Benchmark Dataset for Pore-Scale CO2-Water Interaction

2025-03-22

AI Summary

Accurately characterizing pore-scale CO₂–water two-phase dynamic interactions is critical for geoscientific applications such as carbon capture and storage (CCS). To address the scarcity of high-fidelity, spatiotemporally consistent benchmark data, this work introduces the first high-resolution pore-scale CO₂–water two-phase flow dataset. Generated via direct numerical simulation (DNS) coupled with multiscale pore modeling, it comprises 624 two-dimensional dynamic image sequences—each 512×512 pixels (35 μm resolution) and containing 100 temporally continuous frames—spanning multiple levels of pore heterogeneity. Crucially, the dataset ensures controlled heterogeneity, strict spatiotemporal coherence, and full reproducibility. It fills a critical gap in high-quality training and validation data for CO₂–water interface evolution modeling and permeability prediction, thereby enabling rigorous cross-configuration generalization assessment of machine learning models in subsurface flow simulation.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Machine Learning: Calibration & Uncertainty Quantification
• Application Domains: Humanities & Computational Social Science

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Capturing CO2-water interaction in porous media
• Providing high-resolution dataset for CCS applications
• Benchmarking machine learning models with heterogeneous samples

Innovation

• High-fidelity numerical simulations dataset
• 624 2D samples with 512x512 resolution
• Includes heterogeneity and temporal data