Classifying reservoir facies using attention-based residual neural networks
The accurate classification of reservoir facies remains a fundamental challenge in petroleum geoscience, with significant implications for resource extraction efficiency and reservoir characterization. Traditional approaches relying on manual interpretation and conventional machine learning methods...
Saved in:
Main Authors: | An Hai Nguyen, Khang Nguyen, Nga Mai |
---|---|
Format: | Article |
Language: | English |
Published: |
PeerJ Inc.
2025-07-01
|
Series: | PeerJ Computer Science |
Subjects: | |
Online Access: | https://peerj.com/articles/cs-2977.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Carbonate Seismic Facies Analysis in Reservoir Characterization: A Machine Learning Approach with Integration of Reservoir Mineralogy and Porosity
by: Papa Owusu, et al.
Published: (2025-07-01) -
Applying spectral decomposition to seismic facies clustering with unsupervised machine learning
by: Р. Маліков, et al.
Published: (2025-06-01) -
Graph Attention Neural Network Model With Behavior Features for Knowledge Tracking
by: Wei Zhang, et al.
Published: (2023-01-01) -
Geochemical facies analysis /
by: Ernst, Werner
Published: (1970) -
NFFNet: A Deep Learning Framework for Fault Diagnosis Using Classifier-Guided Graph Attention Networks and Blind Deconvolution
by: Hongfei Yao, et al.
Published: (2025-01-01)