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

Abstract Accurately capturing the complex interaction between CO 2 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 CO 2 and water at the pore scale. The dataset consists of 624 2D samples, each of size 512 × 512 with a resolution of 35 μ m, covering 100 time steps under a constant CO 2 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.