基于改进的 U-net 深度学习网络模型和扫描电子显微镜图像实现了孔隙表征

IF 1.3 4区工程技术 Q4 ENERGY & FUELS

Petroleum Science and Technology Pub Date : 2024-03-14 DOI:10.1080/10916466.2024.2326178

Xiangru Chen, Xin Tang, Junjie Xiong, Ruiyu He, Biao Wang

引用次数: 0

摘要

扫描电镜图像法常用于页岩孔隙的定性表征。通过扫描电镜图像可以直观地观察到页岩微储层孔隙的发育情况，但扫描电镜图像法的效率较低。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Pore characterization was achieved based on the improved U-net deep learning network model and scanning electron microscope images

The SEM image method is commonly used in the qualitative characterization of shale pores. The development of shale micro-reservoir pores can be visually observed through SEM images, but the efficie...

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来源期刊

Petroleum Science and Technology 工程技术-工程：化工

CiteScore

2.90

自引率

13.30%

发文量

277

审稿时长

2.7 months

期刊介绍： The international journal of Petroleum Science and Technology publishes original, high-quality peer-reviewed research and review articles that explore: -The fundamental science of fluid-fluid and rock-fluids interactions and multi-phase interfacial and transport phenomena through porous media related to advanced petroleum recovery processes, -The application of novel concepts and processes for enhancing recovery of subsurface energy resources in a carbon-sensitive manner, -Case studies of scaling up the laboratory research findings to field pilots and field-wide applications. -Other salient technological challenges facing the petroleum industry.