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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.06305 |
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| _version_ | 1866908308269432832 |
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| author | Zeng, Shiqin Li, Haoyun Gahlot, Abhinav Prakash Herrmann, Felix J. |
| author_facet | Zeng, Shiqin Li, Haoyun Gahlot, Abhinav Prakash Herrmann, Felix J. |
| contents | This study investigates the use of score-based generative models for reservoir simulation, with a focus on reconstructing spatially varying permeability and saturation fields in saline aquifers, inferred from sparse observations at two well locations. By modeling the joint distribution of permeability and saturation derived from high-fidelity reservoir simulations, the proposed neural network is trained to learn the complex spatiotemporal dynamics governing multiphase fluid flow in porous media. During inference, the framework effectively reconstructs both permeability and saturation fields by conditioning on sparse vertical profiles extracted from well log data. This approach introduces a novel methodology for incorporating physical constraints and well log guidance into generative models, significantly enhancing the accuracy and physical plausibility of the reconstructed subsurface states. Furthermore, the framework demonstrates strong generalization capabilities across varying geological scenarios, highlighting its potential for practical deployment in data-scarce reservoir management tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Well2Flow: Reconstruction of reservoir states from sparse wells using score-based generative models Zeng, Shiqin Li, Haoyun Gahlot, Abhinav Prakash Herrmann, Felix J. Machine Learning Artificial Intelligence This study investigates the use of score-based generative models for reservoir simulation, with a focus on reconstructing spatially varying permeability and saturation fields in saline aquifers, inferred from sparse observations at two well locations. By modeling the joint distribution of permeability and saturation derived from high-fidelity reservoir simulations, the proposed neural network is trained to learn the complex spatiotemporal dynamics governing multiphase fluid flow in porous media. During inference, the framework effectively reconstructs both permeability and saturation fields by conditioning on sparse vertical profiles extracted from well log data. This approach introduces a novel methodology for incorporating physical constraints and well log guidance into generative models, significantly enhancing the accuracy and physical plausibility of the reconstructed subsurface states. Furthermore, the framework demonstrates strong generalization capabilities across varying geological scenarios, highlighting its potential for practical deployment in data-scarce reservoir management tasks. |
| title | Well2Flow: Reconstruction of reservoir states from sparse wells using score-based generative models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.06305 |