Comparative analysis of sandstone microtomographic image segmentation using advanced convolutional neural networks with pixelwise and physical accuracy evaluation
Abstract The introduction of deep learning techniques has revolutionized the automated segmentation of digital rock images. These methods enable precise evaluations of critical properties such as porosity and fluid flow characteristics, thereby enhancing the efficiency of reservoir characterization....
Saved in:
| Main Authors: | Mazaher Hayatdavoudi, Mohammad Emami Niri, Ahmad Kalhor |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-07211-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Comparative Study of Three-Dimensional Flow Based, Geometric, and Empirical Tortuosity Models in Carbonate and Sandstone Reservoirs
by: Benedicta Loveni Melkisedek, et al.
Published: (2025-07-01) -
Numerical Investigation of Transmission and Sealing Characteristics of Salt Rock, Limestone, and Sandstone for Hydrogen Underground Energy Storage in Ontario, Canada
by: Peichen Cai, et al.
Published: (2025-02-01) -
Multiscale pore/throat characterization in tight sandstone formation with multi-threshold image segmentation algorithm
by: Hu Rongrong, et al.
Published: (2025-05-01) -
Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations
by: Jaehong Chung, et al.
Published: (2024-12-01) -
Integrating Laboratory-Measured Contact Angles into Time-Dependent Wettability-Adjusted LBM Simulations for Oil–Water Relative Permeability
by: Chenglin Liu, et al.
Published: (2025-05-01)