中国石油大学学报（自然科学版）2024，Vol.48Issue(4)：24-32,9.DOI:10.3969/j.issn.1673-5005.2024.04.003
基于空间注意力机制的Mask R-CNN致密储层岩石薄片图像鉴定
Image identification of rock slices of Mask R-CNN tight oil reservoir based on spatial attention mechanism
摘要
Abstract
Aiming at the difficult identification,high production cost,long time consumption and strong human subjective of rock thin section identification in continental tight reservoirs,a deep learning based artificial intelligence method for thin sec-tion identification of tight oil reservoirs was proposed by selecting the Upper Paleozoic in Linxing Block of Ordos Basin and Fuyu reservoir in Sanzhao Sag of Songliao Basin as target areas.Through the introduction of image preprocessing technology to remove the noise of rock slice image and unify the size of image pixels,a spatial geometry enhancement mechanism was constructed,and the Mask R-CNN algorithm was improved based on the spatial attention mechanism.The effectiveness of the above method was verified by applying it to the sample target area.The results show that the image preprocessing technology can effectively improve image quality and reduce noise interference under the premise of guaranteeing image features.The spatial geometry image augmentation mechanism can increase the number of available samples to some extent.The Mask R-CNN algorithm based on the spatial attention mechanism can simultaneously complete the segmentation and intelligent identi-fication of complex rock sheet components.The average accuracy of segmentation accuracy in different data sets is 89.2%,and the overall identification accuracy is 93%,which is applicable to the characterization of rock sheets in tight oil reser-voirs.关键词
致密储层/岩石薄片/深度学习/Mask R-CNN算法/分割与识别Key words
tight oil reservoir/rock thin section/deep learning/Mask R-CNN algorithm/segmentation and recognition分类
信息技术与安全科学引用本文复制引用
李春生,卢羿州,刘涛,刘宗堡,张可佳,刘芳,刘晓文,田梦晴,白玉磊,尹靖淞..基于空间注意力机制的Mask R-CNN致密储层岩石薄片图像鉴定[J].中国石油大学学报（自然科学版）,2024,48(4):24-32,9.基金项目
国家自然科学基金面上项目(42172161) （42172161）
国家青年科学基金项目(42102173) （42102173）
中国石油科技创新基金项目(2020D-5007- 0102) （2020D-5007- 0102）
黑龙江省优秀青年科学基金项目(YQ2020D001) （YQ2020D001）
黑龙江省自然科学基金项目(LH2020F003) （LH2020F003）
黑龙江省创新型科研人才培养计划项目(UNPYSCT-2020144) （UNPYSCT-2020144）
