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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2503.08410 |
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| _version_ | 1866911317187624960 |
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| author | Cirne, Marcos Menke, Hannah Abdellatif, Alhasan Maes, Julien Doster, Florian Elsheikh, Ahmed H. |
| author_facet | Cirne, Marcos Menke, Hannah Abdellatif, Alhasan Maes, Julien Doster, Florian Elsheikh, Ahmed H. |
| contents | Simulating reactive dissolution of solid minerals in porous media has many subsurface applications, including carbon capture and storage (CCS), geothermal systems and oil & gas recovery. As traditional direct numerical simulators are computationally expensive, it is of paramount importance to develop faster and more efficient alternatives. Deep-learning-based solutions, most of them built upon convolutional neural networks (CNNs), have been recently designed to tackle this problem. However, these solutions were limited to approximating one field over the domain (e.g. velocity field). In this manuscript, we present a novel deep learning approach that incorporates both temporal and spatial information to predict the future states of the dissolution process at a fixed time-step horizon, given a sequence of input states. The overall performance, in terms of speed and prediction accuracy, is demonstrated on a numerical simulation dataset, comparing its prediction results against state-of-the-art approaches, also achieving a speedup around $10^4$ over traditional numerical simulators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep-Learning Iterative Stacked Approach for Prediction of Reactive Dissolution in Porous Media Cirne, Marcos Menke, Hannah Abdellatif, Alhasan Maes, Julien Doster, Florian Elsheikh, Ahmed H. Machine Learning Simulating reactive dissolution of solid minerals in porous media has many subsurface applications, including carbon capture and storage (CCS), geothermal systems and oil & gas recovery. As traditional direct numerical simulators are computationally expensive, it is of paramount importance to develop faster and more efficient alternatives. Deep-learning-based solutions, most of them built upon convolutional neural networks (CNNs), have been recently designed to tackle this problem. However, these solutions were limited to approximating one field over the domain (e.g. velocity field). In this manuscript, we present a novel deep learning approach that incorporates both temporal and spatial information to predict the future states of the dissolution process at a fixed time-step horizon, given a sequence of input states. The overall performance, in terms of speed and prediction accuracy, is demonstrated on a numerical simulation dataset, comparing its prediction results against state-of-the-art approaches, also achieving a speedup around $10^4$ over traditional numerical simulators. |
| title | A Deep-Learning Iterative Stacked Approach for Prediction of Reactive Dissolution in Porous Media |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.08410 |