关键词: 荧光薄片, 微观剩余油, 卷积神经网络, 无监督学习, 特征聚类

Abstract: Fluorescent thin section is an important tool to study the properties, distribution characteristics and pore structure of crude oil in reservoirs. However, the data of fluorescent thin section is mainly processed by hand, so that the analysis efficiency is low and easily affected by human factors. This paper proposes an unsupervised automatic segmentation method based on convolution neural network(CNN). Firstly, fluorescent colors generated by different components under the excitation of ultraviolet light source were listed and used to establish fluorescent color chart and standard color system map, thus determining the division standard. Later, after extracting the advanced semantic features of fluorescent images by CNN, feature fusion was achieved through similarity and continuity constraints, and the space distance and angle of fluorescence spectrum was calculated to determine the similarity classification. Finally, the automatic division and quantitative analysis of particles, pores, oily asphalt, colloidal asphalt, and asphaltene in fluorescent images was completed. The experiment of fluorescence thin section images demonstrates that this approach does not rely on a substantial quantity of labeled samples and generally exhibits a low average error, thereby satisfying the practical production demands.

Key words: fluorescent thin section, microscopic remaining oil, convolutional neural network, unsupervised learning, feature clustering