Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911790131052544 |
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| author | Ballini, Enrico Formaggia, Luca Fumagalli, Alessio Scotti, Anna Zunino, Paolo |
| author_facet | Ballini, Enrico Formaggia, Luca Fumagalli, Alessio Scotti, Anna Zunino, Paolo |
| contents | We apply reduced-order modeling (ROM) techniques to single-phase flow in faulted porous media, accounting for changing rock properties and fault geometry variations using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework that effectively manages the resulting non-conforming mesh. To streamline complex and repetitive calculations such as sensitivity analysis and solution of inverse problems, we utilize the Deep Learning Reduced Order Model (DL-ROM). This non-intrusive neural network-based technique is evaluated against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios, demonstrating DL-ROM's capacity to expedite complex analyses with promising accuracy and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03678 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media Ballini, Enrico Formaggia, Luca Fumagalli, Alessio Scotti, Anna Zunino, Paolo Numerical Analysis We apply reduced-order modeling (ROM) techniques to single-phase flow in faulted porous media, accounting for changing rock properties and fault geometry variations using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework that effectively manages the resulting non-conforming mesh. To streamline complex and repetitive calculations such as sensitivity analysis and solution of inverse problems, we utilize the Deep Learning Reduced Order Model (DL-ROM). This non-intrusive neural network-based technique is evaluated against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios, demonstrating DL-ROM's capacity to expedite complex analyses with promising accuracy and efficiency. |
| title | Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2403.03678 |