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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 361 records · Page 20
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NASA NTRS2014

The Rapid Evolution of the Exciting Star of the Stingray Nebula

Context: SAO244567, the exciting star of the Stingray nebula, is rapidly evolving. Previous analyses suggested that it has heated up from an effective temperature of about 21 kK in 1971 to over 50 kK in the 1990s. Canonical post-asymptotic giant branch evolution suggests a relatively high mass while previous analyses indicate a low-mass star. Aims: A comprehensive model-atmosphere analysis of UV and optical spectra taken during 1988-2006 should reveal the detailed temporal evolution of its atmospheric parameters and provide explanations for the unusually fast evolution. Methods: Fitting line profiles from static and expanding non-LTE model atmospheres to the observed spectra allowed us to study the temporal change of effective temperature, surface gravity, mass-loss rate, and terminal wind velocity. In addition, we determined the chemical composition of the atmosphere. Results: We find that the central star has steadily increased its effective temperature from 38 kK in 1988 to a peak value of 60 kK in 2002. During the same time, the star was contracting, as concluded from an increase in surface gravity from log g = 4.8 to 6.0 and a drop in luminosity. Simultaneously, the mass-loss rate declined from log(M/M (solar mass) yr (exp −1)) = −9.0 to −11.6 and the terminal wind velocity increased from v (infinity) = 1800 km s (exp −1) to 2800 km s (exp −1). Since around 2002, the star stopped heating and has cooled down again to 55 kK by 2006. It has a largely solar surface composition with the exception of slightly subsolar carbon, phosphorus, and sulfur. The results are discussed by considering different evolutionary scenarios. Conclusions: The position of SAO244567 in the log T (sub eff) -log g plane places the star in the region of sdO stars. By comparison with stellar-evolution calculations, we confirm that SAO244567 must be a low-mass star (M < 0.55 solar mass). However, the slow evolution of the respective stellar evolutionary models is in strong contrast to the observed fast evolution and the young planetary nebula with a kinematical age of only about 1000 years. We speculate that the star could be a late He-shell flash object. Alternatively, it could be the outcome of close-binary evolution. Then SAO244567 would be a low-mass (0.354 solar mass) helium pre-white dwarf after the common-envelope phase, during which the planetary nebula was ejected.

Stingray↗
NASA NTRS2016

Lidar-Based Estimates of Above-Ground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 +/- 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 +/- 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 +/- 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log-linear model result (63.29 +/-1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log-linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon,−40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., N2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10 +/- 0.88 t/ha, and the total biomass for the country, given a total forest area of 688,096 sq km, is 3.65 +/- 0.06 Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152 Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06 Gt) by 19%.

Hybrid 3-Phase sampling↗
NASA NTRS2017

Mapping of the Resistance of a Superconducting Transition Edge Sensor as a Function of Temperature, Current, and Applied Magnetic Field

We have measured the resistance R (T, I, B(sub ext) of a superconducting transition edge sensor over the entire transition region on a fine scale, producing a four-dimensional map of the resistance surface. The dimensionless temperature and current sensitivities (alpha equivalence partial derivative log R/partial derivative log T|(sub I) and beta equivalence partial derivative log R/partial derivative log I|(sub T) of the TES resistance have been determined at each point. alpha and beta are closely related to the sensor performance, but show a great deal of complex, large amplitude fine structure over large portions of the surface that is sensitive to the applied magnetic field. We discuss the relation of this structure to the presence of Josephson weak link fringes.

Zhang, Shou↗
NASA NTRS2019

Direct Te Metallicity Calibration of R23 in Strong Line Emitters

The gas metallicity of galaxies is often estimated using strong emission lines such as the optical lines of [O iii] and [O ii]. The most common measure is "R23," defined as ([O ii]λλ3726, 3729 + [O iii]λλ4959,5007)/Hβ. Most calibrations for these strong-line metallicity indicators are for continuum selected galaxies. We report a new empirical calibration of R23 for extreme emission-line galaxies using a large sample of about 800 star-forming green pea galaxies with reliable Te -based gas-phase metallicity measurements. This sample is assembled from Sloan Digital Sky Survey (SDSS) Data Release 13 with the equivalent width of the line [O iii]λ5007 > 300 Å or the equivalent width of the line Hβ > 100 Å in the redshift range 0.011 < z < 0.411. For galaxies with strong emission lines and large ionization parameter (which manifests as log [O iii]λλ4959,5007/[O ii]λλ3726,3729 ≥0.6), R23 monotonically increases with log(O/H) and the double-value degeneracy is broken. Our calibration provides metallicity estimates that are accurate to within ~0.14 dex in this regime. Many previous R23 calibrations are found to have bias and large scatter for extreme emission-line galaxies. We give formulae and plots to directly convert R23 and [O iii]λλ4959,5007/[O ii]λλ3726,3729 to log(O/H). Since green peas are best nearby analogs of high-redshift Lyα emitting galaxies, the new calibration offers a good way to estimate the metallicities of both extreme emission-line galaxies and high-redshift Lyα emitting galaxies. We also report on 15 galaxies with metallicities less than 1/12 solar, with the lowest metallicities being 12+log(O/H) = 7.25 and 7.26.

Jiang, Tianxing↗
NASA NTRS2019

Collecting and Processing Earth Science Data Metrics at NASA ESDIS

Since the launch of Terra satellite in 1999, the number of Earth Science remote sensing data products created and distributed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) has increased from a few hundred to nearly ten thousand. NASA's Earth Science Data and Information System (ESDIS) Metrics System (EMS) collects metrics on data ingest, archive, and distribution by its Distributed Active Archive Centers (DAACs) and the Science Investigator-led Systems (SIPS), known as Data Providers. These metrics are critical in helping NASA management as well as data producers in resource planning and gaining a wide range of knowledge of data users and data usage.EMS receives flat files, or log files of data archive, ingest, and distribution either in their raw format, such as Apache web logs, or text files of log records formatted by the Data Providers. Tens of millions of records are processed each day to extract metrics on data products, user information, distribution protocols and services, and so on. The metrics are then made available to designated parties.This presentation provides an overview of the EMS processing workflow and improvement efforts made in recent years to handle ever-increasing number of data records and new metrics requirements, discusses several key steps including mapping log records to data products and identifying user communities along with geo-distribution, and demonstrates typical metrics capabilities produced by the EMS system. Challenges and potential approaches to improve the system are also discussed.

Pan, Jianfu↗
NASA NTRS2019

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Brazilian Amazon↗
NASA NTRSDate not supplied

The Effects of Plasma Application on Radish Seeds with Implications for Space Crop Production

In extended space missions, the astronaut diet will consist mostly of prepackaged foods. This could result in nutritional deficiencies due to the gradual breakdown of certain vitamins. To address this deficiency, fresh produce must be grown from seed during spaceflight. Stored seeds, however, can be vulnerable to microbial contamination which could jeopardize plant health and crop food safety. To alleviate this concern, the current practice is to sanitize seeds on the ground before spaceflight to the International Space Station (ISS). Methods of seed sanitization include alcohol soaking and chlorine gas fumigation, which have harmful effects on the environment and human health. Plasma application is a new sanitization approach that avoids these negative side effects while potentially elevating germination rate and improving growth rate; yet plasma application requires specific exposure time, power, and pressure to achieve these benefits. In the present study, Raphanus sativus ‘Cherry Belle’ radish seeds were exposed to either low pressure (Diener) plasma or atmospheric pressure plasma for varying increments of time (30s-1200s). Data collected includes immediate germination rate (viability) and microbial log reduction. Additionally, seeds were stored for later germination rate testing. Microbial assay controls have highlighted variability in initial microbial load between individual seeds, and further work is being performed to determine the standard microbial load of an untreated seed. In some samples, a negative log reduction was observed after plasma treatment encouraging further study to determine if the plasma is perforating the seed coat and releasing endophytic microbes. Treatment with Diener plasma shows promising microbial log reduction, but lower viability. In contrast, treatment with atmospheric pressure plasma offers high viability, but poor microbial log reduction. Further study will aim at determining whether plasma treatment is effective at not only sanitization, but sterilization. This research was funded by a NASA grant at the Kennedy Space Center.

Plasma↗
NASA NTRSDate not supplied

The Effects of Plasma Application on Radish Seeds with Implications for Space Crop Production

In extended space missions, the astronaut diet will consist mostly of prepackaged foods. This could result in nutritional deficiencies due to the gradual breakdown of certain vitamins. To address this deficiency, fresh produce must be grown from seed during spaceflight. Stored seeds, however, can be vulnerable to microbial contamination which could jeopardize plant health and crop food safety. To alleviate this concern, the current practice is to sanitize seeds on the ground before spaceflight to the International Space Station (ISS). Methods of seed sanitization include alcohol soaking and chlorine gas fumigation, which have harmful effects on the environment and human health. Plasma application is a new sanitization approach that avoids these negative side effects while potentially elevating germination rate and improving growth rate; yet plasma application requires specific exposure time, power, and pressure to achieve these benefits. In the present study, Raphanus sativus ‘Cherry Belle’ radish seeds were exposed to either low pressure (Diener) plasma or atmospheric pressure plasma for varying increments of time (30s-1200s). Data collected includes immediate germination rate (viability) and microbial log reduction. Additionally, seeds were stored for later germination rate testing. Microbial assay controls have highlighted variability in initial microbial load between individual seeds, and further work is being performed to determine the standard microbial load of an untreated seed. In some samples, a negative log reduction was observed after plasma treatment encouraging further study to determine if the plasma is perforating the seed coat and releasing endophytic microbes. Treatment with Diener plasma shows promising microbial log reduction, but lower viability. In contrast, treatment with atmospheric pressure plasma offers high viability, but poor microbial log reduction. Further study will aim at determining whether plasma treatment is effective at not only sanitization, but sterilization. This research was funded by a NASA grant at the Kennedy Space Center.

Space Crop Production↗
NASA NTRS2021

Simultaneous Multiwavelength Flare Observations of EV Lacertae

We present the first results of our ongoing project conducting simultaneous multiwavelength observations of flares on nearby active M dwarfs. We acquired data of the nearby dM3.5e star EV Lac using five different observatories: NASA's Transiting Exoplanet Survey Satellite (TESS), NASA's Neil Gehrels Swift Observatory (Swift), NASA's Neutron Interior Composition Explorer (NICER), the University of Hawaii 2.2-meter telescope (UH88), and the Las Cumbres Observatory Global Telescope (LCOGT) Network. During the ∼25 days of TESS observations, we acquired three simultaneous UV/X-ray observations using Swift that total ∼18 ks, 21 simultaneous epochs totaling ∼98 ks of X-ray data using NICER, one observation (∼3 hr) with UH88, and one observation (∼3 hr) with LCOGT. We identified 56 flares in the TESS light curve with estimated energies in the range log E(T) (erg) = (30.5–33.2), nine flares in the Swift UVM2 light curve with estimated energies in the range log E(UV) (erg) = (29.3–31.1), 14 flares in the NICER light curve with estimated minimum energies in the range log E(N) (erg) = (30.5–32.3), and 1 flare in the LCOGT light curve with log E(L) (erg) = 31.6. We find that the flare frequency distributions (FFDs) of TESS and NICER flares have comparable slopes, β(T) = −0.67 ± 0.09 and β(N) = − 0.65 ± 0.19, and the FFD of UVOT flares has a shallower slope (β(U) = −0.38 ± 0.13). Furthermore, we do not find conclusive evidence for either the first ionization potential (FIP) or the inverse FIP effect during coronal flares on EV Lac.

Rishi R. Paudel↗
NASA NTRSDate not supplied

Enabling Communication Between Astronauts and Ground Teams for Space Exploration Missions

Over the last four years, Playbook’s Mission Log has evolved to become an enabling capability for analog missions that simulate deep space, exploration missions with communication transmission latency. Playbook is a planning and execution web-application for mission operations, aggregating multiple sources of information for astronauts to execute the mission in one place: timeline, procedures, chat interface. Playbook’s Mission Log provides a multimedia chat software interface with unique features and functionalities that support asynchronous communication between analog astronauts and ground support teams. This paper describes the iterative design the Mission Log has undergone based on user observations and solicited feedback. Key features include indicators that help users cope with asynchronous communication as well as aids that assist teams coordinate work. Future work and capabilities are outlined, which build upon the increased use of the Mission Log as a communication and coordination tool for space exploration.

communication↗
DOE OSTI2026

A New Record Census of Dwarf AGN and a Bimodal $M_{\rm BH}$-$M_{\star}$ Scaling Relation with DESI DR1

Using the first spectroscopic data release from the Dark Energy Spectroscopic Instrument (DESI DR1), we search for AGN signatures in 1,678,787 low-redshift ($0.001 \le z \le 0.45$) line-emitting galaxies. Based on the [NII]-BPT emission-line ratio diagnostic, we identify AGN in 314,245/1,211,573 (25.9%) high-mass ($\log (M_{\star}/M_{\odot}) > 9.5$) and 9648/467,214 (2.1%) dwarf ($\log (M_{\star}/M_{\odot}) \le 9.5$) galaxies. Among these AGN, 17,949 are broad-line candidates (BL-AGN) with broad H$α$ emission, enabling black hole (BH) mass estimates using single-epoch virial methods. We find that the AGN fraction in line-emitting galaxies increases monotonically with stellar mass, rising from $\sim$1.4% at the low-mass end to $\sim$93.3% at the high-mass end. Using the large BL-AGN sample, we extend the $M_{\rm BH} - M_{\star}$ scaling relation down to $\log (M_{\star}/M_{\odot}) \approx 7.8$ and $\log (M_{\rm BH}/M_{\odot}) \approx 4.4$. In the context of high-redshift overmassive BHs, our results suggest that galaxies and their central BHs may follow two distinct evolutionary pathways across cosmic time. With this paper, we release the EmFit value-added catalog, containing emission-line flux and width measurements for $\sim$7.4 million galaxies, the largest catalog with emission-line decomposition into narrow, broad, and outflow components to date. This work significantly expands upon the early DESI results and provides a statistical sample for probing the galaxy$-$BH connection in the low-mass galaxy regime.

Pucha, Ragadeepika [Utah U.; Arizona U., Astron. D↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
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 OSTI2021

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

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

58 GEOSCIENCES↗
DOE OSTI2024

Geologic Characterization of the South Georgia Rift Basin for Source Proximal CO2 Storage

The project Geologic Characterization of the South Georgia Rift Basin for Source Proximal CO2 Storage 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 South Carolina Research Foundation and partners evaluated the feasibility of CCS in the Jurassic/ Triassic (J / TR) saline formations of the buried Mesozoic South Georgia Rift (SGR) Basin that extends from South Carolina into Georgia. The J / TR sequence, based on preliminary assessment of limited geologic and geophysical data, appears to have both the appropriate areal extent and multiple horizons to permanently and safely store CO2 The presence of several igneous rock layers within the sequence may potentially provide adequate seals to prevent upward CO2 migration into the Coastal Plain aquifer systems. Approximately 81 kilometers of 2-D seismic reflection data were collected by Bay Geophysical, Inc. to explore a portion of the SGR located in southern Georgia. The 81 kilometers were divided into two lines approximately 40.5 kilometers each, with Line 1 intersecting Georgia well GGS 3457. Line 2 intersects Line 1 at the southern portion of Line 1 to maximize the extent of coverage away from GGS-3457 (a deep well drilled in the 1980s for oil and gas exploration). This well had a set of usable logs, including gamma and neutron logs that provided promising results related to CO2 storage. Results showed sandstone with porosity values greater than 10 percent and a thickness of 120 meters. The design of the seismic shot was to extrapolate information away from the well and to better define the extent of the SGR and the necessary reservoir and caprock for a successful CO2 injection. A numerical simulation model of CO2 Injection and migration was developed based on the geology log for the GGS-3457 well. The simulation model was used to investigate the feasibility of injecting 30 million metric tons of CO2 into SGR J / TA sediments and integrity of the diabase layers as seals to prevent CO2 migration.

2-D seismic↗
DOE OSTI2024

Characterization of Most Promising Sequestration Formations in the Rocky Mountain Region

The project Characterization of Most Promising Sequestration Formations in the Rocky Mountain Region 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 Rocky Mountain Carbon Capture and Storage (RMCCS) project investigated multiple geologic formations and characterized a local site on the Colorado Plateau for future CCS opportunities. The RMCCS project focused on the Cretaceous Dakota, Jurassic Entrada, and Pennsylvanian Weber Sandstones, the three largest regional formations. All formations in this project are potential CO2 storage resources for future power plants, natural gas processing plants, cement plants, and oil shale development projects. The area adjacent to Craig, Colorado, (Sand Wash Basin) was the area selected for detailed geologic characterization on the RMCCS project. The basin was selected in part because the geology can be extrapolated to other sites on the Colorado Plateau. Field mapping and seismic surveys were conducted to identify and evaluate the basin's structural configuration. A 9,745-foot deep characterization well was drilled to collect 131 feet of core and a suite of geophysical well log data. Petrophysical tests on samples of core were used to calibrate geophysical log data, which can be used to obtain storage resource estimates and evaluate associated uncertainty as well as simulate the hydrologic behavior of injected CO2. A detailed analysis of the primary formations (Dakota, Entrada and Weber sandstones) yielded a more accurate CO2 storage resource assessment for these formations within the Colorado Plateau; RMCCS estimates indicate a total CO2 storage resource of more than 38,000 million metric tons. The characterization of the Sand Wash Basin (2-D seismic surveys, multiple well logs and lithological, petrophysical and geochemical analyses) allowed for a detailed 3-D model to be constructed. The model served as the framework for analyses ranging from CO2 storage resource, injectivity, and subsurface flow to uncertainty estimates to evaluation of risk.

2-D seismic↗
DOE OSTI2025

Predicting Dynamic-to-Static Correction Factor from Petrophysical Data and Chemostratigraphy using Unsupervised Machine Learning

Estimating static mechanical properties of stratigraphic layers is critical for optimizing subsurface engineering applications. To estimate dynamic-to-static correction factor F ds (static-to-dynamic Young’s modulus ratio) across the Caney shale interval in Oklahoma, USA, we integrated triaxial test measurements and petrophysical data, including well logs and X-ray fluorescence (XRF) using unsupervised machine learning (ML). We used a novel workflow that includes principal component analysis (PCA) to reduce data set dimensionality of well logs and XRF data sets—both separately and combined—creating three scenarios, and later applied inverse distance weighting (IDW) to derive F ds profiles for these scenarios. Furthermore, we applied K-means clustering on each scenario to predict depositional facies, and built a stiffness zonation profile through chemostratigraphic analysis of the terrigenous elements to validate the predicted F ds . The predicted F ds profile from each scenario using the PCA-IDW method was compared with the constant F ds approach from our previous study by calculating the root mean square error (RMSE). The combined data sets scenario yielded the lowest RMSE value of 0.113, while the RMSE values for the well logs and XRF scenarios were 0.131 and 0.129, respectively. In addition, the predicted F ds from the XRF scenario well-matched the stiffness zonation from the chemostratigraphic analysis that was built using the optimized K-means clustering of nine clusters for that scenario. These methods and findings offer a valuable tool for refining lithological classification and improving the F ds profile, potentially enhancing drilling and stimulation strategies for subsurface energy engineering applications.

clastic rock↗
DOE OSTI2021

Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer

Restricted Boltzmann Machine (RBM) is an energy-based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate the exact gradient of the log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), where obtaining samples is faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results of RBM trained using quantum annealing are compared with the CD-based method. The performance of the two approaches is compared with respect to the classification accuracies, image reconstruction, and log-likelihood results. The classification accuracy results indicate comparable performances of the two methods. Image reconstruction and log-likelihood results show improved performance of the CD-based method. It is shown that the samples obtained from quantum annealer can be used to train an RBM on a 64-bit “bars and stripes” dataset with classification performance similar to an RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer could be useful as it eliminates computationally expensive MCMC steps of CD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗