Application of sparry grain limestone petrographic analysis combining image processing and deep learning

Abstract

Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images.

Keywords

• carbonate rock
• petrography
• convolutional neural network
• deep learning
• artificial intelligence