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OA北大核心CSTPCD

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.