地质与资源2025，Vol.34Issue(1)：61-69,9.DOI:10.13686/j.cnki.dzyzy.2025.01.007
基于深度学习算法的致密储层薄片图像颗粒、孔隙智能表征方法研究
Intelligent characterization of particles and pores in thin slice images of tight reservoirs based on deep learning algorithm
摘要
Abstract
In the thin-section image analysis of tight sandstone reservoir,to solve the problems such as low accuracy and heavy work of traditional methods,TransUnet and Unet neural networks by combining Transformer with convolutional neural network(CNN)are used for efficient characterization of particles and pores.The TransUnet has excellent performance in particle characterization.The experiment shows that the intersection over union(IoU)reaches 0.86,with the recall rate of 0.824 and precision of 0.839,which is superior to traditional methods,proving its effectiveness in tight particle segmentation.The Unet shows efficient characterization of pores as well,with the IoU of 0.824,recall rate of 0.843 and precision of 0.953.Besides,experiment indicates that although porosity affects IoU,the model still maintains high efficiency and accuracy generally.These results fully demonstrate that deep learning method,especially TransUnet,is significantly effective in accurate segmentation of thin section images of complex tight reservoir,providing new ideas for the study of unconventional tight reservoir and showing its great potential in the field of geology.关键词
深度学习/薄片分析/致密储层/粒度分析/TransUnetKey words
deep learning/thin section analysis/tight reservoir/particle size analysis/TransUnet分类
天文与地球科学引用本文复制引用
王金焕,许承武,乔宏亮,唐露,刘天勇,曲端刚,徐坚,孟英杰,李乙鸿..基于深度学习算法的致密储层薄片图像颗粒、孔隙智能表征方法研究[J].地质与资源,2025,34(1):61-69,9.基金项目
国家自然基金面上项目"原位加热下页岩储层孔-裂隙动态演化机制研究"(42172163). （42172163）
