Integrated static modeling and dynamic simulation framework for CO2 storage capacity in Upper Qishn Clastics, S1A reservoir, Yemen
Carbon dioxide (CO 2 ) capture and storage (CCS) is presented as an alternative measure and promising approach to mitigate large-scale anthropogenic CO 2 emissions into the atmosphere. In this context, CO 2 sequestration into depleted oil reservoirs is a practical approach, as it boosts the oil reco...
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Veröffentlicht in: | Geomechanics and geophysics for geo-energy and geo-resources. 2022-02, Vol.8 (1), Article 2 |
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Sprache: | eng |
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Zusammenfassung: | Carbon dioxide (CO
2
) capture and storage (CCS) is presented as an alternative measure and promising approach to mitigate large-scale anthropogenic CO
2
emissions into the atmosphere. In this context, CO
2
sequestration into depleted oil reservoirs is a practical approach, as it boosts the oil recovery and facilitates the permanent storing of CO
2
into the candidate sites. However, the estimation of CO
2
storage capacity in the subsurface is a challenge to kick-start CCS worldwide. Thus, this paper proposes an integrated static and dynamic modeling framework to tackle the challenge of CO
2
storage capacity in the Upper Qishn Formation of the S1A reservoir in the Masila Basin, Yemen. To achieve this work's ultimate goal, the geostatistical modeling was integrated with open-source code (MRST-CO
2
lab) for reducing the uncertainty assessment of CO
2
storage capacity. Also, there is a significant difference between static and dynamic CO
2
storage capacity. The static CO
2
storage capacity varies from 4.54 to 81.98 million tons, while the dynamic CO
2
simulation is estimated from 4.95 to 17.92 million tons. Based on the geological uncertainty assessment of three ranked realizations (P10, P50, P90), our work found that the Upper Qishn sequence of the S1A reservoir could store 15.64 million tons without leakage. This finding demonstrates that the S1A reservoir has the potential for geological CO
2
storage. Ultimately, this study proposes a useful modeling framework that is easy to adapt for other reservoirs in the Masila Basin in Yemen.
Graphical abstract |
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ISSN: | 2363-8419 2363-8427 |
DOI: | 10.1007/s40948-021-00305-x |