Intelligent and efficient lithology identification based on image features of returned cuttings

被引：0
|
作者：
Xia W. [1 ]
Xie W. [1 ]
Tang Y. [1 ]
Li G. [2 ]
Han Y. [3 ]
机构：
[1] School of Electrical Engineering and Information, Southwest Petroleum University, Sichuan, Chengdu
[2] Petroleum Engineering School, Southwest Petroleum University, Sichuan, Chengdu
[3] SINOPEC Research Institute of Petroleum Engineering Technology Co., Ltd, Beijing
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2023年 / 58卷 / 03期
关键词：
cuttings logging; image characteristics of rock debris particles; lithology intelligent recognition; machine vision; the image of returned cuttings;
D O I：
10.13810/j.cnki.issn.1000-7210.2023.03.002
中图分类号：
学科分类号：
摘要：
In the process of cutting logging, the lithology of cuttings is mainly manually analyzed, which is inefficient and unstable, and it is difficult to quickly identify the lithology change of the stratum during the drilling. Therefore, an intelligent lithology identification method based on the texture, color, and shape characteristics of rock particles in returned cuttings images is proposed. Firstly, the gradient of the pixel value of the returned cuttings image is calculated, and the particle centroid is obtained. The contour line of the rock particles is obtained and marked by using the watershed algorithm. Then the image segmentation algorithm is used to separate the single rock particle image to be detected from the returned cuttings image, and a sample library of rock particle images is established. Finally, the MobileNetV2 network is improved by using the attention mechanism and feature fusion module to extract and classify the features of rock particles, so as to identify the lithology of a single rock particle image and then obtain the composition ratio of the returned cuttings sample. The proposed method transforms the overall identification method of returned cuttings, which is often used in the intelligent identification of lithology, into the lithology identification method of a single rock particle in returned cuttings, which greatly filters out the mutual interference of rock particles. The test results of returned cuttings images collected from several oil and gas blocks show that the proposed method can reach an accuracy of more than 92% in identifying limestone, mudstone, sandstone, and shale, and the time consumption for lithology analysis of a group of returned cuttings images is less than 10s. © 2023 Science Press. All rights reserved.
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页码：495 / 506
页数：11
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