Image of A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images

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A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images

Porosity, tortuosity, specific surface area (SSA), and permeability are four key parameters of reactive transport modeling in sandstone, which are important for understanding solute transport and geochemical reaction processes in sandstone aquifers. These four parameters reflect the characteristics of pore structure of sandstone from different perspectives, and the traditional empirical formulas cannot make accurate predictions of them due to their complexity and heterogeneity. In this paper, eleven types of sandstone CT images were firstly segmented into numerous subsample images, the porosity, tortuosity, SSA, and permeability of the subsamples were calculated, and the dataset was established. The 3D convolutional neural network (CNN) models were subsequently established and trained to predict the key reactive transport parameters based on subsample CT images of sandstones. The results demonstrated that the 3D CNN model with multiple outputs exhibited excellent prediction ability for the four parameters compared to the traditional empirical formulas. In particular, for the prediction of tortuosity and permeability, the 3D CNN model with multiple outputs even showed slightly better prediction ability than its single-output variant model. Additionally, it demonstrated good generalization performance on sandstone CT images not included in the training dataset. The study showed that the 3D CNN model with multiple outputs has the advantages of simplifying operation and saving computational resources, which has the prospect of popularization and application.

Ketersediaan
312551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
10 hlm PDF, 54.556 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.5, December 2024
Subjek
Permeability
Tortuosity
Reactive transport
CNN model with multiple outputs
Sandstone
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images
    Porosity, tortuosity, specific surface area (SSA), and permeability are four key parameters of reactive transport modeling in sandstone, which are important for understanding solute transport and geochemical reaction processes in sandstone aquifers. These four parameters reflect the characteristics of pore structure of sandstone from different perspectives, and the traditional empirical formulas cannot make accurate predictions of them due to their complexity and heterogeneity. In this paper, eleven types of sandstone CT images were firstly segmented into numerous subsample images, the porosity, tortuosity, SSA, and permeability of the subsamples were calculated, and the dataset was established. The 3D convolutional neural network (CNN) models were subsequently established and trained to predict the key reactive transport parameters based on subsample CT images of sandstones. The results demonstrated that the 3D CNN model with multiple outputs exhibited excellent prediction ability for the four parameters compared to the traditional empirical formulas. In particular, for the prediction of tortuosity and permeability, the 3D CNN model with multiple outputs even showed slightly better prediction ability than its single-output variant model. Additionally, it demonstrated good generalization performance on sandstone CT images not included in the training dataset. The study showed that the 3D CNN model with multiple outputs has the advantages of simplifying operation and saving computational resources, which has the prospect of popularization and application.
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