An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction

An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction

Abstract This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments u...

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Bibliographic Details
Main Authors: Yin-bo He, Ke-ming Sheng, Ming-liang Du, Guan-cheng Jiang, Teng-fei Dong, Lei Guo, Bo-tao Xu
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Deep learning
Intelligent drilling and completion
Complex predication
TabNet
Tabular data modeling
Sensitivity damage
Online Access:https://doi.org/10.1038/s41598-025-99659-5
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https://doi.org/10.1038/s41598-025-99659-5

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