This study, by petroleum geoscience team at the BGS, was created as part of the 21st Century exploration Roadmap (21CXRM). The petroleum geoscience team worked with the UK North Sea Transition Authority (NSTA), Oil and Gas UK and a consortium of over 45 oil companies to evaluate the remaining petroleum potential of previously overlooked or unfashionable areas of the UK Continental Shelf.The 21st Century Exploration Roadmap (21CXRM): Palaeozoic Project is part of the UK Government's endeavour to maximise the economic recovery of hydrocarbons on the UK Continental Shelf (UKCS), in response to the Wood Review.A study of the Palaeozoic of the UKCS was one of the first projects to be implemented as part of the 21st Century Exploration Roadmap. Multidisciplinary studies further defined the Carboniferous and Devonian petroleum systems focused over and around the Mid North Sea High and northwards to the Orcadian Basin/East Shetland Platform. In the wider Irish Sea area, the focus was the Carboniferous play.Coinciding with the release by NSTA of the UK Government seismic across and around the Mid North Sea High frontier area, project results have been delivered digitally to project sponsors. They include seismic, well and gravity interpretations along with burial/uplift/maturity modelling, source rock geochemistry studies and palaeographic reconstructions to inform the location of prospective Carboniferous and Devonian plays. Onshore data and knowledge has been incorporated.The results were published under the Open Government License in March 2017 and are loaded to the BGS Offshore GeoIndex. Users should note that:These outputs are at a regional scale, created for project specific purposes and so should be used appropriately.They are not BGS corporate products, but peer-reviewed project outputs.

Bell Creek baseline seismic survey conducted in 2012. The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Baseline2012 contains 3 rasters: -Clpbase2012 is the baseline survey. -Clp2012ovr14 is the baseline survey clipped down to where it overlaps the 2014 monitor survey. -Clp2012ovr15 is the baseline survey clipped down to where it overlaps the 2015 monitor survey. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June.

Seismic monitoring survey at Bell Creek 2014 clipped down to the 2015 monitoring survey. The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Monitor2014 contains 3 rasters: -ClpMon2014 is the monitoring survey. -Clp2014ovr15 is the 2014 monitor survey clipped down to where it overlaps the 2015 monitor survey. -ClpDiff1412 is the RMS amplitude difference between the monitor 2014 and baseline 2012 survey. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June.

Seismic monitoring survey at Bell Creek 2014; showing the RMS amplitude difference between the monitor 2014 and baseline 2012 survey. The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Monitor2014 contains 3 rasters: -ClpMon2014 is the monitoring survey. -Clp2014ovr15 is the 2014 monitor survey clipped down to where it overlaps the 2015 monitor survey. -ClpDiff1412 is the RMS amplitude difference between the monitor 2014 and baseline 2012 survey. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June.

2015 Monitoring Seismic Survey at Bell Creek The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June. Monitor2015 contains 4 rasters: Clp2015ovr12 is the 2015 monitor survey clipped down to where it overlaps the 2012 baseline survey. Clp2015ovr14 is the 2015 monitor survey clipped down to where it overlaps the 2014 monitor survey. Clpdiff1512 is the RMS amplitude difference between the monitor 2015 and baseline 2012 survey. Clpdiff1514 is the RMS amplitude difference between the monitor 2015 and monitor 2014 survey.

2015 Monitoring Seismic Survey at Bell Creek; clipped over 2014 survey The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June. Monitor2015 contains 4 rasters: Clp2015ovr12 is the 2015 monitor survey clipped down to where it overlaps the 2012 baseline survey. Clp2015ovr14 is the 2015 monitor survey clipped down to where it overlaps the 2014 monitor survey. Clpdiff1512 is the RMS amplitude difference between the monitor 2015 and baseline 2012 survey. Clpdiff1514 is the RMS amplitude difference between the monitor 2015 and monitor 2014 survey.

2015 Monitoring Seismic Survey at Bell Creek The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June. Monitor2015 contains 4 rasters: Clp2015ovr12 is the 2015 monitor survey clipped down to where it overlaps the 2012 baseline survey. Clp2015ovr14 is the 2015 monitor survey clipped down to where it overlaps the 2014 monitor survey. Clpdiff1512 is the RMS amplitude difference between the monitor 2015 and baseline 2012 survey. Clpdiff1514 is the RMS amplitude difference between the monitor 2015 and monitor 2014 survey.

2015 Monitoring Seismic Survey at Bell Creek The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June. Monitor2015 contains 4 rasters: Clp2015ovr12 is the 2015 monitor survey clipped down to where it overlaps the 2012 baseline survey. Clp2015ovr14 is the 2015 monitor survey clipped down to where it overlaps the 2014 monitor survey. Clpdiff1512 is the RMS amplitude difference between the monitor 2015 and baseline 2012 survey. Clpdiff1514 is the RMS amplitude difference between the monitor 2015 and monitor 2014 survey.

Baseline seismic survey collected during PCOR Phase III. The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Baseline2012 contains 3 rasters: -Clpbase2012 is the baseline survey. -Clp2012ovr14 is the baseline survey clipped down to where it overlaps the 2014 monitor survey. -Clp2012ovr15 is the baseline survey clipped down to where it overlaps the 2015 monitor survey. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June.

Seismic monitoring survey at Bell Creek 2014. The raster layers represent a map of the RMS amplitude slice between the horizons Springen Ranch and the Skull Creek (Muddy Formation) enclosing the Bell Creek sand reservoir at the Bell Creek oil field. This gridded dataset was interpolated within ArcGIS based upon seismic amplitude values measured across the study area. Monitor2014 contains 3 rasters: -ClpMon2014 is the monitoring survey. -Clp2014ovr15 is the 2014 monitor survey clipped down to where it overlaps the 2015 monitor survey. -ClpDiff1412 is the RMS amplitude difference between the monitor 2014 and baseline 2012 survey. Additional information regarding this data and interpretations can be found in the following document: Salako, O., Livers., A.J., Burnison, S.A., Hamling, J.A., Wildgust, N., Gorecki, C.D., Glazewski, K.A., and Heebink, L.V., 2017, Analysis of Expanded Seismic Campaign: Plains CO2 Reduction (PCOR) Partnership Phase III draft Task 9 Deliverable D104 for U.S. Department of Energy National Energy Technology Laboratory Cooperative Agreement No. DE-FC26-05NT42592, Grand Forks, North Dakota, Energy & Environmental Research Center, June.

Listing of Bell Creek Seismic Publications and Reports

The Plains CO2 Reduction (PCOR) Partnership, led by the Energy & Environmental Research Center (EERC), is working with Denbury Onshore (Denbury) to evaluate the effectiveness of large-scale injection of carbon dioxide (CO2) into the Bell Creek oil field for CO2 enhanced oil recovery (EOR) and to study long-term incidental CO2 storage. A technical team that includes Denbury, the EERC, and others are conducting activities to determine the baseline reservoir characteristics for development of a geologic model for predictive simulations and to serve as a comparison to time-lapse data as they are acquired. One of the activities was the acquisition and interpretation of a baseline three-dimensional (3-D) surface seismic survey acquired over a major portion of the field which is the subject of this report. The interpretation will be used to advance the field characterization effort. The geophysical data will be integrated with the EERC’s geologic model to improve its accuracy. In the future, when paired with at least one subsequent 3-D surface seismic survey, the data difference will provide a direct indication of where the CO2 has migrated within the reservoir and aid in monitoring, verification, and accounting goals. A brief overview of the source testing, data acquisition, and processing is given. A seismic source configuration consisting of two heavy vibrators operated in tandem was chosen after a series of tests conducted in August and December of 2011. The data acquisition for the main survey occurred in August and September 2012. The data were processed by a contractor in Houston and delivered to Denbury early in 2013. The EERC received a stacked data set in April with redactions where Denbury did not have mineral rights or leases. Seismic interpretation efforts began with making well ties to the data and identifying the Bell Creek reservoir reflector. The polarity of the dataset was established to be such that an increase in acoustic impedance (AI) would cause a negative deflection on the data. The reservoir is thin and of higher AI than the encasing shale layers, so with this polarity, it presents on the seismic data as a trough–peak combination representing the entering and exiting reflections at a two-way time of ~1150 msec at the 05-06 OW monitoring well location. The origin of the reservoir reflection is due to a large increase in AI at the top of the Springen Ranch and a similar decrease in AI at the top of the Skull Creek. The measured thickness of the Springen Ranch-to-Skull Creek interval in the field varies from about 50 to 75 feet. Spectral analysis of data in the zone of interest reveals a bandwidth of 10–48 Hz, which together with the average interval velocity mathematically limits the vertical resolution of the seismic data at reservoir depth to just under 60 feet. Therefore, the reservoir is a thin-bed reflector with thickness near or below the limit of resolution. A reflector of this type would be expected to exhibit possible tuning effects, but they are not evident. Tuning is an effect that occurs on thin beds when the top and bottom reflections interact to partially add in phase resulting in high reflection amplitudes. An important assumption is that the ix interval is lithologically homogeneous. The tuning thickness for this data’s bandwidth is 76 feet, so high amplitudes would be expected in thicker Springen Ranch-to-Skull Creek sections and lower amplitudes expected where the interval is thinner. The data exhibit the opposite character. This implies a nonhomogeneous interval where internal interactions due to lithology affect the composite reflection amplitude. The character of the Bell Creek sand meaningfully contributes to this effect. The Bell Creek reservoir sand is a subset of the reflection interval. Typically 20 to 30 feet thick, its top and bottom cannot be directly resolved on the seismic data set as delivered. When the sand is thick and clean and juxtaposed against the harder, fine-grained components of the Springen Ranch at the top and the Rozet below, these impedance contrasts internal to the overall composite reflection appear to cause interference effects which result in a low-amplitude reflection. When the sand is thinner or fines upward with less AI contrast, less internal interference results, and the composite reflection maintains a higher amplitude. Because of this, a map of seismic amplitudes generated from the reservoir reflection differentiates between areas of 1) thick clean sand and 2) thinner clean sand or sand with a fine-grained component or shaley matrix. The effect is illustrated in cross sections paired with well logs and also by overlaying the map on existing isopach maps of sand, with good agreement. Several geologic features are visible on the amplitude map and are briefly examined in cross section with the seismic data and well logs. These features include: 1. A fluvial channel feature in the northern part of the field, trending roughly north–south, has a higher amplitude than surrounding areas and is shown to be shale-filled and acts as a flow boundary. 2. A flow boundary between Phases 1 and 2 is also shown to be shale-filled and is a possible extension of the fluvial channel feature to the north. 3. A linear erosional valley trending northwest–southeast which predates the fluvial channel feature and serves as a flow boundary between Phases 1 and 3 and between Phases 2 and 4. The fill at the erosional surface is characterized by coal and bentonitic shales. 4. An erosional valley on the south end of the field which exhibits a dendritic outline. The fill at the erosional surface is characterized by coal and bentonitic shales. Three structural aspects of the field are briefly explored: 1. Thinning of the Bell Creek sand which forms the updip boundary and trap to the southeast is not directly discernible on the seismic data. 2. Polygonal faulting is shown to be prevalent within the overlying Belle Fourche Formation. The faults are thought to originate from dewatering of thick bentonite layers at the bottom of the Belle Fourche and top of the Mowry. Faults do not extend below the top of the Mowry or above the Belle Fourche and likely do not impact the containment integrity of the reservoir. 3. A possible basement faulting system thought to control the southwest to northeast trend of the reservoir paleo-high was identified and is shown on a section. Future work will include integrating the 3-D data and interpretation with the geocellular model and 3-D VSP data acquired in 2013 and 2014. Geomechanical properties will be computed across the field guided by the seismic data. Future time-lapse surface seismic data will use the baseline survey to generate difference displays to image and verify the progress of CO2 that has been injected into the reservoir. Modeling performed by Denbury has shown that injected CO2 will induce a detectable amplitude reduction on the reservoir reflection at the current bandwidth.

The purpose of compiling the CEUS-SSC Project database was to organize and store those data and resources that had been carefully and thoroughly collected and described for the TI Team’s use in characterizing potential seismic sources in the CEUS. An important goal for the development of this database was to document sources and dates for all information that was initially assessed for the CEUS-SSC Project, specifying exactly what data and resources were considered, and provide for pertinent future data sets to be incorporated as they were generated for the project. Development of the project database began at the inception of the project to provide TI Team members with a common set of data, maps, and figures for characterization of potential seismic sources. The database was continually updated during the course of the project through the addition of new references and data collected by TI Team members and project subcontractors, including information presented in project workshops and provided through PPRP review documentation. This appendix presents the contents of the project database, as well as information on the workflow, development roles, database design considerations, data assessment tasks, and management of the database. Based on the CEUS Project Plan, the project database included, but was not limited to, the following general types of data: Magnetic anomaly Gravity anomaly Crystalline basement geology Tectonic features and tectonic/crustal domains Tectonic stress field Thickness of sediments Crustal thickness VP at top of crystalline basement Seismic reflection data at Charleston, South Carolina Earthquake catalog Quaternary faulting and potential Quaternary features Mesozoic rift basins Paleoliquefaction sites Topography and bathymetry Liquefaction dates from published literature for the Wabash, New Madrid, and Charleston seismic zones Index map showing locations of published crustal scale seismic profiles and geologic cross sections

"The Central and Eastern United States Seismic Source Characterization for Nuclear Facilities (CEUS-SSC) Project was conducted from April 2008 to December 2011 to provide the nuclear industry a new, regional seismic source model for use in conducting probabilistic seismic hazard analyses (PSHAs) for nuclear facilities. PSHA is used as a method for accounting for uncertainty in seismic design and in calculating seismic risk. Unlike previous seismic hazard studies, the CEUS-SSC Project was sponsored through an industry-government partnership. The study was conducted using the Senior Seismic Hazard Analysis Committee (SSHAC) Study Level 3 assessment process. The SSHAC process ensures consideration of the knowledge and uncertainties of the larger technical community within a robust and transparent framework." The data includes geological information and other aspects relevant to other studies such as the Appalachian Basin Project.

The data in this archive are the distributed acoustic sensor (DAS) and well log data used in Daley, et al, 2016. Both raw and processed data are included

Chester 16 crosswell seismic data. Please contact NETL's EDX administrator to obtain this data via EDXSupport@netl.doe.gov.

Dover 33 and Chester 16 baseline and repeat vertical seismic profile (VSP) data. Please contact NETL's EDX administrator to obtain this data.

Confining system integrity assessment by detection of natural gas migration using seismic diffractions

As part of the Midwest Regional Carbon Sequestration Partnership (MRCSP) Phase III project, a monitoring study was conducted to assess the effectiveness of DAS (Distributed Acoustic Sensing) -based VSP (Vertical Seismic Profiling) technology for delineating CO2 injected into the Silurian-age pinnacle reefs in northern Michigan, the host rocks for the MRCSP Phase III demonstration project. The DAS VSP study was conducted in the Chester 16 reef, one of several reefs in Otsego County Michigan that is operated by Core Energy, LLC of Traverse City, Michigan.

Understanding the initiation and arrest of earthquakes is one of the long-standing challenges of seismology. Here we report on direct observations of borehole displacement by a meter-sized shear rupture induced by pressurization of metamorphic rock at 1.5 km depth. We observed the acceleration of sliding, followed by fast co-seismic slip and a transient afterslip phase. Total displacements were about 7, 5.5 and 9.5 micrometers, respectively for the observed pre-slip, co-seismic slip and afterslip. The observed pre-slip lasted about 0.4 seconds. Co-seismic slip was recorded by the 1 kHz displacement recording and a 12-component array of 3-C accelerometers sampled at 100 kHz. The observed afterslip is consistent with analytical models of arrest in a velocity-strengthening region and subsequent stress relaxation. The observed slip vector agrees with the activation of a bedding plane within the phyllite, which is corroborated by relocated seismic events that were observed during the later stages of the injection experiment. This submission includes the pressure and deformation data recorded by the SIMFIP probe during the first injection at the 164 ft (50 m) notch of borehole E1-I. The injection was performed on on 05/22/2018 as part of Experiment 1 of the EGS Collab project. This data accompanies a manuscript submitted to GRL, linked in this submission.

The USGS Earthquake Hazards Program is a major element of the four-agency National Earthquake Hazards Reduction Program (NEHRP), established by Congress in 1977. The USGS responsibilities within NEHRP include earthquake monitoring and notification, earthquake impact and hazard assessments, and targeted research on earthquake causes and effects.

The files associated with this data archive are time-lapse crosswell seismic, recorded using a source that generated both P- and S-waves, allowing both P and S tomography. Previously published results demonstrated imaging of injected CO2 using this data. Descriptive

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.

Processed SEGY files from the FutureGen2 project. Data includes 5 categories of OVSP data with 15 lines in each category (all in TWT). VSP data was processed from Sterling Seismic.

This Petrel project features geomechanical models that takes advantage of existing modeling work from PNNL and expands upon its framework to included overlying and underlying geologic zones. Where available, geomechanical logs were incorporated into these models. While the existing CO2 storage interval and caprock (Mt. Simon Ss and Eau Claire Shale) were finely layered by PNNL, the overlying layers were more coarsely layered to limit cell count.

This report compiles the results of geologic characterization of Task 3 (late-stage reef), Task 4 (active EOR reefs), and Task 5 (new EOR reefs) to demonstrate developed methodologies, geologic variability, and reservoir potential.

The expanded seismic campaign consists of acquisition, processing, and interpretation of multiple time-lapse seismic data sets including 2-D surface seismic data, 3-D surface seismic data, vertical seismic profile (VSP) data, and passive seismic data. Initial analysis of the expanded seismic campaign consisted of interpretation and refined processing of these data. Details on the results of the initial analysis of the expanded seismic campaign data will be reported in the topical report Deliverable 104 “Bell Creek Test Site – Analysis of Expanded Seismic Campaign,” which will be submitted to DOE by the end of June 2017.

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.

Kimberlina 1.2 Velocity model and synthetic seismic data, produced in collaboration of teams at the National Energy Technology Laboratory, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory through the National Risk Assessment Partnership. Data is associated with the following publication: Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019. The Kimberlina 1.2 Velocity models were produced by Zan Wang, Robert Dilmore, William Harbert, and Lianjie Huang at NETL. The following citations are directly related to the creation of the velocity models: Wang, Z. Harbert, W., Dilmore, R., Huang, L. Modeling of time-lapse seismic monitoring using CO2 leakage simulations for a model CO2 storage site with realistic geology: Application in assessment of early leak-detection capabilities. International Journal of Greenhouse Gas Control. V. 76, September 2018, Pages 39-52. https://doi.org/10.1016/j.ijggc.2018.06.011 Wang, Z., Dilmore, R., Harbert, W. Inferring CO2 saturation from synthetic surface seismic and downhole monitoring data using machine learning for leakage detection at CO2 sequestration sites. International Journal of Greenhouse Gas Control, V. 100, September 2020. https://doi.org/10.1016/j.ijggc.2020.103115 The velocity models were built based on the Kimberlina 1.2 aquifer impact data which is associated with the following publications: Buscheck, T.A., Mansoor, K., Yang, X., Wainwright, H., and Carroll, S. (2019). Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir. International Journal of Greenhouse Gas Control. 91. 102812. 10.1016/j.ijggc.2019.102812. Xianjin Yang, Thomas A. Buscheck, Kayyum Mansoor, Zan Wang, Kai Gao, Lianjie Huang, Delphine Appriou, Susan A. Carroll, Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, Volume 90, 2019, 102803, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2019.102803 The synthetic seismic data was produced by Youzuo Lin and team at LANL, and are associated with the following citations: Jordan, P. D., and J. L. Wagoner. Characterizing Construction of Existing Wells to a CO2 Storage Target: The Kimberlina Site, California. Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019.

Dover 33 baseline and repeat microseismic data. Please contact NETL's EDX administrator to obtain this data.

This story map tells the tale of Earth’s tectonic plates, their secret conspiracies, awe-inspiring exhibitions and subtle impacts on the maps and geospatial information we so often take for granted as unambiguous. But is it? We recommend you journey through this map on the trail we’ve manicured on the left. You will find yourself hovering over the Mid-Atlantic Ridge or swimming in magma deep within the Earth’s core. Have fun and we hope your voyage is fruitful!

The National Archive of Marine Seismic Surveys (NAMSS) is a marine seismic reflection data archive consisting of data acquired by or contributed to U.S. Department of the Interior agencies. The USGS is committed to preserving these data on behalf of the academic community and the nation. Data are provided with free and open access. --NAMSS Team

The objective of this project was to conduct a carbon storage resource assessment of depleted oil and gas reservoirs in the northern Gulf of Mexico (GoM), specifically an area offshore Texas and Louisiana. Within that area, the project focused on a smaller, geologically representative area close to major sources and existing pipeline infrastructure. The project identified at least one specific site that could be considered for a future commercial or integrated demonstration project with the ability to store at least 30 million metric tons (MMT) of CO2.

The utility of passive seismic emission tomography for mapping geothermal permeability has been tested at San Emidio in Nevada. The San Emidio study area overlaps a geothermal field in production since 1987 and another resource to the south of the production field. Passive seismic data collections were completed at San Emidio in late 2016 by Microseismic Inc as part of a DOE project. The PSET results are being analyzed as part of the WHOLESCALE project. This submission includes P-wave velocity model data, and the passive seismic data with more information on each bellow.

Processed SEGY files from the Perch 3D seismic area in the Michigan Basin. Data was used to make estimates of the stress field in the study area. Data was processed from Texseis, Inc.

The SEM in this Petrel project is based, in part, on an earlier model prepared at Battelle (1) that covers Michigan's Northern Pinnacle Reef trend. While the existing SEM focused on the Niagaran section, this Petrel project expands the framework to included overlying and underlying geologic zones. Where available, geomechanical logs were incorporated into the model.

This dataset includes earthquake catalogues for the three stages of the 2022 well 16A(78)-32 stimulation provided by Geo Energie Suisse. Events in these catalogues have been visually inspected. There are additional events of lower signal to noise that were automatically detected. Those events will require additional analysis and processing.

Map of seismic legacy lines at Bell Creek oil field

In December 2016, 1301 vertical-component seismic instruments were deployed at the San Emidio Geothermal field in Nevada. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in one-minute increments in SEGD and MSEED formats. See the metadata in GDR submission (linked below as "Seismic Survey 2016 Metadata at San Emidio Nevada") for details about the seismic station locations, seismic data logger specifications, instrumentation specifications, descriptions of data, a fracture finding summary, and the final report for the 2016 seismic survey done in San Emidio, Nevada.

1301 Vertical Component seismic instruments were deployed at San Emidio Geothermal field in Nevada in December 2016. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in one-minute increments. Data includes seismic station locations, seismic data logger specifications, instrumentation specifications, descriptions of data, a fracture finding summary and the final report for the 2016 WHOLESCALE seismic survey done in San Emidio, Nevada.

Shallow Seismic Investigations of Devonian Shale Gas Production; June 1982

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686. The purpose of this DOE funding is to develop methods that can provide key information about stress fields that act on deep rocks without invading the earth to acquire that information. The research objective was to demonstrate methods that extract the azimuth directions of SHmax and SHmin stress in deep rocks from traditional seismic reflection data like the data that are used to explore for deep oil and gas reservoirs. Battelle performed two seismic investigations and achieved estimates of SHmax and SHmin azimuths in both efforts that agreed with local, non-seismic, ground-truth measurements of SHmax orientation. The first procedure utilized vertical seismic profiling (VSP) data; the second effort utilized three-dimensional (3D) seismic data.

Topographic maps for the northern Appalachian Basin area, subsurface readings, and seismic data in the forms of PDFs and tables.

This submission contains documents that describe the USU Camas-1 test well, drilled in Camas Prairie, Idaho, in Fall 2018 and Fall 2019. The purpose of this well is to validate exploration methodologies of the Snake River Plain (SRP) Play Fairway Analysis (PFA) project.

This is a link to the website where DAS seismic data, collected from wells 78-32 and 78B-32 during the Utah FORGE 2022 stimulation, is now available for download. The data can be accessed at "Well 16A78-32 2022 Stimulation Seismicity Data" link in the submission under the Silixa heading. The page includes surface acquisition nodal datasets, downhole geophone data, and Silixa fiber data. Raw seismic stimulation data and the scripts to process this data is under the Silixa heading.

This is a link to downhole geophone data collected by Schlumberger. These data were collected in the Utah FORGE deep seismic monitoring wells 58-32 and 56-32. The format is a standard SEGY and the units are bits. To convert to acceleration (m/s2) multiply by 2.333 x 10-7. Use one of the scripts linked below to use wget commands to pull the data.

This is a PDF file generated by Woolsey Land Surveying, P.C containing the surveyed locations, as located, in Latitude and Longitude degrees, of the Utah FORGE FSB4, FSB5, & FSB6 shallow seismic well locations.

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.

http://www.seismicatlas.org/ The VSA has been created to share the geological interpretation of seismic data. By browsing freely through the site you will find seismic images and interpretations. And you can download higher resolution images for your own use - all without signing in. There are no membership fees, all we ask is that you respect the intellectual property rights of the contributors who have posted images on the VSA. Just hit the link "EXPLORE THE VSA" to start!

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.