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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.17592 |
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| _version_ | 1866917300101185536 |
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| author | Abdellatif, Alhasan Menke, Hannah P. Maes, Julien Elsheikh, Ahmed H. Doster, Florian |
| author_facet | Abdellatif, Alhasan Menke, Hannah P. Maes, Julien Elsheikh, Ahmed H. Doster, Florian |
| contents | Accurately capturing the complex interaction between CO2 and water in porous media at the pore scale is essential for various geoscience applications, including carbon capture and storage (CCS). We introduce a comprehensive dataset generated from high-fidelity numerical simulations to capture the intricate interaction between CO2 and water at the pore scale. The dataset consists of 624 2D samples, each of size 512x512 with a resolution of 35 μm, covering 100 time steps under a constant CO2 injection rate. It includes various levels of heterogeneity, represented by different grain sizes with random variation in spacing, offering a robust testbed for developing predictive models. This dataset provides high-resolution temporal and spatial information crucial for benchmarking machine learning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction Abdellatif, Alhasan Menke, Hannah P. Maes, Julien Elsheikh, Ahmed H. Doster, Florian Chemical Physics Machine Learning Computational Physics Accurately capturing the complex interaction between CO2 and water in porous media at the pore scale is essential for various geoscience applications, including carbon capture and storage (CCS). We introduce a comprehensive dataset generated from high-fidelity numerical simulations to capture the intricate interaction between CO2 and water at the pore scale. The dataset consists of 624 2D samples, each of size 512x512 with a resolution of 35 μm, covering 100 time steps under a constant CO2 injection rate. It includes various levels of heterogeneity, represented by different grain sizes with random variation in spacing, offering a robust testbed for developing predictive models. This dataset provides high-resolution temporal and spatial information crucial for benchmarking machine learning models. |
| title | A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction |
| topic | Chemical Physics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2503.17592 |