A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 26 references that are in the map. First posted on arXiv 22 Mar 2025.
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6 of 26 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2021U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flowcited by 297
- 2018Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantificationcited by 199
- 2022Real-time high-resolution CO2 geological storage prediction using nested Fourier neural operatorscited by 47
- 2021CCSNet: a deep learning modeling suite for CO2 storagecited by 13
- 2024Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogatescited by 3
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Citing papers per year: 2026: 1.
1 paper.