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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2407.02505 |
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| _version_ | 1866917711485861888 |
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| author | Liu, Xinliang Yang, Xia Zhang, Chen-Song Zhang, Lian Zhao, Li |
| author_facet | Liu, Xinliang Yang, Xia Zhang, Chen-Song Zhang, Lian Zhao, Li |
| contents | This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media. The architecture is adjusted to manage a variety of crucial factors, such as permeability and porosity heterogeneity. The study extendes MgNO to time-dependent porous media flow problems and validate its accuracy in predicting essential aspects of multiphase flows. Furthermore, the research provides a detailed comparison between MgNO and Fourier Neural Opeartor (FNO), which is one of the most popular neural operator methods, on their performance regarding prediction error accumulation over time. This aspect provides valuable insights into the models' long-term predictive stability and reliability. The study demonstrates MgNO's capability to effectively simulate multiphase flow problems, offering considerable time savings compared to traditional simulation methods, marking an advancement in integrating data-driven methodologies in geoscience applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02505 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A MgNO Method for Multiphase Flow in Porous Media Liu, Xinliang Yang, Xia Zhang, Chen-Song Zhang, Lian Zhao, Li Computational Engineering, Finance, and Science Machine Learning Fluid Dynamics This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media. The architecture is adjusted to manage a variety of crucial factors, such as permeability and porosity heterogeneity. The study extendes MgNO to time-dependent porous media flow problems and validate its accuracy in predicting essential aspects of multiphase flows. Furthermore, the research provides a detailed comparison between MgNO and Fourier Neural Opeartor (FNO), which is one of the most popular neural operator methods, on their performance regarding prediction error accumulation over time. This aspect provides valuable insights into the models' long-term predictive stability and reliability. The study demonstrates MgNO's capability to effectively simulate multiphase flow problems, offering considerable time savings compared to traditional simulation methods, marking an advancement in integrating data-driven methodologies in geoscience applications. |
| title | A MgNO Method for Multiphase Flow in Porous Media |
| topic | Computational Engineering, Finance, and Science Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2407.02505 |