Diffusion-based subsurface CO$_2$ multiphysics monitoring and forecasting
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arXiv
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| Format: | Preprint |
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2024
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| author | Huang, Xinquan Wang, Fu Alkhalifah, Tariq |
| author_facet | Huang, Xinquan Wang, Fu Alkhalifah, Tariq |
| contents | Carbon capture and storage (CCS) plays a crucial role in mitigating greenhouse gas emissions, particularly from industrial outputs. Using seismic monitoring can aid in an accurate and robust monitoring system to ensure the effectiveness of CCS and mitigate associated risks. However, conventional seismic wave equation-based approaches are computationally demanding, which hinders real-time applications. In addition to efficiency, forecasting and uncertainty analysis are not easy to handle using such numerical-simulation-based approaches. To this end, we propose a novel subsurface multiphysics monitoring and forecasting framework utilizing video diffusion models. This approach can generate high-quality representations of CO$2$ evolution and associated changes in subsurface elastic properties. With reconstruction guidance, forecasting and inversion can be achieved conditioned on historical frames and/or observational data. Meanwhile, due to the generative nature of the approach, we can quantify uncertainty in the prediction. Tests based on the Compass model show that the proposed method successfully captured the inherently complex physical phenomena associated with CO$_2$ monitoring, and it can predict and invert the subsurface elastic properties and CO$_2$ saturation with consistency in their evolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18426 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion-based subsurface CO$_2$ multiphysics monitoring and forecasting Huang, Xinquan Wang, Fu Alkhalifah, Tariq Geophysics Machine Learning Carbon capture and storage (CCS) plays a crucial role in mitigating greenhouse gas emissions, particularly from industrial outputs. Using seismic monitoring can aid in an accurate and robust monitoring system to ensure the effectiveness of CCS and mitigate associated risks. However, conventional seismic wave equation-based approaches are computationally demanding, which hinders real-time applications. In addition to efficiency, forecasting and uncertainty analysis are not easy to handle using such numerical-simulation-based approaches. To this end, we propose a novel subsurface multiphysics monitoring and forecasting framework utilizing video diffusion models. This approach can generate high-quality representations of CO$2$ evolution and associated changes in subsurface elastic properties. With reconstruction guidance, forecasting and inversion can be achieved conditioned on historical frames and/or observational data. Meanwhile, due to the generative nature of the approach, we can quantify uncertainty in the prediction. Tests based on the Compass model show that the proposed method successfully captured the inherently complex physical phenomena associated with CO$_2$ monitoring, and it can predict and invert the subsurface elastic properties and CO$_2$ saturation with consistency in their evolution. |
| title | Diffusion-based subsurface CO$_2$ multiphysics monitoring and forecasting |
| topic | Geophysics Machine Learning |
| url | https://arxiv.org/abs/2407.18426 |