[2503.17592] A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction

Physics > Chemical Physics arXiv:2503.17592 (physics) [Submitted on 22 Mar 2025 (v1), last revised 27 Feb 2026 (this version, v3)] Title:A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction Authors:Alhasan Abdellatif, Hannah P. Menke, Julien Maes, Ahmed H. Elsheikh, Florian Doster View a PDF of the paper titled A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction, by Alhasan Abdellatif and 3 other authors View PDF HTML (experimental) Abstract: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 {\mu}m, 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. Subjects: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Computational Physics (physics.comp-ph) Cite as: arXiv:2503.17592 [physics.chem-ph]   (or arXiv:...