Future Carbon Sequestration in the Alabama Fold and Thrust Belt
Future Carbon Sequestration in the Alabama Fold and Thrust Belt
58 GEOSCIENCES↗
SEARCH · Engineering Papers
Results for “Carbon storage”
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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Improving the Long-term Cycle Performance of xLi 2 MnO 3 ·(1-x)LiMeO 2 /Li 4 Ti 5 O 12 Cells via Prelithiation and Electrolyte Engineering
Toward the development of high energy density and long lifetime batteries for behind-the-meter storage (BTMS) applications, Li- and Mn-rich layered oxide cathode (xLi 2 MnO 3 ·(1-x)LiMeO 2 , Me = Ni, Mn, and etc., LMR-NM) and Li 4 Ti 5 O 12 (LTO) anode system was examined. To mitigate the major degradation mechanisms at each electrode (i.e., loss of Li inventory (LLI) at the anode and transition metal dissolution and oxygen release at the cathode), two approaches were taken—prelithiating the LTO electrode and varying the electrolyte solvent compositions. The effect of prelithiation and electrolyte engineering on the long-term cycle performance of LMR-NM/LTO cells were systematically evaluated via electrochemical analyses and post-mortem characterizations. By using a prelithiated LTO anode and supplying additional Li to the system, the capacity retention of LMR-NM/LTO system was improved. The degree of enhancement was dependent on the types of electrolytes used, as their decomposition products determined the level of LLI. With increased capacity retention, however, the cathode was utilized to a greater extent, resulting in more severe loss of the cathode active material. Thus, all degradation mechanisms should be considered comprehensively when designing high performance LMR-NM/LTO cells to account for their complex interplay.
25 ENERGY STORAGE↗
Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization
Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. Here, we observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO 2 reservoir prospecting, reserve estimation, simulation, etc.
3D seismic↗
Coarse woody debris decomposition assessment tool: Model development and sensitivity analysis
Coarse woody debris (CWD) is an important component in forests, hosting a variety of organisms that have critical roles in nutrient cycling and carbon (C) storage. We developed a process-based model using literature, field observations, and expert knowledge to assess woody debris decomposition in forests and the movement of wood C into the soil and atmosphere. The sensitivity analysis was conducted against the primary ecological drivers (wood properties and ambient conditions) used as model inputs. The analysis used eighty-nine climate datasets from North America, from tropical (14.2° N) to boreal (65.0° N) zones, with large ranges in annual mean temperature (26.5°C in tropical to -11.8°C in boreal), annual precipitation (6,143 to 181 mm), annual snowfall (0 to 612 kg m -2 ), and altitude (3 to 2,824 m above mean see level). The sensitivity analysis showed that CWD decomposition was strongly affected by climate, geographical location and altitude, which together regulate the activity of both microbial and invertebrate wood-decomposers. CWD decomposition rate increased with increments in temperature and precipitation, but decreased with increases in latitude and altitude. CWD decomposition was also sensitive to wood size, density, position (standing vs downed), and tree species. The sensitivity analysis showed that fungi are the most important decomposers of woody debris, accounting for over 50% mass loss in nearly all climatic zones in North America. The model includes invertebrate decomposers, focusing mostly on termites, which can have an important role in CWD decomposition in tropical and some subtropical regions. The role of termites in woody debris decomposition varied widely, between 0 and 40%, from temperate areas to tropical regions. Woody debris decomposition rates simulated for eighty-nine locations in North America were within the published range of woody debris decomposition rates for regions in northern hemisphere from 1.6° N to 68.3° N and in Australia.
54 ENVIRONMENTAL SCIENCES↗
Hydrogeologic Model Based on Reduced Complexity
This data set provides the basic hydrogeologic model used for development of the TOUGH2 reservoir modeling results for the Kimberlina site. The hydrogeologic model is based on the static geologic model provided as a separate data set but has some simplifications regarding the geologic layers. Some geologic layers have been merged for the hydrogeologic model because the hydrogeologic properties were very similar. The data set contains several files. The word file explains the structure of the data files. The pdf contains a table with the hydrogeologic properties. The data set also includes 15 thickness files for 15 geological layers (including 3 Vedder sand layers and 3 Vedder shale layers), three elevations files (with reference elevation at ground surface/model top at 9000 m), and one fault trace file for seven faults. Most users may only need 2-3 faults close to the injection well (also see the figure). The arrangement of these layer thickness and elevations files can be see from the three layer files (for Tecplot). To develop a grid structure, start with the Vedder top elevation (REFVedderTop.dat) and add/subtract the thicknesses of layers above/below.
3D Groundwater Model↗
Fracture Shearing in the Eau Claire Formation
This data set consists of Computed Tomographic (CT) data for five sheared Eau Claire Formation core samples with complex and heterogeneous lithology. Each sample was prefractured, housed in a specially adapted core holder, and sheared in incremental fashion. CT scans were taken before and after each shearing event.
Computed Tomography↗
Wellington2-32-core
Data from the Wellington 2-32 well described in Schmitt, R.; Paronish, T.; Crandall, D.; Moore, J.; Hasiuk, F.; Potter, N.; Holubnyak, Y. E. Computed Tomography Scanning and Petrophysical Measurements of the Wellington KGS 2-32 Core; DOE/NETL-2022/2874; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2022; p 61. DOI: 10.2172/182XXX99
Carbon Storage↗
CT and Scanning Data of the KGS Wellington 1-32 CarbonSAFE Core
Data described in the NETL Technical Report Series "Computed Tomography Scanning and Geophysical Measurements of the Wellington 1-32 Core", including processed and raw CT and measurements from NETL's multi-sensor core logger. Paronish, T.; Schmitt, R.; Mitchell, N; Brown, S; Crandall, D.; Moore, J.; Hasiuk, F.; Potter, N.; Holubnyak, Y.E. Computed Tomography Scanning and Geophysical Measurements of the Wellington 1-32 Core; DOE.NETL-2021.2882; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2022; p 108. DOI: https://doi.org/10.2172/1894355 https://edx.netl.doe.gov/dataset/computed-tomography-scanning-and-geophysical-measurements-of-the-wellington-1-32-core
CarbonSafe↗
Patterson #5-25 Well
CT scans, images, and MSCL data to accompany the Patterson #5-25 well TRS
Arbuckle Group; CCUS; Carbon storage; IMSCS↗
ISGS Seal wells
Computed tomography and core logger data described in Technical Report "Computed Tomography Scanning and Geophysical Measurements of the CarbonSAFE Seal Integrity Wells in the Illinois Basin" by Paronish et al., 2023
carbon storage↗
RokBase - Beta
Web based tool and portal to explore and download data associated with Core Characterization Technical Report Series .
Core Characterization↗
ISGS CarbonSAFE Lively Grove Core
Computed tomography and core logging data from the Lively Grove #1 Well core drilled by the Illinois State Geological Survey in 2021 under DE-FE0031892. Data description can be found in: Crandall, D.; Paronish, T.; Mitchell, N.; Jarvis, K.; Brown, S.; Moore, J.; Gill, M.; Blakley, C.; Okwen, R.; Korose, C.; Carman, C. Computed Tomography Scanning and Petrophysical Measurements of the Lively Grove #1 Well Core; DOE/NETL-2023/3877; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2023; p 61.
CarbonSafe↗
J&L #1 Well-Pennsylvania Oriskany Characterization
Dataset for this database page represents data from technical report "Computed Tomography Scanning and Geophysical Measurements of Appalachian Basin Core from the Jones and Laughlin #1 Well, Beaver County PA " Sharma, M; Paronish, T.; Mitchell, N.; Crandall, D.; Neubaum, J. Computed Tomography Scanning and Geophysical Measurements of Appalachian Basin Core from the Jones and Laughlin #1 Well, Beaver County PA; DOE.NETL-2023.3889; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2023; p 37.
Appalachian Basin↗
SMART CarbonSAFE Model
The modeling work for this project built upon previous work done for the CarbonSAFE Rocky Mountain Phase I. A geologic and dynamic model was built in Petrel version 2019.3 for the area of the Colorado Plateau near Price, Utah. It is located at the north end of the San Rafael Swell in a natural gas field called Drunkards Wash. The model is 61.5 km east-west and 53 km north to south with 123x106x21 cells, giving a cell dimension of 50x50 m2. The 21 layers in the Z direction are of varying thickness depending on the formation. The Carmel, the seal layer, has 3 layers and is about 200 meters thick around the injection well. The Navajo Sandstone, the principal reservoir, has 5 layers and is about 100 meters thick. The Kayenta has 5 layers and is roughly 50 meters thick and the Wingate, a secondary reservoir, has 5 layers and is 120 meters thick. The base of the model is the Chinle Formation with 3 layers and about 120 meters of thickness..
Carbon Dioxide↗
Herren Well - Ohio
CT Data that compliments the Technical Report Series document: Pohl, M.; Paronish, T.; Mitchell, N.; Jarvis, K.; Sharma, M.; Moore, J.; Crandall, D.; Danielsen, E. M.; McDonald, J. Computed Tomography Scanning and Petrophysical Measurements of Oriskany Core Across Eastern Ohio, U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2024
Carbon Storage↗
CT and Geophysical Data of Clinton Sandstone Cores from Ohio
Collection of computed tomography and multi-sensor core logger data of 12 wells in Ohio that intersect the Clinton Sandstone formation. This data is described along with well information in a technical report series document: Paronish, T.; Holleran, A.; Pohl, M.; Crandall, D.; Jarvis, K.; Workman, S.; Drosche, J.; McKisic, T.; Collins, C.; Thomas, M.; McDonald, J. Computed Tomography Scanning and Geophysical Measurements of the Clinton Sandstone in Ohio; DOE.NETL-2025.4949; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, 2025; p 68. https://doi.org/10.2172/2589262
AS↗
Geochronology of Cambrian Sedimentary and Volcanic Rocks in the Illinois Basin: Defining the Illinois Aulacogen
The Wabash #1 well, drilled for the Wabash CarbonSAFE Project and located in Vigo County, Indiana, USA, was drilled in early 2020 as a stratigraphic test well to characterize and evaluate the basal Cambrian Mt. Simon Sandstone for carbon dioxide storage (TD=8750 ft; 2667 m). The Wabash #1 well is located along the eastern flank of a newly interpreted Cambrian aulacogen that occurs in western Indiana and eastern Illinois. Here we present 938 new detrital zircon U-Pb ages (LA-ICPMS) from early Cambrian sandstones sampled near the base of the well. A basalt lava flow was penetrated at ~8530 ft (2600 m) and has an 40 Ar/ 39 Ar age of 525.03 +/- 1.10 Ma, which represents the first known Cambrian crust in the Illinois Basin. The two sandstone samples from beneath the basalt are dominated by zircons derived from the Midcontinent Granite-Rhyolite terrane. The sandstone samples from above the basalt reflect a mixture of these locally derived Mazatzal and Granite-Rhyolite terrane zircons, but also distal Archean, Grenville, and Yavapai zircons. Each sample has small numbers of Cambrian zircons, which is consistent with those in basal Cambrian sandstones in other deep wells to the west. These early Cambrian detrital zircons and early Cambrian age of the basalt, combined with sediment thickness patterns permit the interpretation of the Illinois aulacogen, which formed during the final stage of Rodinian rifting.
58 GEOSCIENCES↗
The Effect of Compressibility and Outer Boundaries on Incipient Viscous Fingering
The knowledge of the effects of instability and heterogeneity on displacements, primarily enhanced oil recovery, and carbon dioxide storage are well known, although they remain difficult to predict. Here, the usual recourse to modeling these effects is through numerical simulation. Simulation remains the gold standard for prediction; however, its results lack generality, being case-specific. There are also several analytic models for displacements that are usually more informative than simulation results. However, these methods apply to steady-state, incompressible flow.
02 PETROLEUM↗