- April 29, 2022
- Posted by: M. Sadegh Riasi
- Category: Research News
![Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi-Scale 3D Convolutional Neural Network 1 Elmorsy et al 2022 - Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi-Scale 3D Convolutional Neural Network](https://www.interpore.org/wp-content/uploads/2022/05/Elmorsy-et-al-2022.png)
Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi-Scale 3D Convolutional Neural Network
Mohamed Elmorsy, Wael El-Dakhakhni, Benzhong Zhao
Subsurface characterization is critical in understanding and controlling many natural and industrial processes. While recent advances in 3D imaging have enabled digital subsurface characterization, the exorbitant computational cost associated with direct numerical simulation remains a persistent challenge. Here, we introduce a novel 3D convolutional neural network (CNN) for end-to-end prediction of permeability. We show that increasing the dataset size and diversity, utilizing multi-scale feature aggregation, and optimizing network architecture elevate the model accuracy beyond that of existing state-of-the-art 3D CNN models. We demonstrate that the model is generalizable and capable of predicting the permeability of previously unseen samples with excellent accuracy.
Water Resources Research 58, (2022)
Corresponding Author: Benzhong Zhao
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