A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Wang, Ziyan; Battiato, Ilenia

doi:10.1016/j.advwatres.2023.104588

Advances in Water Resources

2024

DOI: 10.1016/j.advwatres.2023.104588

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A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Ziyan Wang,

Ilenia Battiato

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Cited by 3 publications

References 53 publications

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Deep learning-based pore network generation: Numerical insights into pore geometry effects on microstructural fluid flow behaviors of unconventional resources

Guo,

Xiao,

Martyushev

et al. 2024

Energy

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Deep learning-based pore network generation: Numerical insights into pore geometry effects on microstructural fluid flow behaviors of unconventional resources

Guo,

Xiao,

Martyushev

et al. 2024

Energy

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A machine learning assisted upscaling method for the Arrhenius kinetic model, with application to the in-situ conversion process

Tan,

Li,

Liu

et al. 2025

Fuel

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A Learning‐Based Multiscale Model for Reactive Flow in Porous Media

Karimi,

Bhattacharya

2024

Water Resources Research

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We study solute‐laden flow through permeable geological formations with a focus on surface reactions that lead to changes in flow and formation. As the fluid flows through the permeable medium, it reacts with the medium, thereby changing the morphology and properties of the medium; this in turn, affects the flow conditions and chemistry. These phenomena occur at various lengths and time scales and make the problem extremely complex. Multiscale modeling addresses this complexity by dividing the problem into those at individual scales, and systematically passing information from one scale to another. However, accurate implementation of these multiscale methods is still prohibitively expensive. We present a methodology to overcome this challenge that is computationally efficient and quantitatively accurate. We introduce a surrogate for the solution operator of the lower scale problem in the form of a recurrent neural operator, train it using one‐time off‐line data generated by repeated solutions of the lower scale problem, and then use this surrogate in application‐scale calculations. The result is the accuracy of concurrent multiscale methods, at a cost comparable to those of classical models. We study various examples, and show the efficacy of this method in understanding the evolution of the morphology, properties and flow conditions over time in geological formations.

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A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Cited by 3 publications

References 53 publications

Deep learning-based pore network generation: Numerical insights into pore geometry effects on microstructural fluid flow behaviors of unconventional resources

Deep learning-based pore network generation: Numerical insights into pore geometry effects on microstructural fluid flow behaviors of unconventional resources

A machine learning assisted upscaling method for the Arrhenius kinetic model, with application to the in-situ conversion process

A Learning‐Based Multiscale Model for Reactive Flow in Porous Media

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