Advanced workflow for time-lapse seismic monitoring of $$\hbox {CO}_2$$ storage in saline aquifers with its application in a field basin
Abstract Accurately characterizing $$\hbox {CO}_2$$ sequestration and migration post-injection is crucial to the success of carbon capture and storage (CCS) projects. Time-lapse seismic monitoring technique is an effective tool; however, it can only reveal changes in elastic properties such as compr...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-09476-z |
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| Summary: | Abstract Accurately characterizing $$\hbox {CO}_2$$ sequestration and migration post-injection is crucial to the success of carbon capture and storage (CCS) projects. Time-lapse seismic monitoring technique is an effective tool; however, it can only reveal changes in elastic properties such as compressional wave velocity ( $$V_{\text {p}}$$ ) and quality factor ( $$Q_{\text {p}}$$ ). In contrast, reservoir simulation enables detailed tracking of fluid movement within the reservoir, allowing for precise simulation of $$\hbox {CO}_2$$ saturation. Thus, to enable a more accurate characterization of $$\hbox {CO}_2$$ migration, we develop an integrated workflow that closes the loop between reservoir saturation data and time-lapse seismic data, which operate at different resolution scales. First, we build a realistic geological model for $$\hbox {CO}_2$$ storage based on the field information from typical saline aquifers in the Pearl River Mouth Basin (PRMB). Then, using rock physics theory, we establish relationships between $$\hbox {CO}_2$$ saturation and seismic properties ( $$V_{\text {p}}$$ and $$Q_{\text {p}}$$ ) to construct seismic models. Subsequently, we employ time-lapse seismic techniques to analyze the effects of $$\hbox {CO}_2$$ saturation changes on seismic data and quantitatively estimate these effects using the spectral-ratio method. Finally, the workflow developed in this study efficiently addresses challenges associated with varying observational scales and interdisciplinary research. It offers a valuable approach for predicting and detecting early $$\hbox {CO}_2$$ leakage based on known reservoir properties. This dataset will be available as an open-access resource, providing a valuable tool for testing and advancing research in the CCS field. |
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| ISSN: | 2045-2322 |