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Updated: Sep 16, 2025

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
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Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

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监督机器学习和无监督数据压缩模型的应用,用于采用钻探,石物理和井日志数据进行孔隙压力预测

Abu Bakker Siddique1, Tanveer Alam Munshi1, Nazmul Islam Rakin1

  • 1Department of Petroleum and Mining Engineering, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.

Scientific reports
|July 9, 2025
PubMed
概括

这项研究引入了一种新的混合堆叠机器学习模型,用于准确预测水库孔隙压力,其性能优于传统方法. 数据驱动的方法通过使用多样化的井日志和钻探数据来提高井口稳定性和钻探安全性.

关键词:
钻探优化 钻探优化机器学习 机器学习孔隙压力是指孔隙压力.主要组件分析的主要组件分析.储水池的表征 储水池的表征堆叠模型的堆叠模型

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Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography12:18

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

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