Search NASA⌕ Search

SEARCH · Search NASA

Results for “resource estimates”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Site Characterization of the Highest-Priority Geologic Formations for CO2 Storage in Wyoming

The project Site Characterization of the Highest-Priority Geologic Formations for CO2 Storage in Wyoming is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The Wyoming Carbon Underground Storage Project (WY-CUSP) consisted of CO2 storage site characterization and evaluation, focusing on Wyoming’s most promising CO2 storage reservoirs (the Pennsylvanian Weber/Tensleep Sandstone and Mississippian Madison Limestone) and premier CO2 storage site (Rock Springs Uplift). Results from the WY-CUSP project suggest the two reservoirs could store up to 17,000 million tons of CO2. The WY-CUSP team drilled a stratigraphic test well and acquired a 3-D seismic survey covering 25 square miles of the Rock Springs Uplift site. The team retrieved 916 feet of core from the 12,810-foot-deep well, along with a complete log suite, borehole images, fluid samples, and other data. Project partners (1) provided continuous visual documentation of the core, including grain size, mineralogy, facies distribution, and porosity; (2) performed continuous permeability and velocity scans of selected reservoir intervals; and (3) chemically analyzed the fluid samples. WY-CUSP scientists integrated seismic attributes with observations from log suites, a VSP survey, core, fluid samples, and laboratory analyses, including continuous permeability scans. From these integrations, researchers constructed 3-D spatial distribution volumes of reservoir and seal properties that represent geological heterogeneity at the targeted CO2 storage site. The WY-CUSP team used this data to perform new CO2 plume migration simulations. Baker Hughes, Inc., completed a series of small-scale, in-situ water injectivity measurements. A database was formed when observations, analyses, and experiments from the stratigraphic test well were integrated. Correlation of these data allowed petrophysical parameters to be extrapolated from the test well out into the storage domain (5x5 mile 3-D seismic survey volume). This resulted in an improved, realistic understanding of performance assessments for potential CO2 storage scenarios. The WY-CUSP team worked on (1) improving CO2 storage resource estimates, (2) establishing long-term integrity and permanence of confining layers, (3) designing a profitable strategy for pressure management, and (4) evaluating the utilization of stored CO2 at the Rock Spring Uplift. Finally, Baker Hughes developed a microseismic baseline for the test site using in-bore geophones to complete field operations.

3-D seismic↗
DOE OSTI2022

International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Nuclear Security Planning and Operations (Day 3) [Slides]

This module will provide an overview of best practices for nuclear security planning requirements to include building a nuclear security plan and a concept of operations. It will introduce best practices for nuclear security planning to include the following: (1) Develop a Nuclear Security Plan, (2) Review 10 key elements of a Concept of Operations, (3) Estimate resources for an MPE, and (4) Capture lessons learned and best practices.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Global inventory and meta-analysis of offshore geologic carbon storage efforts

Poster for presentation at the American Geophysical Union Fall Meeting 2023 detailing work conducted for the Carbon Storage Data Field Work Proposal. This poster presents an inventory and meta-analysis conducted for Carbon Storage Data Task 4, which includes a review of offshore geologic carbon storage projects worldwide including site characterization, resource estimates, and transport information.

Mark-Moser, Mackenzie K.↗
DOE OSTI2023

Tidal Resource Gaps Analysis Technical Report

The tidal resource gaps project was created to address a growing body of evidence that models underpredict tidal current speeds compared to measurements at a number of the top-ranking tidal energy sites. In response, this project compared opportunistic tidal power density measurements from 16 tidal energy hot spot sites with estimates from resource assessment data to identify discrepancies. To improve the accuracy of resource estimates from model data, updated data from eight improved model simulations were obtained. Model improvements included grid refinement, domain coupling, and the use of unstructured or nested grids. New resource estimates were calculated from the updated models, and these data were used to update the tidal hot spots-a list of promising tidal energy sites around the United States.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Resource Characterization to Estimate Potential for Electricity Co-Production at Blackburn Oil Field, Nevada

The U.S. Department of Energy estimates that an annual average of 25 billion barrels of hot water are produced from oil and gas wells within the United States. The thermal energy available in the co-produced water stream is usually discarded, as the produced waters are considered an inconvenience by the operators and are disposed of using injection wells. However, utilizing organic Rankine cycle (ORC) generators, a vast amount of thermal energy can be captured and converted into electricity (albeit at relatively low efficiency due to the low temperatures). The National Renewable Energy Laboratory (NREL), in collaboration with Transitional Energy and Grant Canyon Oil & Gas, evaluated the feasibility of geothermal co-production of electricity by utilizing existing oil wells in Blackburn oil field in Nevada. The once prolific Blackburn oil field is located in Pine Valley, approximately 45 miles east-southeast of Elko, Nevada. Currently, the wells targeting the highly fractured Devonian Nevada dolomite reservoir are operating at a water cut ratio of more than 99%, with individual fluid (oil and water) production rates reaching 7.4 L/s (4,021 BBL/day). Analysis of publicly available data showed that the combination of the suitable wells' maximum historical production rates reached 22.90 L/s. The production from these wells occurs naturally and the wells are choked (and even shut down) by the operator to mitigate excessive water production, indicating a strong reservoir recharge and future opportunity to increase the water production for geothermal electricity generation. The main goal of this study was to evaluate the productivity of the existing wells, the performance of the reservoir, the surface network, and the operational constraints in order to achieve 1 MWe of electricity production from the field's water production. Utilizing the GEOPHIRES tool, we have determined that a twofold to threefold increase in the total fluid production, compared to the historical production under artificial restraint (choke), is required to reach a 1-MWe net target output for a low-temperature ORC system with air-cooled condensers. Lower flow rates would be required when utilizing water-based condensers instead of air-cooled condensers. However, that would require a constant supply of cold water, which may be challenging given the arid environment of the project site.

Blackburn↗
DOE OSTI2023

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

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

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Rapid Initial-State Preparation for the Quantum Simulation of Strongly Correlated Molecules

Studies on quantum algorithms for ground-state energy estimation often assume perfect ground-state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here, we address that problem in two ways: by faster preparation of matrix-product-state (MPS) approximations and by more efficient filtering of the prepared state to find the ground-state energy. We show how to achieve unitary synthesis with a Toffoli complexity about 7 × lower than that in prior work and use that to derive a more efficient MPS-preparation method. For filtering, we present two different approaches: sampling and binary search. For both, we use the theory of window functions to avoid large phase errors and minimize the complexity. We find that the binary-search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003 . Finally, we estimate the total resources to perform ground-state energy estimation of Fe - S cluster systems, including the Fe Mo cofactor by estimating the overlap of different MPS initial states with potential ground states of the Fe Mo cofactor using an extrapolation procedure. With a modest MPS bond dimension of 4000 , our procedure produces an estimate of approximately 0.9 overlap squared with a candidate ground state of the Fe Mo cofactor, producing a total resource estimate of 7.3 × 10 10 Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that have used perfect ground-state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system. Published by the American Physical Society 2025

Berry, Dominic W. (ORCID:0000000334461449)↗
DOE OSTI2021

State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin

Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be used to optimize DER operations for distribution voltage regulation.

Darbali-Zamora, Rachid↗
DOE OSTI2026

Validating and Comparing Energy Estimation Methods at Water Resource Recovery Facilities

Water resource recovery facilities play a crucial role in the water-energy nexus, consuming a substantial amount of energy in the United States. Growing treatment volumes and more stringent water quality standards are expected to increase the amount of energy needed to treat wastewater, but accurately estimating energy consumption and potential remains challenging due to variability in scale, treatment methods, and effluent treatment standards. In this study, we used publicly available data to evaluate the accuracy of methods for estimating energy consumption and generation, then quantified uncertainty based on key factors like flow rate, treatment level, and geographic location. To validate methods, we estimated energy consumption and generation at the facility-level, then compared estimates to self-reported data from utilities in major U.S. cities. We found that process models of treatment trains under best practice configurations were accurate relative to other methods for estimating electricity use, total energy use, and electricity generation from biogas utilization, and less complex methods based on effluent treatment level and prime movers also performed well for estimating electricity consumption and generation, respectively. Applying the evaluated methods to a national inventory of treatment facilities, we estimate that annual energy consumption ranged from 56.3 x 10^3 to 82.5 x 10^3 TJ in 2012 and 83.6 x 10^3 to 127 x 10^3 TJ in 2042. Our results indicate that not all estimation methods are suited for every use case, so we recommend that researchers and practitioners select an estimation method based on data availability and desired computational intensity.

Hodson, Abigayle↗
DOE OSTI2022

The impact of multi-sensor land data assimilation on river discharge estimation

River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. In this study, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R 2 increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. Finally, by assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to predict the terrestrial hydrological cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗
DOE OSTI2023

NETL’s Perspective on Storage Efficiency and CO2-SCREEN

Carbon capture and storage (CCS) is a process that captures carbon dioxide (CO2) by separating it from anthropogenic emissions sources before atmospheric release and storing that CO2 in deep geologic reservoirs. CCS is a powerful method for reducing anthropogenic CO2 which can ultimately diminish the effects of climate change. Prospective CO2 storage resource is the amount of carbon dioxide that can be stored in a given geologic formation typically given as a mass (e.g., metric tons). Obtaining accurate estimates of CO2 storage resources is necessary for governments and industries to make energy-related policy decisions. Researchers at the National Energy Technology Laboratory (NETL) under the Department of Energy (DOE) developed a methods and a tool [CO2-SCREEN (Storage prospeCtive Resource Estimation Excel aNalysis) to estimate prospective carbon storage resources for saline formations, unconventional shale formations, and residual oil zones. The methods and tool provide CO2 storage and efficiency outputs in the form of probability estimates (i.e. P10 and P90) as well as partitioning storage and efficiency estimates based on storage mechanism (total, free phase, sorbed phase, and dissolution phase). This presentation will focus on how storage efficiency is calculated based on numerical modeling efforts, how it’s applied in the storage methods and tool, and then highlighting needs for future development.

Hanson, Angela Goodman↗
DOE OSTI2020

Hawthorne Nevada Deep Direct-Use Feasibility Study - Data Used for Geothermal Resource Conceptual Modeling and Power Capacity Estimates

This data submission includes several data components that were used to develop a conceptual model and power capacity-estimates of two low-temperature geothermal resources that define geothermal prospect A at Hawthorne, Nevada. Data are sourced from a combination of legacy publicly-available data and more recent data acquisition conducted by the US Navy Geothermal Program Office (2008-2013) and the Great Basin Center for Geothermal Energy at the University of Nevada, Reno (2008-2010). Data sets include compiled fluid geochemistry data, down-hole temperature logs for wells in the vicinity of prospect A, 2 meter temperature survey data, temperature-spinner logs acquired in well HWAAD-2A, fracture picks from image log data acquired in wells HWAAD-2 and HWAAD-3, and X-Ray Diffraction (XRD) analyses on cuttings from wells HWAAD-2A and HWAAD-3. These data have been reviewed for errors and inconsistencies, but it is possible that few errors could still remain. The resource conceptual model and power capacity estimates are included in the final report to the US Department of Energy, and are presented in a manuscript by Ayling and Hinz. A link to the manuscript published in Geothermics is linked below in this submission.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States (2023 Edition)

Estimates of the potential of renewable energy are essential for understanding how we can decarbonize our electric grid and economy. They provide key data for policymakers, land managers, and energy modelers by defining the quantity, quality, and cost of renewable resources. However, estimating renewable energy potential is challenging and requires frequent updates because of rapid advances in technology, cost reductions, and uncertainty about developable land that are due to social, regulatory, and environmental factors. Additionally, the complex processes involved in renewable energy development require regular reviews of methods and assumptions, which can also impact our understanding of renewable potential. In this, the 2023 edition of this annual report, we present new estimates of the technical potential for land-based wind and solar photovoltaics (PV) for the contiguous United States (CONUS). We also provide cost estimates for the available resources, presenting representative supply curves that can be used in downstream modeling and analysis. Additionally, we introduce the new methodologies used to estimate wind capacity, wind energy losses, transmission cost and representation, updated technology cost and design, and scenarios of siting constraints designed to help bound the uncertainty of renewable potential.

14 SOLAR ENERGY↗
DOE OSTI2023

A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation

Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.

Gaussian process regression↗
DOE OSTI2022

Introduction to Evolve Central Appalachia

The Evolve-CAPP project is developing and implementing strategies to enable the Central Appalachian basin to realize its full economic potential for producing high-value, non-fuel, carbon-ore (CO) products, rare earth elements (REE), and critical minerals (CM). The Basinal Assessment of CORE-CM Resources is a state-of-the-art geological model and comprehensive database that will be used to identify, locate, and quantify in-situ resources in the basin. Restrictive aspects of resource recovery, such as ownership and legal restrictions, are being considered in estimating recoverable resources. A gap analysis of missing or unavailable data that could not be fully integrated in the initial assessment due to budget, access, or other constraints during the assessment effort is planned. A characterization and data acquisition plan to fully characterize the basin’s CORE-CM potential will address gaps in data. The plan will be developed with input from project team members and relevant stakeholders, including land and mineral holding companies and coal operators, who maintain sampling programs as part of their normal operations.

Bishop, Richard↗
DOE OSTI2020

Shallow EGS Regional Resource Potential and Map - Snake River Plain

SMU Geothermal Lab developed a methodology to estimate shallow (1 km to 4 km) Enhanced Geothermal Systems (EGS) resource potential using an approach that utilizes recent geology and geophysical research along with new well data to improve the thermal conductivity model, mitigate impacts from groundwater flow in the thermal model, and examine radioactivity data variations. By incorporating the results of the most recent projects with the SMU shallow methodology, we developed a more accurate, updated resource estimate for the Snake River Plain (SRP). The resulting maps and resource estimates can be used by the National Renewable Energy Lab (NREL), Bureau of Land Management (BLM), and the public to determine how best to move forward with future project development.

15 GEOTHERMAL ENERGY↗