Search NASA⌕ Search

SEARCH · Search NASA

Results for “image log”

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 OSTI2022

Tuscaloosa Marine Shale Laboratory

The Tuscaloosa Marine Shale (TMS) in Louisiana and Mississippi is an Upper Cretaceous source rock formation sandwiched between the sands of the upper and lower Tuscaloosa sections. The TMS is believed to be the source rock for underlying prolific Tuscaloosa sand formation. The TMS has an unproven estimate of 7,000,000,000 bbls of recoverable oil while its current total average production is about 3,000 bbls of oil per day in 2017. In 2013 and 2014, more than 80 wells were drilled horizontally into the TMS that were fractured using multi-stage fracturing technology. The results from this have been mixed, but recent production for several wells show an appealing initial oil production rate of more than 1000 bbl/day. The preliminary core analysis by industry partners and a few literature studies shows that the TMS is one of the most clay-rich and sensitive shales to water. Due to these and other technical problems, there is high risk for the economic development of TMS compared to other shale plays. The experiences of major industrial players in the TMS show the necessity of open and collaborative efforts to better understand the critical gaps in the development of this challenging and potentially highly economic shale play to enable more cost-efficient and environmentally-sound recovery from this unconventional liquid-rich shale play. The overall objective of this project is to form a consortium of science and industry partners to address the following six major objectives using scientific and technical approaches: 1. To improve wellbore integrity by better understanding the sources of the wellbore instability issues, proposing innovative mud and cement design for the TMS. 2. To improve formation evaluation using laboratory techniques for the evaluation of mineralogical composition, organic content, and produced-water chemistry as well as well log and geophysical analysis. 3. To determine the role of geologic discontinuities on fracture growth and shale creep behavior using digital image correlation technique. 4. To investigate the application of stable CO 2 foam and super-hydrophobic proppants for improved reservoir stimulation. 5. To better understand the nature of water/hydrocarbon/CO 2 flow in clay and organic-rich formation and the role of water/fluid interaction on recovery. 6. To prepare better socio-economic environment for TMS development by community engagement. Subsequently, the TMS virtual laboratory conducted testing and analysis of various properties of rock and formation fluids from the TMS, including but not limited to the following: Analyzing reports and logs to better understand the source of wellbore instability in TMS wells; Experiments to design a customized cement based on TMS requirements; Experiments to obtain the mineralogical and geochemical composition of TMS samples; Seismic analysis of TMS geophysical data to better predict total organic carbon (TOC) content and brittleness in TMS; Well log analysis to better estimate the TOC and geo-mechanical properties of TMS; Experiments on formation water to understand the chemistry of produced water; Experiments to determine the role of lamination and natural fractures on fracture propagation or rock deformation using digital image correlation technique in in-direct tensile tests, semi-circular bend test and creep tests Experiments to determine the stability and rheological properties of nanoparticle-stabilized CO 2 foam in TMS rock samples; Experiments to determine fluid dynamics in un-propped TMS fractures and the role of nano-coating of proppants on fluid dynamics in fractures with proppants; Micro-fluidics experiments to enhance the understanding of fluid dynamics in tight liquidrich pores with high clay content; Socio-economic studies to better engage communities in TMS development.

58 GEOSCIENCES↗
DOE OSTI2021

Secondary porosity prediction in complex carbonate reefs using 3D CT scan image analysis and machine learning

Development and preservation of carbonate porosity and permeability are critical to characterizing reservoirs. However, secondary porosity, such as vugs and fractures, are difficult to identify and require collection of expensive wireline tools or core sampling. Wireline logs and cores have traditionally been used to identify the presence of secondary porosity but fail to quantify the contribution to total reservoir porosity. Additionally, advanced wireline logs and core are not readily available for most wells. Dual energy CT scans were collected on whole core from the A-1 Carbonate and Brown Niagaran formations drawn from six wells in northern Michigan. 3D analysis techniques were applied to identify and isolate secondary porosity features. A series of machine learning and data analytics techniques were applied to the dataset to predict secondary porosity features on basic wireline logs. The predictive model successfully predicted secondary porosity with high confidence.

02 PETROLEUM↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
DOE OSTI2025

Shock compression of additively manufactured high solids loaded polymer composites with aligned porosity

The effects of aligned porosity on the shock-compression response of additively manufactured (AM) composite structures with collinear and log-cabin geometries are investigated. The composites contain two inorganic and two organic particle populations and a UV-curable polymer binder. X-ray phase contrast imaging (X-PCI) is used as an in-material diagnostic in plate-impact experiments performed at the Dynamic Compression Sector of the Advanced Photon Source at the Argonne National Laboratory. Macroscale shock and particle velocities and microscale pore collapse velocities are measured from X-PCI images obtained from samples impacted in various orientations relative to the AM build directions. The linear shock and particle velocity relation for the structures with collinear build geometry shows directional differences, with the build direction exhibiting the highest slope and the filament print direction having the shallowest slope. Composites with log-cabin geometry, due to their slightly lower porosity and smaller sized pores, show higher shock velocities than the collinear structures at the same particle velocity. Microscale directionality effects are also observed, with the pore collapse velocity dependent on the pore configuration. Specifically, the pore collapse velocity is highest for pores elongated along the impact direction which is the case for the colinear structures impacted in the filament print direction, which is consistent with the directionality trend observed in the data for the shock and particle velocity equation of state. The pore collapse velocity approaches ∼80 % of the shock velocity and is several times the average bulk particle velocity, suggesting a highly hydrodynamic mechanism of pore collapse. Overall, both the macroscale response as measured by the shock and particle velocity relationship, and the microscale response as measured by the pore collapse velocity, are found to be anisotropic and dependent on the AM composite structure.

Wagner, K. B. [Georgia Institute of Technology, At↗
DOE OSTI2023

A Multiwavelength Study of Active Galactic Nuclei in Post-merger Remnants

Here, we investigate the role of galaxy mergers in triggering active galactic nuclei (AGN) in the nearby universe. Our analysis is based on a sample of 79 post-merger remnant galaxies with deep X-ray observations from Chandra/XMM-Newton capable of detecting a low-luminosity AGN of ≥10 40.5 erg s -1 . This sample is derived from a visually classified, volume-limited sample of 807 post-mergers identified in the Sloan Digital Sky Survey Data Release 14 with log M * /M ⊙ ≥ 10.5 and 0.02 ≤ z ≤ 0.06. We find that the X-ray AGN fraction in this sample is 55.7% ± 5.6% compared to 23.6% ± 2.8% for a mass- and redshift-matched noninteracting control sample. The multiwavelength AGN fraction (identified as an AGN in one of X-ray, IR, radio or optical diagnostics) for post-mergers is 76.6% ± 4.8% compared to 39.1% ± 3.2% for controls. Thus post-mergers exhibit a high overall AGN fraction with an excess between 2 and 4 depending on the AGN diagnostics used. In addition, we find most optical, IR, and radio AGN are also identified as X-ray AGN while a large fraction of X-ray AGN are not identified in any other diagnostic. This highlights the importance of deep X-ray imaging to identify AGN. We find that the X-ray AGN fraction of post-mergers is independent of the stellar mass above log M * /M ⊙ ≥ 10.5 unlike the trend seen in control galaxies. Overall, our results show that post-merger galaxies are a good tracer of the merger–AGN connection and strongly support the theoretical expectations that mergers trigger AGN.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Reference data, predictors, and probability grids for forest degradation classes in three sites in the Brazilian Amazon

Forest degradation by fires and selective logging is widespread in the Amazon region. We implemented a gradient boosted classification modeling framework to classify intact, logged, and burned forests at three Amazonian sites: Feliz Natal Municipality and Xingu Indigenous Territory in Mato Grosso State, and Saracá-Taquera National Forest in Pará State. We used forest degradation history from Landsat time-series as reference data and textural metrics derived from PlanetScope images as predictors. Textural metrics were computed using the Gray-Level Co-Occurrence Matrix (GLCM) textural technique. Included in the attached zip file are ten files: - a shapefile containing the reference data (fire and selective logging polygons and year of event) for each site; - a multiband tif file containing the 8 GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, Correlation) at the original PlanetScope resolution (3.125m) for each site; - a multiband tif file containing the 72 aggregated GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, and Correlation aggregated using the mean, first quartile, third quartile, maximum, median, minimum, root mean square, standard deviation, and skewness statistics) at 562m resolution for each site; - a multiband tif file containing the 3 probability grids for either intact, logged, or burned forests at the aggregation resolution (562m) for each site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Kimberlina synthetic multiphysics dataset for CO 2 monitoring investigations

Abstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO 2 reservoir model based on a potential CO 2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO 2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO 2 , and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO 2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO 2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.

58 GEOSCIENCES↗
DOE OSTI2025

Microtron Data Log

The Microtron at Los Alamos National Laboratory (LANL) is a versatile electron accelerator originally designed for medical therapy. Since 2001, it has been used for non-destructive radiographic imaging and research and development applications. Operating at four different energy levels—6, 10, 15, and 20 MeV—the Microtron produces dose rates of approximately 780, 1800, 2700, and 2800 R/min at a distance of one meter from the source, respectively. This high-energy X-ray source enables detailed internal examination of dense and thick objects without causing damage, making it invaluable for various scientific and industrial applications. For instance, LANL’s Microtron has been utilized to study the performance of large-panel cerium-doped lutetium yttrium silicon oxide (LYSO) scintillators, which are essential components in advanced imaging systems.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

COOL-LAMPS. IV. A Sample of Bright Strongly Lensed Galaxies at 3 < z < 4

We report the discovery of five bright, strong gravitationally lensed galaxies at 3 < z < 4: COOL J0101+2055 (z = 3.459), COOL J0104–0757 (z = 3.480), COOL J0145+1018 (z = 3.310), COOL J0516–2208 (z = 3.549), and COOL J1356+0339 (z = 3.753). These galaxies have magnitudes of r AB , z AB < 21.81 mag and are lensed by galaxy clusters at 0.26 < z < 1. This sample nearly doubles the number of known bright lensed galaxies with extended arcs at 3 < z < 4. We characterize the lensed galaxies using ground-based grz/giy imaging and optical spectroscopy. We report model-based magnitudes and derive stellar masses, dust content, and star formation rates via stellar population synthesis modeling. Building lens models based on ground-based imaging, we estimate source magnifications ranging from ~29 to ~180. Combining these analyses, we derive demagnified stellar masses in the range log 10 (M * /M ⊙ ) ~ 9.69 – 10.75 and star formation rates in the youngest age bin in the range log 10 (SFR/(M ⊙ yr -1 )) ~ 0.39 – 1.46, placing the sample galaxies on the massive end of the star-forming main sequence in this redshift interval. In addition, three of the five galaxies have strong Lyα emissions, offering unique opportunities to study Lyα emitters at high redshift in future work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Utah FORGE: Well 16B(78)-32 Logs from Schlumberger Technologies

This dataset is a collection of well logs provided by Schlumberger Technologies from the Utah FORGE well 16B(78)-32 drilling project. Information here includes critical borehole information collected by an ultrasonic borehole imager (UBI) and a fullbore formation microimager (FMI). Well 16B(78)-32 serves as the production well for reservoir creation, fluid circulation, and demonstration of heat extraction for the FORGE project. It has been drilled as a doublet approximately 300 feet parallel to and above the injection well 16A(78)-32. The total depth measured 10,947 feet and the vertical depth measured 8,357 feet.

11.5 inch intermediate casing logs↗
DOE OSTI2020

Charged Wellbore Casing Controlled Source Electromagnetics (CWC-CSEM) for Reservoir Imaging and Monitoring (Final report)

This project addresses the needs of the U.S. Department of Energy (DOE) to develop advanced monitoring technologies and protocols to track the fate of subsurface carbon dioxide (CO2) plumes for carbon storage. Specifically, the project seeks to develop and test a unique and novel system of technologies consisting of electromagnetic data acquisition, coupled multiphysics imaging, and reservoir model enhancement to understand the migration and long-term distribution of CO2 in the subsurface. The overarching objective is to develop an integrated approach for long term monitoring of carbon storage. The two main components of the project include the methodology development and the test of the method at a field site. The methodology component consists of 1) developing the field procedure and protocol for collecting time-lapse controlled-source electromagnetic (CSEM) data with source electric current injected into the subsurface through wellbore casings; 2) building of background 3D electrical conductivity utilizing multiple sources of data such as supplemental surface transient EM (TEM) surveys, well-logs, and seismic structural information, for enhancing CSEM signal from reservoir depths; and 3) coupled multiphysics simulations and inversion of CSEM data constrained by production data and by structural information from seismic imaging of the reservoir and overlying formations. The testing component used the field site of Bell Creek Oil Field, which served both as a field laboratory for the method development as well as a test site to evaluate the CSEM signal strengths and the methodology developed in this research project. We have accomplished all the proposed tasks and developed the methodology as planned. These include the procedure for time-lapse CSEM data acquisition, data processing techniques, integration with 3D conductivity model building, fast reservoir simulation for history matching using machine learning, and interpreting CSEM data with coupling to the reservoir modeling. Collectively, the outcome of these tasks form a coherent workflow that can be applied to monitor dedicated carbon storage in saline reservoirs. The testing component evaluated the applicability and limitations of the method, and concluded that the method would be ideal for monitoring dedicated carbon storage sites utilizing saline reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Abundances of CNO elements in z ∼ 0.3–0.4 Lyman continuum leaking galaxies

We present observations with the Space Telescope Imaging Spectrograph onboard the Hubble Space Telescope (HST) of 11 Lyman continuum (LyC) leaking galaxies at redshifts, z , in the range 0.29–0.43, with oxygen abundances 12+log(O/H) = 7.64–8.16, stellar masses M ⋆ ∼ 10 7.8 –10 9.8 M ⊙ , and O 32 = [O III ] λ5007/[O II ] λ3727 of ∼ 5–20 aiming to detect the C III ] λ1908 emission line. We combine these observations with the optical Sloan Digital Sky Survey (SDSS) spectra for the determination of carbon, nitrogen, and oxygen abundances. Our sample was supplemented by 31 galaxies from the literature, for which carbon, nitrogen, and oxygen abundances can be derived from the HST and SDSS spectra. These additional galaxies, however, do not have LyC observations. We find that log(C/O) for the entire sample at 12+log(O/H) < 8.1 does not depend on metallicity, with a small dispersion of ∼0.13 dex around the average value of ∼−0.75 dex. On the other hand, the log(N/O) in galaxies at z > 0.1, including LyC leakers, is systematically higher compared to the rest of the sample with lower metallicity. We find that log(C/O) slightly decreases with increasing M ⋆ from ∼ −0.65 at M ⋆ = 10 6 M ⊙ to ∼ −0.80 at M ⋆ = 10 9 –10 10 M ⊙ , whereas log(N/O) is considerably enhanced at M ⋆ > 10 8 M ⊙ . The origin of these trends remains basically unknown. A possible solution would be to assume that the upper mass limit of the stellar initial mass function in more massive galaxies is higher. This would result in a higher production of oxygen and a larger fraction of massive stars with stellar wind polluting the interstellar medium with nitrogen.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

The future of subsurface monitoring: AEC’s breakthroughs in CCS technology

Carbon capture and storage (CCS) has emerged as a key solution in the fight against climate change. However, for CCS to succeed, it is crucial to ensure that the sequestered CO2 stays safely trapped underground. The U.S. Department of Energy (DOE) has emphasized the need for advancements in subsurface monitoring, measurement, reporting, and verification. Aside from caprock integrity failure, the other primary failure points usually involve defective cement in the casing annulus of wellbores or plugged and abandoned wells. In addition, many energy producers (e.g., oil and gas, geothermal) and storage and disposal operators (e.g., H2 and water) must deal with the same issue. Poorly placed or degraded cement can create pathways for gas or fluid to escape from casing annuli and in plugged and abandoned or orphan wells, posing environmental risks. Yet, a reliable and cost-effective way to monitor cement and well integrity over multiple decades is still unavailable. Traditional geophysical methods like 4D seismic imaging and surface-based electromagnetic monitoring lack the resolution and accuracy for detecting these types of failures (Vasco et al., 2022; Fawad and Mondol, 2021). Wireline logging is expensive to run continuously and is obtrusive to the operation. While fiber optics can potentially be a solution, its bulkiness can significantly compromise the cement's integrity. To address these challenges, the Advanced Energy Consortium (AEC) at The University of Texas at Austin’s Bureau of Economic Geology (the Bureau) has been pioneering research in subsurface monitoring using its portfolio of distributed autonomous microfabricated sensors for harsh subsurface environments since 2008. A class of these microsensors [System on a Chip (SoC)] can be mixed in cement and permanently placed without compromising the cement column; the sensors would then communicate with each other or a data acquisition (DAQ) master node. Another class of the AEC microsensors can be fully autonomous, with rechargeable micro-batteries capable of exceeding 100°C, flash memory, and, currently, a pressure and temperature sensor. They are designed to circulate in mud, geothermal fluids, U-loops, or pipelines. They can log data into memory and are unobtrusive to operations. Our team has been working on a multi-year DOE-funded project (DE-FE0031856)—supported by $2.95M in federal funding and $0.75M in cost-matching from the AEC—to demonstrate SoC sensor utility for CO2 leakage monitoring in CCS applications. This multi-institutional collaboration developed a novel sensing architecture utilizing radiofrequency (RF) microsensors embedded within the cement sheath. These sensors detect CO2 migration and are interrogated via a Smart Casing Collar (SCC).

58 GEOSCIENCES↗
DOE OSTI2020

Structural and stellar-population properties versus bulge types in Sloan Digital Sky Survey central galaxies

ABSTRACT This paper studies pseudo-bulges (P-bulges) and classical bulges (C-bulges) in Sloan Digital Sky Survey (SDSS) central galaxies using the new bulge indicator ΔΣ1, which measures relative central stellar-mass surface density within 1 kpc. We compare ΔΣ1 to the established bulge-type indicator Δ〈μe〉 from Gadotti (2009) and show that classifying by ΔΣ1 agrees well with Δ〈μe〉. ΔΣ1 requires no bulge–disc decomposition and can be measured on SDSS images out to z = 0.07. Bulge types using it are mapped on to 20 different structural and stellar-population properties for 12 000 SDSS central galaxies with masses 10.0 < log M*/M⊙ < 10.4. New trends emerge from this large sample. Structural parameters show fairly linear log–log relations versus ΔΣ1 and Δ〈μe〉 with only moderate scatter, while stellar-population parameters show a highly non-linear ‘elbow’ in which specific star formation rate remains roughly flat with increasing central density and then falls rapidly at the elbow, where galaxies begin to quench. P-bulges occupy the low-density end of the horizontal arm of the elbow and are universally star forming, while C-bulges occupy the elbow and the vertical branch and exhibit a wide range of star formation rates at a fixed density. The non-linear relation between central density and star formation rate has been seen before, but this mapping on to bulge class is new. The wide range of star formation rates in C-bulges helps to explain why bulge classifications using different parameters have sometimes disagreed in the past. The elbow-shaped relation between density and stellar indices suggests that central structure and stellar populations evolve at different rates as galaxies begin to quench.

Luo, Yifei↗
DOE OSTI2021

Core Analysis E-Reports Produced for the Southwest Regional Partnership on Carbon Sequestration for Wells 13-10A, 13-14, and 32-8 of the Farnsworth Unit, Texas

The submission is three electronic reports (e-reports) produced by Terra Tek, a former Schlumberger company, for and under the oversight of the Southwest Regional Partnership on Carbon Sequestration (SWP). The SWP designed and implemented a coring and core analysis program in conjunction with the previous field operator Chaparral Energy, L.L.C. The main SWP contacts who oversaw the coring and core analysis program, including geologic core descriptions and sample selection for testing by the Terra Tek, are listed below in the Authors section. The three reports are for Wells 13-10A, 13-14, and 32-8 of the Farnsworth Unit, TX, a site of the SWP's CO2 storage and enhanced oil recovery project. The e-reports include many data types that are not listed in full detail here, but may include: geomechanical, geochemical, and petrophysical measurements; white light and UV core photos; spectral gamma logs on the core; and Heterogeneous Rock Analysis logs. Fracture core review reports are included for Wells 13-14 and 32-8. The three e-reports each have many subfolders with the various types of data in spreadsheets, pdfs, image files, or other formats. The main e-reports for each well are listed by the well name. The sub-folder “E-Report” for each well has a Report.html file, which, when opened, will have hyperlinks to the various data sets and information. The files in the zipped folder will need to be extracted for the Report.html files to work properly. These authors thank Joseph Hall, a geologist formerly of Chaparral Energy, L.L.C., for assistance in designing the coring program, his geologic expertise of the Farnsworth Unit, and his assistance with characterizing the core. Christopher Gillespie, of Terra Tek at the time, managed the myriad geomechanical, geochemical, and petrophysical measurements provided by Terra Tek for this project. The core from which the samples were collected for this project are housed at the Subsurface Data and Core Libraries of the New Mexico Bureau of Geology and Mineral Resources, Socorro, New Mexico, USA. Funding for this project is provided by the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) through the Southwest Regional Partnership on Carbon Sequestration (SWP) under Award No. DE-FC26-05NT42591. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. These e-reports describe objective technical results and analysis. Any subjective views or opinions that might be expressed in the reports do not necessarily represent the views of the US Department of Energy or the United States Government.

Caprock↗
DOE OSTI2021

Ultra diffuse galaxies in the MATLAS low-to-moderate density fields

Recent advances in deep dedicated imaging surveys over the past decade have uncovered a surprisingly large number of extremely faint low surface brightness galaxies with large physical sizes called ultra diffuse galaxies (UDGs) in clusters and, more recently, in lower density environments. As part of the Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) survey, a deep imaging large program at the Canada-France-Hawaii Telescope (CFHT), our team has identified 2210 dwarf galaxies, 59 (~3%) of which qualify as UDGs. Averaging over the survey area, we find ~0.4 UDG per square degree. They are found in a range of low to moderate density environments, although 61% of the sample fall within the virial radii of groups. Based on a detailed analysis of their photometric and structural properties, we find that the MATLAS UDGs do not show significant differences from the traditional dwarfs, except from the predefined size and surface brightness cut. Their median color is as red as the one measured in galaxy clusters, albeit with a narrower color range. The majority of the UDGs are visually classified as dwarf ellipticals with log stellar masses of ~6.5 – 8.7. The fraction of nucleated UDGs (~34%) is roughly the same as the nucleated fraction of the traditional dwarfs. Only five (~8%) UDGs show signs of tidal disruption and only two are tidal dwarf galaxy candidates. A study of globular cluster (GC) candidates selected in the CFHT images finds no evidence of a higher GC specific frequency S N for UDGs than for classical dwarfs, contrary to what is found in most clusters. The UDG halo-to-stellar mass ratio distribution, as estimated from the GC counts, peaks at roughly the same value as for the traditional dwarfs, but spans the smaller range of ~10 – 2000. Finally, we interpret these results to mean that the large majority of the field-to-group UDGs do not have a different formation scenario than traditional dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

KMT-2016-BLG-1836Lb: A Super-Jovian Planet from a High-cadence Microlensing Field

We report the discovery of a super-Jovian planet in the microlensing event KMT-2016-BLG-1836, which was found by the Korea Microlensing Telescope Network (KMTNet) high-cadence observations (Γ~4 hr -1 ). The planet–host mass ratio q ~ 0.004. A Bayesian analysis indicates that the planetary system is composed of a super-Jovian M{ planet =2.2 -1.1 +1.9 M J planet orbiting an M or K dwarf, M{ host =0.49 -0.25 0.38 M ⊙ , at a distance of D L =7.1 -2.4} +0.8 kpc. The projected planet–host separation is 3.5 -0.9 +1.1 au, implying that the planet is located beyond the snow line of the host star. Future high-resolution images can potentially strongly constrain the lens brightness and thus the mass and distance of the planetary system. Without considering detailed detection efficiency, selection, or publication biases, we find a potential mass-ratio desert at -3.7 ≲ log q ≲ -3.0 for the 31 published KMTNet planets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Novel Proppant Logging Technique for Infill Drilling of Unconventional Shale Wells

Summary During the development of an unconventional play, wells are drilled and completed in batches, and depending on the development plans, current and expected energy market trends, as well as other developmental considerations, new wells are drilled and hydraulically fractured later near existing producing laterals. This creates challenges in terms of optimizing resource recovery and reducing interwell communication. A novel approach is proposed that utilizes systematic composite sampling and analysis of drilling mud returns to look for and quantitatively identify sand particles. The workflow involves cleaning, drying, and segregation of samples into sizes of interest to us (size distribution of pumped proppant in offset parent wells). These samples are imaged at a very high resolution and analyzed for grains using characteristic optical imaging properties to classify proppant sand particles using computer vision algorithms. Further analysis, such as elemental compositional analysis, is used to validate the results from the imaging workflow. We present a case study from the Permian Basin, where a new child well was used as a test case to prove this technology at the Hydraulic Fracturing Test Site (HFTS-2) in Delaware Basin. We introduce new proppant parameters that help identify sustained proppant zones vs. localized propped fractures. We have used additional diagnostics and data collected at the test site to validate observations from the proppant log and have successfully interpreted significantly propped vs. unpropped zones. A key finding from this test has been the significant proppant transport distances observed away from parent wells. Observable proppant was found at a lateral distance of approximately 425 m for one set of parent wells and more than 915 m for another set of parent wells. While a major limitation of this technique is the sampling rate, given adequate sampling, the proposed technology represents a systematic and one-of-a-kind interpretation of spatial proppant distribution while drilling infill wells. It provides us with unique opportunities to better understand the current state of the reservoir being targeted, including zones that are likely highly drained relative to others, and how the planned hydraulic fracturing of child wells can be improved.

Energy & Fuels↗