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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Geothermal Exploration North of Mount St. Helens: Washington State Play-Fairway Project

Active seismicity within a broad zone in proximity to volcanism in the St. Helens Seismic Zone north of Washington State’s Mount Saint Helens provide key ingredients for hydrothermal circulation at depth. This seismicity extends well north of the volcanic edifice below where several faults and associated fractures in outcrop record repeated slip, dilation and alteration indicative of localized fluid flow. Candidate reservoir rocks include marine metasediments overlain by extrusive volcanics. This study characterizes the faults and fractures in the extrusives which are available in outcrop and sampled along well TGH17-24 and Plate Boundary Observatory strainmeter borehole, B201. Outcrop mapping and borehole image log analysis down to 250 m document abundant volcaniclastic deposits and basalt flows that are highly fractured, although intervening ash layers truncate the vertical extent of many of these structures. However, strike slip faults with well-developed gouge zones and adjacent localized high fracture density provide potential for vertically extensive fracture flow paths. Where these rocks are altered by intrusion, they develop high fracture density that can support heat transfer. Borehole image logs further record breakout that indicate the azimuth of SHmax in the shear zone is approximately 054/234°±23.6°, consistent with seismically active strike slip faults in the seismic zone, faults mapped in outcrop and evident in the borehole image logs, and with of plate convergence data. Furthermore, temperature profiles in two wells indicate isothermal conditions at average groundwater temperatures, consistent with rapidly flowing water localized within fractures.

drill↗
DOE OSTI2025

Dwarf galaxy halo masses from spectroscopic and photometric lensing in DESI and DES

We present the most precise and lowest-mass weak lensing measurements of dwarf galaxies to date, enabled by spectroscopic lenses from the Dark Energy Spectroscopic Instrument (DESI) and photometric lenses from the Dark Energy Survey (DES) calibrated with DESI redshifts. Using DESI spectroscopy from the first data release, we construct clean samples of galaxies with median stellar masses $\log_{10}(M_*/M_{\odot})=8.3-10.1$ and measure their weak lensing signals with sources from DES, KiDS, and SDSS, achieving detections with $S/N$ up to 14 for dwarf galaxies ($\log_{10}(M_*/M_{\odot})<$9.25) -- opening up a new regime for lensing measurements of low-mass systems. Leveraging DES photometry calibrated with DESI, we extend to a photometric dwarf sample of over 700,000 galaxies, enabling robust lensing detections of dwarf galaxies with combined $S/N=38$ and a significant measurement down to $\log_{10}(M_*/M_{\odot})=8.0$. We show that the one-halo regime (scales $\lesssim 0.15h^{-1}\rm Mpc$) is insensitive to various systematic and sample selection effects, providing robust halo mass estimates, while the signal in the two-halo regime depends on galaxy color and environment. These results demonstrate that DESI already enables precise dwarf lensing measurements, and that calibrated photometric samples extend this capability. Together, they pave the way for novel constraints on dwarf galaxy formation and dark matter physics with upcoming surveys like the Vera C. Rubin Observatory's LSST.

Treiber, Helena [Princeton U., Astrophys. Sci. Dep↗
DOE OSTI2025

Rancor Integrated Procedure System (RIPS): A Computer-Based Procedure Platform for Advanced Reactor Research

The Rancor Microworld Simulator is a simplified, pressurized water, small modular reactor simulator that includes a multi-unit plant model server, an advanced digital human-machine control interface, and the Rancor Integrated Procedure System (RIPS). Rancor provides a research and development tool that can be used for collecting operator performance data and for prototyping concepts of operations (ConOps) for advanced reactor development. RIPS is meant as a research tool and includes many unique features: (1) RIPS has a robust procedure authoring system. (2) RIPS has the capability to run any of the three IEEE-Std-1786 computer-based procedure types. (3) RIPS can be configured to take on the look and feel of different vendors’ computer-based procedure systems for the purpose of developing and evaluating different ConOps for plant upgrades or new builds. (4) RIPS includes the capability for logging operator procedure use, including integrating procedure logs with Rancor simulator logs, thereby allowing automated data collection of operator scenario runs. (5) RIPS integrates with the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), a dynamic human reliability analysis environment that creates a digital human twin or virtual operator to mimic reactor operator performance. (6) RIPS includes support for automation of plant monitoring and control functions. While RIPS is explicitly built into Rancor, it may also be used with full-scope training simulators. This functionality allows RIPS to be used for existing plants and advanced reactors under development.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Counter Unmanned Aircraft System Metrics Tool

SAND2023-05387O The Counter Unmanned Aircraft System (CUAS) Metrics Tool consists of three applications: • The Data Collection Tool is designed to be used during testing and captures details that can be imported into the Figure Creator application. • The Unmanned Aircraft System (UAS) Log Converter converts different UAS logs into a standardized hierarchical data format with a timeline of geospatial data. • The Figure Creator develops relevant images for the test event. The application includes logic to ingest and correlate data from the Data Collection Tool and the UAS Log Converter to produce the chosen figures. 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.

Mayle, Ashley↗
DOE OSTI2019

Bayesian and LASSO Regressions for Comparative Permeability Modeling of Sandstone Reservoirs

In this research, Bayesian model averaging (BMA) and least absolute shrinkage and selection operator regression (LASSO) algorithms were adopted for the core permeability modeling as a function of well log and core measurements. More specifically, the core permeability (dependent factor) was modeled given the well log and core data (independent variables) and then predicted in non-cored intervals of a well in a sandstone formation. The BMA is a stochastic linear modeling and a Bayesian parameter selection. Among 50 linear models generated as a function of the independent variables and depicted in Occam’s window, the best model of the highest posterior probability is determined. The best model has the optimal subset parameters that most influences the response factor. The core permeability modeling was again conducted using the LASSO algorithm, which adopts a completely different way of modeling and subset selection by using the penalized least-squared equation. Both BMA and LASSO resulted in a very accurate prediction of the core permeability by achieving perfect measured and calculated permeability matching. Matches for the two algorithms were quantified by computing the adjusted R-squared and root-mean-squared prediction error. In addition, results of the BMA and LASSO were compared to the conventional multiple linear regression (LM). The LASSO algorithm led to an accurate matching similar to LM, but slightly better than BMA. Therefore, both BMA and LASSO represent integrated procedures for accurate permeability estimation similar to conventional regression analysis.

58 GEOSCIENCES↗
DOE OSTI2019

Calibration of NMR porosity to estimate permeability in carbonate reservoirs

Considering the performance of carbon dioxide (CO 2 ) stored in geologic reservoirs requires some knowledge of the subsurface heterogeneity, where the most reliable data is extracted from wellbore measurements. This benefit is especially true for carbonate reservoirs were reactions between supercritical CO 2 , the reservoir brine, and carbonate minerals create new storage capacity and enhanced permeability. Techniques like NMR well logging hold promise for estimating subsurface permeability, because the total porosity and pore size can be calculated from response of 1 H NMR signal. Resulting depth profiles of porosity and permeability from downhole logs are useful input for reservoir simulations and can be used to design injection protocols and estimate storage capacity. We conducted a detailed characterization study to quantify the NMR response to matrix pore space, the connectivity to larger vugs and fractures, and the resulting permeability of core samples from distinct carbonate formations. The cores used in study span 5 orders in permeability, 4 orders in mean T 2 , and 2%–42% in porosity. The goal of the study was to use independent measures of pore volumes, surface relaxivity, fluid-accessible surface areas, and permeability to develop carbonate permeability models. It was not possible to develop a single model that reproduced permeability for different carbonate formations, because phenomena specific to the reservoirs could not be extracted even with the state-of-the-art characterization techniques used in this study. Estimates of permeability require calibration. Best calculations for a given formation were achieved by using a simple form of the Kozeny equation (Eq. 6): κ=Α’θ 3*n , and calibrating A’ and n with laboratory measurements made on the formation cores. Empirical calibrations can then be used to map the vertical heterogeneity of the storage complex to better understand the physical movement CO 2 and brine in the subsurface and the positive feedback between mineral dissolution and the development of flow pathways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Porous Colloidal Nanoparticles as Injectable Multimodal Contrast Agents for Enhanced Geophysical Sensing

Injecting fluids into underground geologic structures is crucial for the development of long-term strategies for managing captured carbon and facilitating sustainable energy extraction operations. Here, we have previously reported that the injection of metal–organic frameworks (MOFs) into the subsurface can enhance seismic monitoring tools to track fluids and map complex structures, reduce risk, and verify containment in carbon storage reservoirs because of their absorption capacity of low-frequency seismic waves. Here, we demonstrate that water-based Cr/Zn/Zr MOF colloidal suspensions (nanofluids) are multimodal geophysical contrast agents that enhance near-wellbore logging tools. Based on experimental fluid-only measurements, MIL-101(Cr), ZIF-8, and UiO-66 nanofluids have distinct complex conductivity and/or low-field nuclear magnetic resonance (NMR) signatures that are relevant to field-deployed technologies, implying the potential to enhance near-wellbore monitoring of CO 2 injection and associated processes with downhole logging tools. Small- and wide-angle X-ray scattering characterization of ~0.5 wt % MIL-101(Cr) suspensions confirmed phase stability and provided insight into the fractal nature of colloidal nanoparticles. Finally, low-field (2 MHz) NMR measurements of MIL-101(Cr) nanofluid injection into a prototypical Berea sandstone demonstrate how paramagnetic high-surface area MOFs may dominate the relaxation times of hydrogen-bearing fluids in porous geologic matrices, enhancing the mapping of near-surface and near-wellbore transport pathways and advancing sustainable subsurface energy technologies.

NMR↗
DOE OSTI2024

Comparing gas composition from fast pyrolysis of live foliage measured in bench-scale and fire-scale experiments

Background: Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims: Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods: Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO 2 , CH 4 , H 2 , C 6 H 6 O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results: Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO 2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion: Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications: These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The luminosity function at z ∼ 8 from 97 Y-band dropouts: Inferences about reionization

We present the largest search to date for Y-band dropout galaxies (z ∼ 8 Lyman break galaxies, LBGs) based on 350 arcmin{sup 2} of Hubble Space Telescope observations in the V, Y, J, and H bands from the Brightest of Reionizing Galaxies (BoRG) survey. In addition to previously published data, the BoRG13 data set presented here includes approximately 50 arcmin{sup 2} of new data and deeper observations of two previous BoRG pointings, from which we present 9 new z ∼ 8 LBG candidates, bringing the total number of BoRG Y-band dropouts to 38 with 25.5 ≤ m{sub J} ≤ 27.6 (AB system). We introduce a new Bayesian formalism for estimating the galaxy luminosity function, which does not require binning (and thus smearing) of the data and includes a likelihood based on the formally correct binomial distribution as opposed to the often-used approximate Poisson distribution. We demonstrate the utility of the new method on a sample of 97 Y-band dropouts that combines the bright BoRG galaxies with the fainter sources published in Bouwens et al. from the Hubble Ultra Deep Field and Early Release Science programs. We show that the z ∼ 8 luminosity function is well described by a Schechter function over its full dynamic range with a characteristic magnitude M{sup ⋆}=−20.15{sub −0.38}{sup +0.29}, a faint-end slope of α=−1.87{sub −0.26}{sup +0.26}, and a number density of log{sub 10} ϕ{sup ⋆}[Mpc{sup −3}]=−3.24{sub −0.24}{sup +0.25}. Integrated down to M = –17.7, this luminosity function yields a luminosity density log{sub 10} ϵ[erg s{sup −1} Hz{sup −1} Mpc{sup −3}]=25.52{sub −0.05}{sup +0.05}. Our luminosity function analysis is consistent with previously published determinations within 1σ. The error analysis suggests that uncertainties on the faint-end slope are still too large to draw a firm conclusion about its evolution with redshift. We use our statistical framework to discuss the implication of our study for the physics of reionization. By assuming theoretically motivated priors on the clumping factor and the photon escape fraction we show that the UV luminosity density from galaxy samples down to M = –17.7 can ionize only 10%-50% of the neutral hydrogen at z ∼ 8. Full reionization would require extending the luminosity function down to M = –15. The data are consistent with a substantial fraction of neutral hydrogen at z > 7, in agreement with recent suggestions based on deep spectroscopy of z ∼ 8 LBGs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Utah FORGE: Well Data for Student Competition

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

15 GEOTHERMAL ENERGY↗
DOE 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 OSTI2024

Characterization of Pliocene and Miocene Formations in the Wilmington Graben, Offshore Los Angeles, for Large-Scale Geologic Storage of CO2

The project Characterization of Pliocene and Miocene Formations in the Wilmington Graben, Offshore Los Angeles, for Large-Scale Geologic Storage of CO2 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 Los Angeles Basin presents an opportunity for large-scale geologic CO2 storage. Due to its large population and historical and geologic setting as one of the most prolific oil and gas producing basins in the United States, the region is home to more than 12 major power plants and oil refineries that produce more than 5 million metric tons of fossil fuel-related CO2 emissions each year. GeoMechanics Technologies worked to characterize the Pliocene and Miocene sediments in the Wilmington Graben, offshore of Los Angeles, California, for high-volume CO2 storage. The Graben is located offshore of the Los Angeles and Long Beach Harbor area, making it accessible yet geologically isolated from the nearby Wilmington oilfield and onshore areas. These sediments span more than 5,000 feet of vertical interval with an estimated storage resource of more than 100 million metric tons of CO2. The project team analyzed and interpreted existing geologic data within the region, including detailed exploration well log data and 2-D and 3-D seismic data. New seismic lines were acquired to fill in current data gap areas and two new characterization wells were drilled and logged. This information was integrated with existing geologic interpretations for adjacent onshore areas to help characterize optimal areas for CO2 storage and seals to safely store CO2. Integrated 3-D geologic and geomechanical models for the Wilmington Graben were developed to simulate the fate and transport of injected CO2 in the subsurface and to assess risks. This project contributed to the understanding of injectivity, containment mechanisms, rate of dissolution and mineralization, and storage capacity of the Wilmington Graben and associated analogous basins. This effort also provided greater insight into the potential for offshore geologic formations to safely and permanently store CO2.

.las↗
DOE OSTI2024

Characterization of the Triassic Newark Basin of New York and New Jersey for Geologic Storage of Carbon Dioxide

The project Characterization of the Triassic Newark Basin of New York and New Jersey for Geologic Storage of Carbon Dioxide 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. Sandia Technologies, LLC, and co-investigator Conrad Geoscience Corporation, examined the potential for large-scale, permanent CO2 storage in sedimentary strata within the Newark Rift Basin. The Newark Rift Basin underlies an industrialized, developed region comprising parts of New York, New Jersey, and Pennsylvania. The project characterized and investigated the suitability of Triassic age sedimentary formations for potential geologic CO2 storage. The project team drilled and cored two test wells to define the sedimentary geologic formations underlying the basin and to document or reach basement rock. With this geologic characterization phase, an integration of seismic, geologic, borehole, and formation core results provided a higher resolution assessment of CO2 storage potential. The Stockton Formation is known to be a potentially favorable geologic storage formation in the basin. In 2011, the 1-NYSTA Tandem Lot stratigraphic test well was drilled to a depth of 6,855 feet in the northern portion of the Newark Basin in southern New York State. Approximately 9 miles south-southeast on the Lamont Doherty Campus, TW-4 was drilled and cored in 2013 to a depth of 1,802 feet and contacted apparent igneous basement at a depth of 1,712 feet. Both wells penetrated the Palisades Sill ranging from 800 feet thick in the eastern well to approximately 1,800 feet in thickness at the 1-NYSTA Tandem Lot deep drill site. A diabase sill can provide an excellent seal and dense confining layer for potential CO2 storage reservoirs and flow layers that are situated beneath it within the Stockton Sandstone. The Stockton Sandstone was encountered beneath the sill in the TW-4 well on the Lamont campus, and data integration suggests that it was likely observed near total depth in the deep 1-NYSTA Tandem Lot well. The test wells confirm and define reservoirs are present beneath the sill and offer CO2 storage potential. The integration of geologic and reservoir characterization of well logs, formation cores, and formation fluids indicated Triassic age lacustrine playa lake and mudbank shales of the Upper Passaic Group can provide an effective seal for the porous and permeable underlying sandstone reservoir layers. This project acquired seismic data, drilled borehole well logs, acquired core samples, and integrated these findings to provide a better understanding of the subsurface geologic formations in the Newark Rift Basin. These findings have contributed to a higher degree of accuracy in predicting potential geologic storage opportunities, while refining geologic storage capacity estimates for the indicated reservoirs and flow units.

.las↗
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 OSTI2018

Fracture Diagnostics Using Low Frequency Electromagnetic Induction and Electrically Conductive Proppants

The monitoring and diagnostics of induced fractures are important for the real-time performance evaluation of hydraulic fracturing operations. Previous electromagnetic (EM) based studies show that single backbone tri-axial induction logging tools are promising candidates for real-time monitoring and diagnosis of fractures in open-hole wells. To support the development of field deployable tools, the concept must be tested in experiments, in a controllable environment, before it is tested under field-like conditions. To this end, we have developed numerical tools which can simulate any wellbore environment while logging hydraulic fractures with the induction tool. We have designed and built a prototype induction tool and performed two sets of tests to compare with numerical simulation results. The computational and experimental setup consists of tri-axial transmitter- and receiver-coils in co-axial, co-planar and cross-polarized configurations. Both lab and shallow earth measurements are shown to be in good agreement with simulations for all examined cases. The average relative and maximum discrepancies of the measured signals from the simulated ones were lower than 3% and 10%, respectively. With the prototype tool, strong signals sensitive to the fracture’s surface area and dip-angle were measured in co-axial coil configuration, while weaker signals sensitive to the fracture’s aspect ratio were observed in the co-planar configuration. Cross-polarized signals are also shown to be strong and sensitive to the fracture’s dip. Lastly, we resolved the detectable components of the signal tensor to obtain parameters of simplified fracture geometries. The inversion algorithm, a derivative free directional search model, uses an objective function defined as a combination of co-axial and cross-polarized signals from different tool spacings to provide a well behaved global minimum. The robustness of inversion algorithm is tested on synthetic data for single cluster fractures in homogeneous and heterogeneous background electrical conductivity. All the effective model parameters for different cases, fracture conductivity (width), propped area (length) and dip-angle, are shown to be recovered with sufficient accuracy. We also evaluated the effect of multiple neighboring fractures and suggested a multi-cluster inversion algorithm that can accurately recover the proppant distribution in each fracture. Based on these results we provide a downhole tool design, with detailed specifications, that can effectively recover far-field proppant distribution in hydraulic fractures.

02 PETROLEUM↗
DOE OSTI2016

THE COMPLEX CIRCUMNUCLEAR ENVIRONMENT OF THE BROAD-LINE RADIO GALAXY 3C 390.3 REVEALED BY CHANDRA HETG

We present the first high spectral resolution X-ray observation of the broad-line radio galaxy 3C 390.3 obtained with the high-energy transmission grating spectrometer on board the Chandra X-ray Observatory . The spectrum shows complex emission and absorption features in both the soft X-rays and Fe K band. We detect emission and absorption lines in the energy range E = 700–1000 eV associated with ionized Fe L transitions (Fe XVII–XX). An emission line at the energy of E ≃ 6.4 keV consistent with the Fe K α is also observed. Our best-fit model requires at least three different components: (i) a hot emission component likely associated with the hot interstellar medium in this elliptical galaxy with temperature kT = 0.5 ± 0.1 keV; (ii) a warm absorber with ionization parameter log ξ = 2.3 ± 0.5 erg s{sup −1} cm, column density log N {sub H} = 20.7 ± 0.1 cm{sup −2}, and outflow velocity v {sub out} < 150 km s{sup −1}; and (iii) a lowly ionized reflection component in the Fe K band likely associated with the optical broad-line region or the outer accretion disk. These evidences suggest the possibility that we are looking directly down the ionization cone of this active galaxy and that the central X-ray source only photoionizes along the unobscured cone. This is overall consistent with the angle-dependent unified picture of active galactic nuclei.

79 ASTRONOMY AND ASTROPHYSICS↗