The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations
Abstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state mod...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
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| Series: | Geoscience Data Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/gdj3.191 |
| _version_ | 1797205950044569600 |
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| author | David Alumbaugh Erika Gasperikova Dustin Crandall Michael Commer Shihang Feng William Harbert Yaoguo Li Youzuo Lin Savini Samarasinghe |
| author_facet | David Alumbaugh Erika Gasperikova Dustin Crandall Michael Commer Shihang Feng William Harbert Yaoguo Li Youzuo Lin Savini Samarasinghe |
| author_sort | David Alumbaugh |
| collection | DOAJ |
| description | Abstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO2, and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website. |
| first_indexed | 2024-04-24T08:59:15Z |
| format | Article |
| id | doaj.art-05d61b2e0332482c9bc83b189177d77f |
| institution | Directory Open Access Journal |
| issn | 2049-6060 |
| language | English |
| last_indexed | 2024-04-24T08:59:15Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geoscience Data Journal |
| spelling | doaj.art-05d61b2e0332482c9bc83b189177d77f2024-04-16T03:08:10ZengWileyGeoscience Data Journal2049-60602024-04-0111221623410.1002/gdj3.191The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigationsDavid Alumbaugh0Erika Gasperikova1Dustin Crandall2Michael Commer3Shihang Feng4William Harbert5Yaoguo Li6Youzuo Lin7Savini Samarasinghe8Lawrence Berkeley National Laboratory Berkeley California USALawrence Berkeley National Laboratory Berkeley California USANational Energy Technology Laboratory Morgantown West Virginia USALawrence Berkeley National Laboratory Berkeley California USALos Alamos National Laboratory Los Alamos New Mexico USANational Energy Technology Laboratory Morgantown West Virginia USAColorado School of Mines Golden Colorado USALos Alamos National Laboratory Los Alamos New Mexico USAColorado School of Mines Golden Colorado USAAbstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO2, and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.https://doi.org/10.1002/gdj3.191CO2 storagegeophysicsmonitoringsubsurface |
| spellingShingle | David Alumbaugh Erika Gasperikova Dustin Crandall Michael Commer Shihang Feng William Harbert Yaoguo Li Youzuo Lin Savini Samarasinghe The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations Geoscience Data Journal CO2 storage geophysics monitoring subsurface |
| title | The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations |
| title_full | The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations |
| title_fullStr | The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations |
| title_full_unstemmed | The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations |
| title_short | The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations |
| title_sort | kimberlina synthetic multiphysics dataset for co2 monitoring investigations |
| topic | CO2 storage geophysics monitoring subsurface |
| url | https://doi.org/10.1002/gdj3.191 |
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