Ensemble Score Filter for Data Assimilation of Two-Phase Flow Models in Porous Media
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912369524867072 |
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| author | Hu, Ruoyu Poudel, Sanjeeb Bao, Feng Lee, Sanghyun |
| author_facet | Hu, Ruoyu Poudel, Sanjeeb Bao, Feng Lee, Sanghyun |
| contents | Numerical modeling and simulation of two-phase flow in porous media is challenging due to the uncertainties in key parameters, such as permeability. To address these challenges, we propose a computational framework by utilizing the novel Ensemble Score Filter (EnSF) to enhance the accuracy of state estimation for two-phase flow systems in porous media. The forward simulation of the two-phase flow model is implemented using a mixed finite element method, which ensures accurate approximation of the pressure, the velocity, and the saturation. The EnSF leverages score-based diffusion models to approximate filtering distributions efficiently, avoiding the computational expense of neural network-based methods. By incorporating a closed-form score approximation and an analytical update mechanism, the EnSF overcomes degeneracy issues and handles high-dimensional nonlinear filtering with minimal computational overhead. Numerical experiments demonstrate the capabilities of EnSF in scenarios with uncertain permeability and incomplete observational data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ensemble Score Filter for Data Assimilation of Two-Phase Flow Models in Porous Media Hu, Ruoyu Poudel, Sanjeeb Bao, Feng Lee, Sanghyun Numerical Analysis Numerical modeling and simulation of two-phase flow in porous media is challenging due to the uncertainties in key parameters, such as permeability. To address these challenges, we propose a computational framework by utilizing the novel Ensemble Score Filter (EnSF) to enhance the accuracy of state estimation for two-phase flow systems in porous media. The forward simulation of the two-phase flow model is implemented using a mixed finite element method, which ensures accurate approximation of the pressure, the velocity, and the saturation. The EnSF leverages score-based diffusion models to approximate filtering distributions efficiently, avoiding the computational expense of neural network-based methods. By incorporating a closed-form score approximation and an analytical update mechanism, the EnSF overcomes degeneracy issues and handles high-dimensional nonlinear filtering with minimal computational overhead. Numerical experiments demonstrate the capabilities of EnSF in scenarios with uncertain permeability and incomplete observational data. |
| title | Ensemble Score Filter for Data Assimilation of Two-Phase Flow Models in Porous Media |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2504.09245 |