Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning

Abstract

The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency.

Semantic Segmentation of Some Rock-Forming Mineral Thin Sections Using Deep Learning Algorithms: A Case Study from the Nikeiba Area, South Eastern Desert, Egypt

“…Finally, we have successfully developed a model capable of detecting more distinct classes of petrographic features, namely 11 classes, significantly surpassing the previous methodologies [23][24][25][26][27]40]. This improvement is primarily attributed to the utilization of the ResNet-34 architecture, which strikes an optimal balance between depth and computational efficiency.…”