Intelligent segmentation and quantitative characterization of shale microstructure based on deep learning:A case study of second member of Liushagang Formation in Beibu Gulf Basin
The research on the intelligent characterization method of shale microstructure is one of the current research hotspots,and it is an important means for the spatial quantitative characterization of shale reservoirs and is of great significance for shale oil exploration and development.In this study,the low-maturity shale of the second member of the Liushagang Formation in the Beibu Gulf Basin of China was taken as the research object.The Shale-net deep learning model developed based on the deep learning theory and the physical experiment data such as CT and FIB-SEM confirmed the reliability of the model and the effectiveness of image processing.The results show that the mean intersection over union(mIoU)between the predicted region and the real region of the deep learning model can reach a maximum of 0.899 8,or in other words,the accuracy can reach 89.98％.The Shale-net deep learning model has a good segmentation effect on pore fractures,organic matter,clay minerals,quartz,and pyrite.The maximum values of mIoU are 0.943 8,0.952 9,0.859 2,0.844 6,and 0.980 0,respectively,and the segmentation accuracy of pore fractures,organic matter,and pyrite exceeds 90％.The segmentation accuracy of clay minerals and quartz with complex characteristics also reaches about 85％.In addition,it can be seen from the segmentation effect that the Shale-net deep learning model can segment different substances more accurately,which proves that the Shale-net deep learning model has better performance than the traditional threshold segmentation and watershed methods in the intelligent characterization of shale microstructure.The results of quantitative characterization by different methods show that the quantitative results of the Shale-net deep learning model are closer to those of digital cores.It is proven that the Shale-net deep learning model has a better identification ability for oil shale reservoir structures and minerals.This model can be used as an effective means for intelligent segmentation and quantitative characterization of shale microstructure.
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