Identification and comparison of organic matter-hosted pores in shale by SEM image analysis—a deep learning-based approach

被引：0
|
作者：
Chen Z. [1 ]
Tang X. [1 ,2 ]
Liang G. [2 ]
Guan Z. [2 ]
机构：
[1] School of Energy Resources, China University of Geosciences (Beijing), Beijing
[2] Key Laboratory for Strategic Evaluation of Shale Gas Resources, Ministry of Natural Resources, China University of Geosciences (Beijing), Beijing
来源：
Earth Science Frontiers | 2023年 / 30卷 / 03期
关键词：
deep leaning; fracture; organic matter-hosed pore; pyrite; scanning electron microscope image; shale; U-Net;
D O I：
10.13745/j.esf.sf.2022.5.45
中图分类号：
学科分类号：
摘要：
The introduction of deep learning models can greatly improve the efficiency of geological image analysis and thus increase the level of quantitative research. As an example, the Ar-ion polishing scanning electron microscope (SEM) images of shale samples from the Lower Cambrian Niutitang Formation in western Hubei, Upper Yangtze were analyzed using three deep learning models, Mask-RCNN, FCN and U-Net, to identify the minerals, organic matter and pores (basic tasks) after image pretreatment (binarization, etc.) We compared the running time and identification accuracy between the three models, and discussed the model applicability and model differences in geological image recognition and processing. In addition, we compared the best performance model, U-Net model, with the general image processing softwares (JmicroVision, Adobe Photoshop, etc.) in pore recognition. The FCN model performed well in the basic tasks, but could not distinguish the mineral components and fractures with similar colors; whereas the Mask-RCNN model could identify the main minerals with strong segmentation but not low-resolution pores and fractures. In comparison, the U-Net model greatly improved the efficiency of shale geological image recognition with an 300-fold increase in image recognition speed over the general image processing softwares. Applying the U-Net model, the pore structural types of the Niutitang shale of the study area can be divided into circular intra-granular mineral pores, random irregular inter-granular mineral pores, angular organic matter-hosted pores and dense organic matter-hosted micropores. The pore structural parameters obtained based on SEM image analysis of large enough sample size may be used for reservoir classification and evaluation. The example provided in this study may help improving the efficiency of geological image research as well as promoting artificial intelligence application in oil and gas research. © 2023 Science Frontiers editorial department. All rights reserved.
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页码：208 / 220
页数：12
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