2 etching is performed at various currents, areal doses, on different substrates, and characterized using scanning electron and atomic force microscopies as well as Raman and photoluminescence spectroscopies. Scanning transmission electron microscopy reveals a sub-40 nm etching resolution and the progression of point defects and lateral etching of the consequent unsaturated bonds. The results confirm that the electron beam-induced etching process is minimally invasive to the underlying material in comparison to ion beam techniques, which damage the subsurface material. Single-layer MoS 2 field-effect transistors are fabricated, and device characteristics are compared for channels that are edited via the selected area etching process. The source–drain current at constant gate and source–drain voltage scale linearly with the edited channel width. Moreover, the mobility of the narrowest channel width decreases, suggesting that backscattered and secondary electrons collaterally affect the periphery of the removed area. Focused electron beam doses on single-layer transistors below the etching threshold were also explored as a means to modify/thin the channel layer. The FEBIE exposures showed demonstrative effects via the transistor transfer characteristics, photoluminescence spectroscopy, and Raman spectroscopy. While strategies to minimize backscattered and secondary electron interactions outside of the scanned regions require further investigation, here, we show that FEBIE is a viable approach for selective nanoscale editing of MoS 2 devices.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes

Increasing evidence emerged for non-random spatial distributions for microbes, but the underlying processes resulting in microbial assemblage variation among and within Earth’s ecosystems is still lacking. For instance, some studies showed that the deterministic processes by habitat specialization are important, while other studies hold that bacterial communities are assembled by neutral forces. Here we examine the relative importance of deterministic and stochastic processes for bacterial communities from subsurface environments, as well as stream biofilm, lake water, lake sediment and soil using pyrosequencing of 16S rRNA gene. We show that there is a general pattern in phylogenetic signal in species niches across recent evolutionary time for all studied habitats, enabling us to infer the influences of community assembly processes from patterns of phylogenetic turnover in community composition. The phylogenetic dissimilarities among habitat types were significantly higher than within them, and the communities were clustered according to their original habitat types. For communities within habitat types, the highest phylogenetic turnover rate through space was observed in subsurface environments, followed by stream biofilm on mountainsides, whereas the sediment assemblages across regional scales showed the lowest turnover rate. Quantifying phylogenetic turnover as the deviation from a null expectation suggested that measured environmental variables imposed strong selection on bacterial communities for nearly all sample groups, and for three sample groups, that spatial distance reflects unmeasured environmental variables that impose selection, as opposed to spatial isolation. Such characterization of spatial and environmental variables proved essential for proper interpretation of partial mantel results based on observed beta diversity metrics. In summary, our results clearly indicate a dominant role of deterministic processes on bacterial assemblages and highlight that bacteria show strong habitat associations that have likely emerged through evolutionary adaptation.

Bacteria↗
DOE OSTI2018

Electronic structure and surface properties of MgB 2 (0001) upon oxygen adsorption [Electronic structure and surface properties of MgB2: oxygen adsorption on MgB2(0001)]

Here, we use density-functional theory to investigate the bulk and surface properties of MgB 2 . The unique bonding structure of MgB 2 is investigated by Bader's atoms-in-molecules, charge density difference, and occupancy projected band structure analyses. Oxygen adsorption on the charge-depleted surfaces of MgB 2 is studied by a surface potential energy mapping method, reporting a complete map including low-symmetry binding sites. The B-terminated MgB 2 (0001) demonstrates reconstruction of the graphenelike B layer, and the reconstructed geometry exposes a threefold site of the subsurface Mg, making it accessible from the surface. Detailed reconstruction mechanisms are studied by simulated annealing method based on ab initio molecular dynamics and nudged elastic band calculations. The surface clustering of B atoms significantly modifies the B 2p states to occupy low energy valence states. The present paper emphasizes that a thorough understanding of the surface phase may explain an apparent inconsistency in the experimental surface characterization of MgB 2 . Furthermore, these results suggest that the surface passivation can be an important technical challenge when it comes to development of a superconducting device using MgB 2 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

Presentation material for a paper presented at the GHGT-17 conference, Calgary, Canada, October 20-24, 2024. The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

CO2 geologic storage↗
DOE OSTI2013

Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory - 13264

A series of site investigations and decision-support analyses have been performed related to a chromium plume in the regional aquifer beneath the Los Alamos National Laboratory (LANL). Based on the collected data and site information, alternative conceptual and numerical models representing governing subsurface processes with different complexity and resolution have been developed. The current conceptual model is supported by multiple lines of evidence based on comprehensive analyses of the available data and modeling results. The model is applied for decision-support analyses related to estimation of contaminant- arrival locations and chromium mass flux reaching the regional aquifer, and to optimization of a site monitoring-well network. Plume characterization is a challenging and non-unique problem because multiple models and contamination scenarios are consistent with the site data and conceptual knowledge. To solve this complex problem, an advanced methodology based on model calibration and uncertainty quantification has been developed within the computational framework MADS (http://mads.lanl.gov). This work implements high-performance computing and novel, efficient and robust model analysis techniques for optimization and uncertainty quantification (ABAGUS, Squads, multi-try (multi-start) techniques), which allow for solving problems with large degrees of freedom. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

Siriwardane, Hema↗
DOE OSTI2013

Updated Subsurface Data Base For Bear Creek Valley, Chestnut Ridge, And Parts Of Bethel Valley On The U.S. Department of Energy Oak Ridge Reservation

This document represents a compilation of location, construction, and hydrologic information relating to boreholes, groundwater monitoring wells, and surface water locations that have been installed/established at the Y-12 National Security Complex (Y-12) through August 2013. To date, a total of 1422 boreholes and wells have been installed in and around the Y-12 area. Of those, 835 existing boreholes and wells continue to be utilized for groundwater monitoring programs, research, remedial investigations, plume characterization and delineation studies, and various other hydrogeologic endeavors. In addition, 215 surface water locations, such as rivers, streams, seeps, springs, lakes, ponds, and building sumps are included in this database. General data about boreholes and wells included in the database are survey coordinates, survey system, elevations, alternative names and well status. Surface water location information (Appendix I) includes name, alias, functional area, northing and easting coordinates, survey system, map number and sampling history. Tabulated construction data include total depth, completion method, borehole diameter, casing and screen materials, casing and screen diameters, casing and screen depths, filter pack depths, open-hole intervals, and open-hole diameters. Hydrogeological data summarized in this document include the aquifer monitored by the completion interval, depth to weathered and fresh bedrock, formations penetrated, well sampling history, and whether rock core and geophysical logs were obtained. This document (which is the sixth revision to Y/TS–881 and the ninth overall update of a previous compilation) is published on a regular basis by the Y-12 Groundwater Protection Program (GWPP), which serves as custodian of drilling records and well construction data for the network of wells and other groundwater monitoring stations at Y-12. The tabulations in this database are arranged in appendices of like information. An example application of the data compiled in this document would be the evaluation of the suitability of wells for continued use in groundwater investigations and monitoring studies. Other uses may include: the siting of hydrogeological characterization activities, background evaluations of sites prior to initiation of new drilling activities, and hydrogeological review of selected sites prior to the initiation of remedial actions or new construction activity. Existing boreholes, wells, and surface water locations are presented on a series of maps (Appendix K). Boreholes and wells that have been plugged and abandoned, destroyed, could not be located, or otherwise are of unknown status/condition appear on a second series of maps of identical coverage (Appendix L).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Devising a framework to Couple Electromagnetic Geophysical Measurements with Reservoir Simulations to Monitor CO2 Plume Movement

Monitoring CO2 plume movement and extent is critical to any CO2 utilization and storage (CCUS) project but can present significant challenges. Downhole measurements (such as cross-well seismic) can give the resolution needed to track the CO2 plume. That resolution comes at a significant expense in equipment and lost production time, and the resolution is only in the vicinity of that well. Surface seismic is less expensive but has decreasing resolution with depth and may not be sensitive to changes in the CO2 plume. Conversely, electrical conductivity has been shown to be correlated with changes in phase saturation associated with CCUS operations. A novel approach for subsurface monitoring that can be applied with minimal expense and operation disruption is charged wellbore casing controlled-source electromagnetics (CWC-CSEM). This technique injects electrical current into two legacy wells. Surface measurements of the induced electric and magnetic fields are obtained with distributed data loggers. Time-lapse measurements are sensitive to changes in CO2 plume movement, but inversion requires the application of prior knowledge from prior characterization efforts. We propose a methodology where simulations are used to inform CWC-CSEM inversion while simultaneously being validated. As such, uncertainty is reduced, and more robust flow models can be devised. A three-year study is underway at the Bell Creek Field in southeast Montana (DOE-NETL Project DE-FE0028320) to evaluate the effectiveness of CWC-CSEM on an active CO2-EOR operation. Determining the relationship between electrical resistivity and phase saturations, and coupling that to a reservoir model is the thrust of this project. Initially, the relative permeability/capillary pressure relationship and the compositional oil model were identified as model parameters with high uncertainty. To elucidate the impact of these uncertain parameters, simulations were run for ten years with a single five-spot pattern. Final water saturation distributions were converted into resistivity using Archie’s Law and available fluid sampling data to provide an exploration of the time lapse electromagnetic anomaly which can be anticipated using CWC-CSEM methods. This technique has promise as an inexpensive and accurate way to monitor CO2 injection in a CCUS site.

Moodie, Nathan↗
DOE OSTI2018

Nebraska Integrated Carbon Capture and Storage Pre-Feasibility Study

In collaboration with the Nebraska Public Power District (NPPD), the Energy & Environmental Research Center (EERC) has conducted a pre-feasibility study for a commercial-scale carbon dioxide (CO 2 ) geologic storage complex in western Nebraska integrated with potential CO 2 capture at Gerald Gentleman Station (GGS). GGS is the largest coal-fired electricity-generating station in Nebraska, emitting 8.5 million metric tons (Mt) of CO 2 annually, and is located near the town of Sutherland. This pre-feasibility (Phase 1) project has been executed as part of the U.S. Department of Energy (DOE) CarbonSAFE program, a multiphase initiative to support the deployment of large-scale carbon capture and storage (CCS) projects. Each CarbonSAFE project is required to demonstrate the potential to capture and store at least 50 million tonnes (Mt) of CO 2 over a 25-year operational period. The EERC and NPPD established a coordination team to identify challenges to a potential Nebraska CCS project, comprising local stakeholder organizations, which met twice in Lincoln and via several Webinars, providing feedback and guidance throughout the pre-feasibility study. The EERC also secured the technical support of Schlumberger Carbon Services and Computer Modelling Group Ltd. (CMG). The project comprised four technical themes, all using published information sources. Regional and stakeholder analysis, including identification of sensitive environmental areas, potential resource conflicts, and strategies for public outreach. A review of geographic and socioeconomic characteristics, in combination with geologic characterization in the subbasinal analysis described below, identified an area to the southwest and within a 75-mile radius of GGS as the most prospective for development of a storage site. This area largely avoided lands with protected status such as wetlands. A public outreach plan has also been developed for implementation in any further phases of CCS assessment in western Nebraska, for example a CarbonSAFE Phase 2 feasibility study. Scenario analysis, addressing economic and regulatory factors. GGS is the only single major source of CO 2 emissions capable of satisfying the CarbonSAFE 50-Mt scale requirement within the study region. Chemical absorption using amines was identified as the most viable technology for postcombustion CO 2 capture at GGS. The total cost of a CCS project at GGS was estimated to be between $67/tonne CO 2 for capture and auxiliary boiler to minimize parasitic load and $70/tonne CO 2 avoided cost, using the Carnegie-Mellon University Integrated Environmental Control Model (IECM). The total avoided cost included the capture facility and parasitic load, a flue gas desulfurization plant required for the use of amine solvent technology, transport via pipeline, and dedicated storage infrastructure. Nebraska has no legislation in place to address typical CCS-specific issues, for example pore space ownership for storage. Long-term liability, therefore, falls under the U.S. Environmental Protection Agency Underground Injection Control (UIC) Class 6 program regulations. Subbasinal analysis, addressing the potential for a dedicated subsurface “container” to store the required 50-Mt quantity of CO 2 . Modeling and simulation studies identified an area to the southwest of GGS with the potential for storage of 50 Mt CO 2 in the Cloverly Formation, comprising sandstones with interbedded and intermingled shales. The area of review (AOR) that would be required for monitoring under a Class VI operating permit was estimated to be as high as 400–700 square miles, due to uncertain pressure effects. The viability of this storage option is subject to significant uncertainty due to the relatively limited amount of existing characterization data available to the pre-feasibility study; for example, dynamic simulation indicated that the proposed storage rate might require as little as two or as many as 14 injection wells. A key uncertainty is the relative proportion and distribution of sandstone and shale within the Cloverly Formation. A preliminary, semiquantitative risk assessment also suggested uncertainty over storage capacity and injectivity constitute the most significant project risks at this pre-feasibility stage. No assessed risks were considered to rule out the possibility of a project moving to deployment. National Risk Assessment Partnership (NRAP) validation, using software tools developed by the National Energy Technology Laboratory (NETL) to assess risks associated with the potential 50-Mt CO 2 storage complex. NRAP tools were used to assess hypothetical leakage scenarios. Results broadly supported the conclusion of the semiquantitative risk analysis – for example, even worst-case analysis of theoretical leakage scenarios found limited migration rates and impacts. In summary, the work undertaken in this Phase 1 pre-feasibility study has shown that western Nebraska has potential to host a commercial-scale CCS project, including a dedicated storage “container” for 50 Mt of CO 2 . However, the following key challenges would need to be overcome: The business case for deploying CCS projects is uncertain; recently announced federal tax credits and sales of CO 2 for enhanced oil recovery may not cover the full costs of a CCS project at GGS, as estimated by this pre-feasibility study. The potential 50-Mt CO 2 dedicated storage container defined in this pre-feasibility study should be regarded as having a relatively low level of readiness to support a CCS project. Public outreach would be a vital element in western Nebraska, where sensitivities around such environmental issues as water resource protection and pipeline construction would need to be carefully addressed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Real-time tracking and analysis of gas bubble dynamics in laser powder bed fusion using in-situ X-ray characterization and machine learning

Porosity defects remain a significant challenge in the laser powder bed fusion (LPBF) process, adversely affecting the mechanical properties and reliability of additively manufactured components. Here, this study investigates the real-time formation and trajectory of gas bubbles during LPBF of Al6061 alloy using advanced in-situ X-ray characterization and machine learning. The unsupervised Gaussian mixture model and particle tracking algorithm developed are able to precisely track and quantify the properties of gas bubbles and keyhole pores. Our analysis identified five distinct types of gas bubble formation and movement patterns, emphasizing the diverse origins and behaviors of these defects. It enables precise quantification of trajectories, velocities, and morphological changes of gas bubbles, offering a granular view of the subsurface dynamics within the melt pool. Additionally, we explored keyhole-induced pore dynamics, revealing the critical role of keyhole oscillation and collapse for the formation of both large and small gas pores. It defines four different regions of gas bubble movement within the melt pool, providing a clearer understanding of how local fluid dynamics affect pore behavior. The results underscore the importance of integrating in-situ experimental observation and automated machine learning to develop a more robust predictive model for defect formation in LPBF.

In-situ X-ray imaging↗
DOE OSTI2012

Updated Conceptual Model for the 300 Area Uranium Groundwater Plume

The 300 Area uranium groundwater plume in the 300-FF-5 Operable Unit is residual from past discharge of nuclear fuel fabrication wastes to a number of liquid (and solid) disposal sites. The source zones in the disposal sites were remediated by excavation and backfilled to grade, but sorbed uranium remains in deeper, unexcavated vadose zone sediments. In spite of source term removal, the groundwater plume has shown remarkable persistence, with concentrations exceeding the drinking water standard over an area of approximately 1 km2. The plume resides within a coupled vadose zone, groundwater, river zone system of immense complexity and scale. Interactions between geologic structure, the hydrologic system driven by the Columbia River, groundwater-river exchange points, and the geochemistry of uranium contribute to persistence of the plume. The U.S. Department of Energy (DOE) recently completed a Remedial Investigation/Feasibility Study (RI/FS) to document characterization of the 300 Area uranium plume and plan for beginning to implement proposed remedial actions. As part of the RI/FS document, a conceptual model was developed that integrates knowledge of the hydrogeologic and geochemical properties of the 300 Area and controlling processes to yield an understanding of how the system behaves and the variables that control it. Recent results from the Hanford Integrated Field Research Challenge site and the Subsurface Biogeochemistry Scientific Focus Area Project funded by the DOE Office of Science were used to update the conceptual model and provide an assessment of key factors controlling plume persistence.

300 Area↗
DOE OSTI2020

Microseismic Event Denoising: Removal of Borehole Waves

The vertical borehole array at Farnsworth Unit, TX is used to monitor microseismic activity in the subsurface around the Carbon Capture and Sequestration (CCS) reservoir. The array consists of 16 3-component seismometers spaced vertically in a single borehole. Tube or borehole waves traveling up or down the borehole can corrupt signals of interest, such as microseismic events. A denoising convolutional neural network (DCNN) was trained to remove borehole waves from seismic waveforms of microseismic events for the purpose of reducing unwanted signal detections and better characterizing events of interest. This R&D leverages the work of Sandia colleague Rigo Tibi, who used a DCNN developed by Greg Beroza at Stanford University to improve the signal-to-noise ratio (SNR) of teleseismic events detected by the International Monitoring System.

58 GEOSCIENCES↗
DOE OSTI2021

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗
DOE OSTI2018

Utah FORGE: Well Data for Student Competition

Well 58-32 (previously labeled MU-ESW1) was drilled near Milford Utah during Phase 2B of the FORGE Project to confirm geothermal reservoir characteristics met requirements for the final FORGE site. Well Accord-1 was drilled decades ago for geothermal exploration purposes. While the conditions encountered in the well were not suitable for developing a conventional hydrothermal system, the information obtained suggested the region may be suitable for an enhanced geothermal system. Geophysical well logs were collected in both wells to obtain useful information regarding there nature of the subsurface materials. For the recent testing of 58-32, the Utah FORGE Project contracted with the well services company Schlumberger to collect the well logs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Riverbed Hydrologic Exchange Dynamics in a Large Regulated River Reach

Hydrologic exchange flux (HEF) is an important hydrologic component in river corridors that includes both bidirectional (hyporheic) and unidirectional (gaining/losing) surface water – groundwater exchanges. Quantifying HEF rates in a large regulated river is difficult due to the large spatial domains, complexity of geomorphologic features and subsurface properties, and the great stage variations created by dam operations at multiple time scales. In this study, we developed a method that combined numerical modeling and field measurements for estimating HEF rates across the river bed in a 7-km long reach of the highly regulated Columbia River. A high-resolution computational fluid dynamics (CFD) modeling framework was developed and validated by field measurements and other modeling results to characterize the HEF dynamics across the river bed. We found that about 85% of the time from 2008-2014 the river was losing water with an annual average net HEF rates across the river bed (Qz) of -2.3 m 3 s -1 (negative indicating downwelling). June was the only month that the river gained water, with monthly averaged Qz of 0.8 m 3 s -1 . We also found that the daily dam operations increased the hourly gross gaining and losing rate over an average year of 8% and 2%, respectively. By investigating the HEF feedbacks at various time scales, we suggest that the dam operations could reduce the HEF at seasonal time scale by decreasing the seasonal flow variations, while also enhance the HEF at sub-daily time scale by generating high frequency discharge variations. These changes could generate significant impacts on biogeochemical processes in the hyporheic zone.

54 ENVIRONMENTAL SCIENCES↗