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Updated: Jul 24, 2026

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

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基于深度学习的紧密水库的断裂效应评估

Ankang Feng1, Yuxin Ke1, Chuang Hei1

  • 1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括

本研究引入了一种深度学习方法,用于评估紧密的石油和天然气储库中的液压压裂效应. 这种新方法使用ResNet与SE模块,达到99.75%的准确性,改进了传统方法.

关键词:
在美国,CNN是CNN.深度学习是一种深度学习.断裂的预测 断裂的预测生成性的对抗性网络.

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