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Abstract Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid–solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.
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Flow Heterogeneity Controls Dissolution Dynamics in Topologically Complex Rocks
Abstract Rock dissolution is a common subsurface geochemical reaction affecting pore space properties, crucial for reservoir stimulation, carbon storage, and geothermal energy. Predictive models for dissolution remain limited due to incomplete understanding of the mechanisms involved. We examine the influence of flow, transport, and reaction regimes on mineral dissolution using 29 time‐resolved data from 3D rocks. We find that initial pore structure significantly influences the dissolution pattern, with reaction rates up to two orders of magnitude lower than batch conditions, given solute and fluid‐solid boundary constraints. Flow unevenness determines the location and rate of dissolution. We propose two models describing expected dissolution patterns and effective reaction rates based on dimensionless metrics for flow, transport, and reaction. Finally, we analyze feedback between evolving flow and pore structure to understand conditions that regulate/reinforce dissolution hotspots. Our findings underscore the major impact of flow arrangement on reaction‐front propagation and provide a foundation for controlling dissolution hotspots.
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- PAR ID:
- 10608258
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 52
- Issue:
- 8
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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