Optimizing carbon dioxide trapping for geological storage
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866929504393363456 |
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| author | Azpiroz, Jaione Tirapu Giro, Ronaldo Ferreira, Rodrigo Neumann Barros da Silva, Marcio Nogueira Pereira Rodriguez, Manuela Fernandes Blanco Lopez, Adolfo E. Correa Vasquez, David A. Lazo Ferreira, Matheus Esteves Del Grande, Mariana Da Silva, Ademir Ferreira Steiner, Mathias B. |
| author_facet | Azpiroz, Jaione Tirapu Giro, Ronaldo Ferreira, Rodrigo Neumann Barros da Silva, Marcio Nogueira Pereira Rodriguez, Manuela Fernandes Blanco Lopez, Adolfo E. Correa Vasquez, David A. Lazo Ferreira, Matheus Esteves Del Grande, Mariana Da Silva, Ademir Ferreira Steiner, Mathias B. |
| contents | Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, the CO2 trapping potential depends on injection pressure, temperature, and the rock's interaction with the surrounding fluids. Modeling this interaction requires adequate representations of both capillary volume and surface. For the lack of scalable representations, however, the prediction of a rock's CO2 storage potential has been challenging. Here, we report how to represent a rock's pore space by statistically sampled capillary networks (ssCN) that preserve morphological rock characteristics. We have used the ssCN method to simulate CO2 drainage within a representative sandstone sample at reservoir pressures and temperatures, exploring intermediate- and CO2-wet conditions. This wetting regime is often neglected, despite evidence of plausibility. By raising pressure and temperature we observe increasing CO2 penetration within the capillary network. For contact angles approaching 90 degrees, the CO2 saturation exhibits a pronounced maximum reaching 80 percent of the accessible pore volume. This is about twice as high as the saturation values reported previously. For enabling validation of our results and a broader application of our methodology, we have made available the rock tomography data, the digital rock computational workflows, and the ssCN models used in this study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_13512 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimizing carbon dioxide trapping for geological storage Azpiroz, Jaione Tirapu Giro, Ronaldo Ferreira, Rodrigo Neumann Barros da Silva, Marcio Nogueira Pereira Rodriguez, Manuela Fernandes Blanco Lopez, Adolfo E. Correa Vasquez, David A. Lazo Ferreira, Matheus Esteves Del Grande, Mariana Da Silva, Ademir Ferreira Steiner, Mathias B. Applied Physics Computational Physics Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, the CO2 trapping potential depends on injection pressure, temperature, and the rock's interaction with the surrounding fluids. Modeling this interaction requires adequate representations of both capillary volume and surface. For the lack of scalable representations, however, the prediction of a rock's CO2 storage potential has been challenging. Here, we report how to represent a rock's pore space by statistically sampled capillary networks (ssCN) that preserve morphological rock characteristics. We have used the ssCN method to simulate CO2 drainage within a representative sandstone sample at reservoir pressures and temperatures, exploring intermediate- and CO2-wet conditions. This wetting regime is often neglected, despite evidence of plausibility. By raising pressure and temperature we observe increasing CO2 penetration within the capillary network. For contact angles approaching 90 degrees, the CO2 saturation exhibits a pronounced maximum reaching 80 percent of the accessible pore volume. This is about twice as high as the saturation values reported previously. For enabling validation of our results and a broader application of our methodology, we have made available the rock tomography data, the digital rock computational workflows, and the ssCN models used in this study. |
| title | Optimizing carbon dioxide trapping for geological storage |
| topic | Applied Physics Computational Physics |
| url | https://arxiv.org/abs/2312.13512 |