Learning Pore-scale Multiphase Flow from 4D Velocimetry
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918386313723904 |
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| author | Wang, Chunyang Zhu, Linqi Gu, Yuxuan van der Merwe, Robert Ju, Xin Spurin, Catherine Krevor, Samuel Ying, Rex Pfaff, Tobias Blunt, Martin J. Bultreys, Tom Wen, Gege |
| author_facet | Wang, Chunyang Zhu, Linqi Gu, Yuxuan van der Merwe, Robert Ju, Xin Spurin, Catherine Krevor, Samuel Ying, Rex Pfaff, Tobias Blunt, Martin J. Bultreys, Tom Wen, Gege |
| contents | Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce a multimodal learning framework that infers multiphase pore-scale flow directly from time-resolved four-dimensional (4D) micro-velocimetry measurements. The model couples a graph network simulator for Lagrangian tracer-particle motion with a 3D U-Net for voxelized interface evolution. The imaged pore geometry serves as a boundary constraint to the flow velocity and the multiphase interface predictions, which are coupled and updated iteratively at each time step. Trained autoregressively on experimental sequences in capillary-dominated conditions ($Ca\approx10^{-6}$), the learned surrogate captures transient, nonlocal flow perturbations and abrupt interface rearrangements (Haines jumps) over rollouts spanning seconds of physical time, while reducing hour-to-day--scale direct numerical simulations to seconds of inference. By providing rapid, experimentally informed predictions, the framework opens a route to ''digital experiments'' to replicate pore-scale physics observed in multiphase flow experiments, offering an efficient tool for exploring injection conditions and pore-geometry effects relevant to subsurface carbon and hydrogen storage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12516 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Pore-scale Multiphase Flow from 4D Velocimetry Wang, Chunyang Zhu, Linqi Gu, Yuxuan van der Merwe, Robert Ju, Xin Spurin, Catherine Krevor, Samuel Ying, Rex Pfaff, Tobias Blunt, Martin J. Bultreys, Tom Wen, Gege Machine Learning Fluid Dynamics Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce a multimodal learning framework that infers multiphase pore-scale flow directly from time-resolved four-dimensional (4D) micro-velocimetry measurements. The model couples a graph network simulator for Lagrangian tracer-particle motion with a 3D U-Net for voxelized interface evolution. The imaged pore geometry serves as a boundary constraint to the flow velocity and the multiphase interface predictions, which are coupled and updated iteratively at each time step. Trained autoregressively on experimental sequences in capillary-dominated conditions ($Ca\approx10^{-6}$), the learned surrogate captures transient, nonlocal flow perturbations and abrupt interface rearrangements (Haines jumps) over rollouts spanning seconds of physical time, while reducing hour-to-day--scale direct numerical simulations to seconds of inference. By providing rapid, experimentally informed predictions, the framework opens a route to ''digital experiments'' to replicate pore-scale physics observed in multiphase flow experiments, offering an efficient tool for exploring injection conditions and pore-geometry effects relevant to subsurface carbon and hydrogen storage. |
| title | Learning Pore-scale Multiphase Flow from 4D Velocimetry |
| topic | Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2603.12516 |