Report on geologic carbon sequestration opportunities in the Mount Simon Sandstone with information on geology and stratigraphy, core analyses and geophysical logs, variation in the reservoir, porosity, permability, and storage capacity.

Analysis of Unit Mobility Ratio Well-to-Well Tracer Flow to Determine Reservoir Heterogeneity DOE/SF/11564-1

Conchas Reservoir Storage Capacity

NIPER-540

Petrographic and Analytical Results

FRACGEN/NFFLOW is a DOE sponsored project to simulate the behavior of tight, fractured, strata-bound reservoirs that arise from irregular, discontinuous, or clustered networks of fractures. This distribution includes the PC programs and user interfaces for fracture network generation, discrete fracture reservoir simulation, and visualization of fracture networks and reservoir performance. New features in this release are optional fixed pressure boundary conditions, permeable unfractured layers, liquid data handling, sorption, and stress sensitive aperture modeling.

The objective of this project is to develop a comprehensive, interdisciplinary, and quantitative characterization of a fluvial-deltaic reservoir which will allow realistic inter-well and reservoir-scale modeling to be constructed for improved oil-field development in similar reservoirs world-wide. The geological and petrophysical properties of the Cretaceous Ferron Sandstone in east-central Utah will be quantitatively determined. Both new and existing data will be integrated into a three-dimensional representation of spatial variations in porosity, storativity, and tensorial rock permeability at a scale appropriate for inter-well to regional-scale reservoir simulation. Results could improve a reservoir management through proper infill and extension drilling strategies, reduction of economic risks, increased recovery from existing oil fields, and more reliable reserve calculations. Transfer of the project results to the petroleum industry is an integral component of the project.

This is a comprehensive collection of all original surface and subsurface technical data, as well as various derivative subsurface models, collected from the FutureGen 2.0 project. This collection of data has been vetted for confidential or sensitive documents.

Online web mapping tool for visualization and simple analysis of Earth-energy data files from public and DOE related sources. Geocube allows users to upload and visualize their own datasets but also comes preloaded with individual spatial datasets as well as spatial data collections that align to topical themes.

Dataset is a compilation of datapoints in the US waters of the Gulf of Mexico where BOEM has released data for oil and gas reservoirs. These reservoirs are typically in sand formations so the name of the dataset is often called "Sands" and the year of the latest release of data from BOEM. To be able to view data spatially, the sand dataset was joined to the BOEM Boreholes dataset by matching the API numbers of the discovery wells. Thus the "sands" are an estimated location below the mudline and are not exact.

NHD_MajorAreas are a subset of the largest double banked stream/river polygon features selected from the High Resolution NHD dataset for Washington State. This subset includes only NHDAreas that have an associated NHD_MajorStream. NHD_MajorStreams are classified as those that have GNIS Names that include "River", or have a Stream Order > 7, or have a GNIS Name that includes "Creek" and is longer than 24 km. NHD_MajorAreas have an NHD_MajorStream flowing through it.The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. This high-resolution NHD, generally developed at 1: 12,000/1:24,000 scale, adds detail to the original 1:100,000-scale NHD. Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Updated March 2019

NHD_MajorStreams are a subset of the largest linear stream/river features selected from the High Resolution NHD dataset for Washington State. This subset includes only NHDflowlines that have GNIS Names that include "River", or have a Stream Order > 7, or have a GNIS Name that includes "Creek" and is longer than 24 km. The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. This high-resolution NHD, generally developed at 1:12,000/1:24,000 scale, adds detail to the original 1:100,000-scale NHD Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Updated March 2019

NHD_MajorWaterbodies are a subset of the largest waterbody features selected from the High Resolution NHD dataset for Washington State. This subset includes only waterbodies that are classified as lake/ponds ,reservoirs, or estuaries and that are > 1 sqKm (10763900 sqft).The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. This high-resolution NHD, generally developed at 1:12,000/1:24,000 scale, adds detail to the original 1:100,000-scale NHD Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.

Managing deliveries in the river basins in New Mexico is a critical function for the Interstate Stream Commission. Staff analyze, review, and implement projects in New Mexico and analyze stream flow, reservoir levels, and other data on stream systems.

The 1944 Water Treaty requires that the International Boundary Water Commission (IBWC) keep a record of the Rio Grande waters belonging to each country. This site contains daily Rio Grande flow conditions and reservoir reports.

The goal of this three-year project was to provide a quantitative definition of reservoir heterogeneity. This objective was accomplished through the integration of geologic, geophysical, and engineering databases into a multi-disciplinary understanding of reservoir architecture and associated fluid-rock and fluid-fluid interactions. This interdisciplinary effort integrated geological and geophysical data with engineering and petrophysical results through reservoir simulation to quantify reservoir architecture and the dynamics of fluid-rock and fluid-fluid interactions. An improved reservoir description allows greater accuracy and confidence during simulation and modeling as steps toward gaining greater recovery efficiency from existing reservoirs. A field laboratory, the Sulimar Queen Unit, was available for the field research. Several members of the PRRC staff participated in the development of improved reservoir description by integration of the field and laboratory data as well as in the development of quantitative reservoir models to aid performance predictions. Subcontractors from Stanford University and the University of Texas at Austin (UT) collaborated in the research and participated in the design and interpretation of field tests. The three-year project was initiated in September 1993 and led to the development and application of various reservoir description methodologies. A new approach for visualizing production data graphically was developed and implemented on more »the Internet. Using production data and old gamma rays logs, a black oil reservoir model that honors both primary and secondary performance was developed. The old gamma ray logs were used after applying a resealing technique, which was crucial for the success of the project. In addition to the gamma ray logs, the development of the reservoir model benefitted from an inverse Drill Stem Test (DST) technique which provided initial estimates of the reservoir permeability at different wells.

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. 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: CO2 Saturation Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models-part-3); 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 – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models-part-3); Vs Velocity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models-part-3); and Density Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models-part-3). 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: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models); [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). 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.

Comprehensive characterization of carbon capture utilization and storage (CCUS) opportunities throughout the ten-state region. Spanning from the offshore Atlantic Coastal Plain through the Appalachian and Michigan basins, this region hosts a diverse assemblage of reservoir types and provides multiple CCUS targets. A key component of this research is the evaluation of opportunities for enhanced oil recovery (EOR) in legacy oil fields via carbon dioxide (CO2) floods. The latest phase of MRCSP research added several new attributes to an already comprehensive database in order to identify and rank the best opportunities for CO2-EOR throughout the 10-state region. Detailed reservoir parameters are necessary to perform a comprehensive evaluation of any given EOR target, and a ranking of opportunities depends on both availability of data and relative consideration, or weight, assigned to the various attributes. A renewed focus on CO2-EOR also helped to identify information severely lacking in the MRCSP region, such as permeability and oil gravity.

Measuring and Predicting Reservoir Heterogeneity in Complex Deposystems, Annual Report; August 1992

Includes ET Toolbox, Cochiti, Albuquerque, Belen, and Socorro division readings, 24 hour rainfall, reservoir storage, and snowpack readings and information.

Currently, users can either view this data directly in a web browser, though this can be confusing to users who do not understand the SensorThings API (https://newmexicowaterdata.org/faq/#sensorthingsapi) structure. Users who have some programming knowledge can also query this data with the Python programming language following this tutorial (https://developer.newmexicowaterdata.org/help). Development is currently underway for applications that more easily allow general users to query and visualize this data.

Petrographic and Reservoir Quality Assessment Dolostone and Limestone Devonian Aged Formations - Zama Field Area Twp 116-117; Rge05-06W6 Volume I - Text; Volume II - Tables, Figures, Micrographs

Petrographic and Reservoir Quality Assessment Dolostone and Limestone Muskeg and Zama Formations 05-10-117-04W6

DOE/BC/14893-6

DOE/BC/14893-10

Report on Reservoir Condition CO2-Brine Drainage and Imbibition Relative Permeability Displacement Characteristics in the Zama Area, Muskeg Anhydrite Formation (Caprock)

Hycal Energy Research Laboratories Ltd. (Hycal) conducted a series of reservoir characterization and core displacement tests on a sample of intergranular Sulphur Point formation core material taken from well 08-13-116-06 W6M (1370.10 m interval) in the Zama field in northwestern Alberta. The objective of the program was to determine the reservoir condition drainage (water saturation decreasing) and imbibition (water saturation increasing) relative permeability displacement characteristics for carbon dioxide and formation brine for the Sulphur Point carbonate formation.

Vertical Seismic Profile data collected in 2009 and 2010 as part of SECARB Phase III Early Test at Cranfield oilfield in Mississippi to determine CO2 induced change from seismic response. Data divided into 3D VSP and Offset VSP folders. Associated Publications: Daley, T. M., Hendrickson, J., & Queen, J. H. (2014). Monitoring CO2 Storage at Cranfield, Mississippi with Time-Lapse Offset VSP – Using Integration and Modeling to Reduce Uncertainty. Energy Procedia, 63, 4240-4248. doi:10.1016/j.egypro.2014.11.459

Borehole gravity measurements obtained during the SECARB project at the Cranfield oil site in Mississippi from CFU31-F2 and CFU31-F3 wells. Data was used to calculate density changes within the Cranfield reservoir and to test borehole gravity performance compared to a variety of other methods for monitoring the injected CO2 plume. Associated Publications: Dodds, K., Krahenbuhl, R., Reitz, A., Li, Y., Hovorka, S. D., 2013, Evaluation of time lapse borehole gravity for CO2 plume detection SECARB Cranfield: International Journal of Greenhouse Gas Control.

Core photos and analysis collected from Cranfield oilfield in southwest Mississippi as part of SECARB project. Cores are from CFU31F-1, CFU31F-2, and CFU31F-3 wells in the Detailed Area of Study. Data includes permeability and porosity measurements and gamma ray scans. Associated Publications: Kordi, M., 2013, Characterization and prediction of reservoir quality in chlorite-coated sandstones: evidence from the Late Cretaceous Lower Tuscaloosa Formation at Cranfield Field, Mississippi, U.S.A., The University of Texas at Austin, Ph.D. dissertation, 193 p.

Distributed Temperature Sensing data files collected during the SECARB project from Detailed Area of Study wells (CFU F-1, F-2, F-3) at Cranfield oil site in Mississippi. Associated Publications: Nuñez-López, V., Muñoz-Torres, J., and Zeidouni, M., 2014, Temperature monitoring using distributed temperature sensing (DTS) technology: Energy Procedia, v. 63, p. 3984–3991, doi:10.1016/j.egypro.2014.11.428.

Electrical Resistance Tomography Data collected as part of SECARB project at Cranfield oil site in Mississippi. Associated Publications: Carrigan, C.R., Yang, X., LaBrecque, D.J., Larsen, D., Freeman, D., Ramirez, A.L., Daily, W., Aines, R., Newmark, R., Friedmann, S. J., Hovorka, S., 2013. Electrical resistivity tomographic monitoring of CO2 movement in deep geologic reservoirs. Int. J. of Greenhouse Gas Control, 18, 401-408. Yang, X., Chen, X., Carrigan, C.R. & Ramirez, A.L., 2014. Uncertainty quantification of CO2 saturation estimated from electrical resistance tomography data at the Cranfield site, Int J Greenh Gas Con, 27, 59-68.

Hydrotest, gas composition, injection fluid, mass spectrometer, and U-tube gas sample analysis data gathered during SECARB project at Cranfield oil site in Mississippi. Geochemical data collected as part of geologic characterization phase of SECARB. Associated Publications: Lu, J., Cook, P. J., Hosseini, S. A., Yang, C., Romanak, K. D., Zhang, T., Freifeld, B. M., Smyth, R. C., Zeng, H., and Hovorka, S. D., 2012, Complex fluid flow revealed by monitoring CO2 injection in a fluvial formation: Journal of Geophysical Research, v. 117, B03208, doi:10.1029/2011JB008939. Lu, J., Kharaka, Y. K., Thordsen, J. J., Horita, J., Karamalidis, A., Griffith, C., Hakala, J. A., Ambats, G., Cole, D. R., Phelps, T. J., Manning, M. A., Cook, P. J., and Hovorka, S. D., 2012, CO2‒rock‒brine interactions in Lower Tuscaloosa Formation at Cranfield CO2 sequestration site, Mississippi, U.S.A.: Chemical Geology, v. 291, p. 269‒277. Yang, C., Hovorka, S. D., Treviño, R. H., and Delgado-Alonso, J., 2015, Integrated framework for assessing impacts of CO2 leakage on groundwater quality and monitoring-network efficiency: case study at a CO2 enhanced oil recovery site: Environmental Science &Technology, v. 49, p. 8887–8898, doi:10.1021/acs.est.5b01574.

Thin sections, sedimentary graphic logs, and XRD results from CFU31-F2 and CFU31-F3 wells. Data collected as part of geologic characterization phase of SECARB project at the Cranfield oilfield in southwest Mississippi. Thin sections for CFU29-12 well also included. Associated Publications: Kordi, M., 2013, Characterization and prediction of reservoir quality in chlorite-coated sandstones: evidence from the Late Cretaceous Lower Tuscaloosa Formation at Cranfield Field, Mississippi, U.S.A., The University of Texas at Austin, Ph.D. dissertation, 193 p.

Bottom-hole, above zone monitoring interval, and injection zone pressure data collected during the SECARB project in Cranfield, Mississippi to assess the relationship between pressure field and multiphase field. Submission includes 10-second interval data from Detailed Area of Study wells: CFU31-F1 (injector), CFU31-F2 (observation), CFU31-F3 (observation) and Ella G Lees no. 7 (observation) well located west of the DAS. Associated Publications: Joy, C. A., 2011, The effects of pressure variation and chemical reactions on the elasticity of the lower Tuscaloosa sandstone of the Cranfield Field Mississippi, The University of Texas at Austin, Master’s thesis, 97 p. Kim, S., and Hosseini, S. A., 2013, Above-zone pressure monitoring and geomechanical analysis of a field scale CO2 injection, Cranfield Mississippi, Greenhouse Gases: Science and Technology, doi:10.1002/ghg.1388. Kim, S., and Hosseini, S. A., 2017, Study on the ratio of pore-pressure/stress changes during fluid injection and its implications for CO2 geologic storage: Journal of Petroleum Science and Engineering, v. 149, p. 138-150, doi:10.1016/j.petrol.2016.10.037. Mathias, S. A., Gluyas, J. G., Gonzalez Martinez de Miguel, G. J., and Hosseini, S. A., 2011, Role of partial miscibility on pressure buildup due to constant rate injection of CO2 into closed and open brine aquifers: Water Resources Research, v. 47, W12525, 11 p., doi:10.1029/2011WR011051. Meckel, T. A., Zeidouni, M., Hovorka, S. D., and Hosseini, S.A., 2013, Assessing sensitivity to well leakage from three years of continuous reservoir pressure monitoring during CO2 injection at Cranfield, MS, USA: International Journal of Greenhouse Gas Control, [insert volume no., page numbers], doi:10.1016/j.ijggc.2013.01.019. Nicot, J.-P., Oldenburg, C. M., Bryant, S. L., and Hovorka, S. D., 2009, Pressure perturbations from geologic carbon sequestration: area-of-review boundaries and borehole leakage driving forces, in Energy Procedia (v. 1, no.1), Proceedings of 9th International Conference on Greenhouse Gas Control Technologies, GHGT9, 16–20 November, Washington DC, p. 47–54. Tao, Q., Bryant, S. L., and Meckel, T. A., 2013, Modeling above-zone measurements of pressure and temperature for monitoring CCS sites: International Journal of Greenhouse Gas Control, v. 18, p. 523–530, doi:10.1016/j.ijggc.2012.08.011.

Soil-Gas monitoring data collected at the P-site and DAS study locations during SECARB project at Cranfield oil site in Mississippi. Data was used to study influence of gravel pad, pit, plants, and plugged and abandoned (P&A) oil well on near-surface soil-gas compositions. Associated Publications: Anderson, J. S., Romanak, K. D., Yang, C., Lu, J., Hovorka, S. D., and Young, M. H., 2017, Gas source attribution techniques for assessing leakage at geologic CO2 storage sites: Evaluating a CO2 and CH4 soil gas anomaly at the Cranfield CO2-EOR site: Chemical Geology, v. 454, p. 93-104, doi:10.1016/j.chemgeo.2017.02.024 Hingst, M. C., 2013. Geochemical effects of elevated methane and carbon dioxide in near-surface sediments above an EOR/CCUS site, The University of Texas at Austin, Master’s thesis. http://hdl.handle.net/2152/21836 Lu, J., Kharaka, Y. K., Thordsen, J. J., Horita, J., Karamalidis, A., Griffith, C., Hakala, J. A., Ambats, G., Cole, D. R., Phelps, T. J., Manning, M. A., Cook, P. J., and Hovorka, S. D., 2012, CO2‒rock‒brine interactions in Lower Tuscaloosa Formation at Cranfield CO2 sequestration site, Mississippi, U.S.A.: Chemical Geology, v. 291, p. 269‒277. Yang, C., Jamison, K., Xue, L., Dai, Z., Hovorka, S. D., Fredin, L., and Treviño, R. H., 2017, Quantitative assessment of soil CO2 concentration and stable carbon isotope for leakage detection at geological carbon sequestration sites: Greenhouse Gases: Science and Technology, v. 7, no. 4, p. 680-691, doi:10.1002/ghg.1679. Yang, C., Romanak, K. D., Reedy, R. C., Hovorka, S. D., and Treviño, R. H., 2017, Soil gas dynamics monitoring at a CO2-EOR site for leakage detection: Geomechanics and Geophysics for Geo-Energy and Geo-Resources, v. 3, p. 351-364, doi:10.1007/s40948-017-0053-7

Reservoir data comes from the USBR. This data is retrieved daily and stored in the San Juan Flows database. All data on this site should be considered provisional unless otherwise noted. During the irrigation season, real-time diversion data for the Hogback and Fruitland diversions are uploaded via satellite every hour and made available to us by by the USGS. We automatically retrieve the data every hour. Equipment used in collection and telemetry of diversion data are maintained by the Navajo Nation.

The data set contains a summary of all measurements that were collected at the cottage grove reservoir over the time period of the study. This dataset is associated with the following publication: Eckley, C., T. Luxton, J. Goetz, and J. McKernan. Water-level fluctuations influence sediment porewater chemistry and methylmercury production in a flood-control reservoir.. David Carpenter, and Eddy Zeng ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 222: 32-41, (2017).

A series of simulations was run in an initial study to determine the dependence of critical reservoir parameters, such as residual oil saturation, on reservoir heterogeneities. In the initial work a random set of permeabilities constrained to a log normal distribution was input to a black oil simulator (BEST). At the end of a simulated waterflood, no significant correlation was found between residual oil saturation and permeability. A different approach was then used where a large heterogeneity was placed at the center of the simulation grid. Under these conditions a good correlation between residual oil saturation and permeability was found at the end of the simulated waterflood. In the future, additional simulations will be conducted to determine the minimum resolution required to reflect a reservoir heterogeneity. 1 ref., 8 figs., 2 tabs.

Resource and reserves estimation methodology for conventional oil and gas reservoirs is based in large part on the historic precedent of geologic and engineering evaluation over the last 150 years. Conversely, the methods used for shale gas reservoirs are recently developed and still evolving. Gas shale formations display complex reservoir characteristics, including free and adsorbed gas, natural fractures, and very low matrix permeability. These characteristics vary significantly vertically and laterally within shale reservoirs, controlled by both depositional setting and subsequent burial and tectonic history. During early stages of a shale gas development program, critical data are required to fully assess the gas in place (resources) and the potential for economic recovery of that gas (reserves). These data, including formation geometry, porosity, organic content and composition, gas sorption, and reservoir pressure, are used by the geologist to begin to understand the static reservoir structure. Stochastic techniques are often employed to populate the static geologic model of the exploration area. Subsequent data obtained from completion and production operations begin to define the dynamic reservoir structure, including completed reservoir volume and flow dynamics. Combining traditional deterministic forecasting techniques (analog, volumetric, decline curve) with more specialized methods (shale gas specific analytic and simulation models) provides an initial understanding of reservoir performance and ultimate recovery. Awareness of the reservoir and reservoir property continuity is critical for assessing with reasonable certainty the extent and viability of this continuous play within and outside of the initial exploration area. Assessing developed plays again relies on a combined stochastic/deterministic approach; however, in this more data-rich setting the geologist will rely on a more deterministic approach for evaluating the changing static and dynamic conditions within the reservoir. From this evaluation, production regions/compartments within the continuous deposit are defined. Production analysis and forecasting at this stage often use a stochastic approach, in part because of the abundant production data and the known variability inherent in shale gas production. Understanding production variability in light of the static and dynamic reservoir conditions can further satisfy the reasonable certainty criteria required for reserves estimation.

Third International Reservoir Characterization Technical Conference 1991

Includes reservoir operational data, real-time data collection platform (DCP) data, daily and monthly reports, and conditions reports/maps.

This is Reclamation’s new hydrologic database access portal. These new tools are designed to replace the Data Retrieval apps below, as they begin to reach the end of their functional lifespans. We encourage everyone to take a look at our new and improved access portal and begin plans to transition to this improved data delivery source.

The Bureau of Reclamation’s new hydrologic database access portal. For more information see https://www.usbr.gov/uc/water/

Contains Snow Survey, Water Supply and Reservoir Storage Products

USGS Real Time Water Data for New Mexico includes streamflow, groundwater, lake and reservoir, precipitation, and water quality data. Real-time data typically are recorded at 15-60 minute intervals, stored onsite, and then transmitted to USGS offices every 1 to 4 hours, depending on the data relay technique used.

Use of "Rock-Typing" to Characterize Carbonate Reservoir Heterogeneity, Final Report; March 1994

This report describes the development of a preliminary 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during Stage 3 of the April 2022 stimulation. A preliminary 3D velocity model for the larger FORGE area was developed using RMS velocities of the seismic reflection survey and seismic velocity logs from borehole measurements as an input model. To improve the accuracy of the model in the shallow subsurface, travel times phase arrivals of the direct propagating P-waves were determined from the seismic reflection data, using PhaseNet, a deep-neural-network-based seismic arrival time picking method. The travel times were subsequently inverted using the input velocity model. The results showed that the input velocity model needs improvement as the resulting model appears too fast in the easter region of the FORGE area. During the next phase of this work, we will update the input velocity model and generate P-wave arrival times for additional seismic source locations, to improve the horizontal resolution in the sedimentary layer and to obtain a model that better matches the sedimentary layer and the travel time observations.

The Pioneer Anticline, 25 miles southwest of Bakersfield, California, which has yielded oil since 1926, was the subject of a three-year study aimed at recovering more oil. A team from Michigan Technological University of Houghton, Michigan (MTU), and Digital Petrophysics, Inc. of Bakersfield, California (DPI), undertook the study as part of the Department of Energy`s Advanced Extraction and Process Technology Program. The program provides support for projects which cross-cut geoscience and engineering research in order to develop innovative technologies for increasing the recovery of some of the estimated 340 billion barrels of in-place oil remaining in U.S. reservoirs. In recent years, low prices and declining production have increased the likelihood that oil fields will be prematurely abandoned, locking away large volumes of unrecovered oil. The major companies have sold many of their fields to smaller operators in an attempt to concentrate their efforts on fewer {open_quotes}core{close_quotes} properties and on overseas exploration. As a result, small companies with fewer resources at their disposal are becoming responsible for an ever-increasing share of U.S. production. The goal of the MTU-DPI project was to make small independent producers who are inheriting old fields from the majors aware that high technology computer software is now available at relatively low cost. In this project, a suite of relatively inexpensive, PC-based software packages, including a commercial database, a multimedia presentation manager, several well-log analysis program, a mapping and cross-section program, and 2-D and 3-D visualization programs, were tested and evaluated on Pioneer Anticline in the southern San Joaquin Valley of California. These relatively inexpensive, commercially available PC-based programs can be assembled into a compatible package for a fraction of the cost of a workstation program with similar capabilities.

Five earth models were generated in SEAM Phase I to simulate a realistic earth model of a salt canopy region of the Gulf of Mexico complete with fine-scale stratigraphy that includes oil and gas reservoirs. The model represents a 35 km EW x 40 km NS area and 15 km deep. The grid interval for the Elastic Earth model is 20 m x 20 m x 10 m (x,y,z). All model properties are derived from fundamental rock properties including v-shale (volume of shale) and porosities for sand and shale that follow typical compaction gradients below water bottom. Hence, properties have subtle contrasts at macro-layer boundaries, especially in the shallow section, generating very realistic synthetic data. The Elastic Earth Model distribution is the model used for simulation of the SEAM Phase I RPSEA elastic data set. For the simulations, the minimum S-wave velocity was set at 600 m/s by compressing all S-wave velocities in the originally designed model having velocities between 100 and 800 m/s into a range between 600 and 800 m/s. This distribution has 3 binary files, one each for the density, P-wave velocity and the S-wave velocity. A README is also included.