基于改进BlendMask的页岩扫描电镜图像矿物鉴定方法

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Abstract：The inteligent identification of shale scanning electron microscope （SEM） images can rapidly analyze shale reservoir minerals，which is one of the important means of predicting the“sweet spot”of shale oil reservoirs，and is also a future technological development trend. Traditional methods have problems such as low automation， low sample suitability，and limited feature extraction when identifying mineral components. To this end，this paper proposes a BlendMask-based SEM image characterization method for shale. Firstly，image preprocessng techniques such as bilateral filtering， Laplacian，and image normalization are used to denoise，sharpen，and unify the pixel of original images to improve the quality of training samples； Then， image augmentation methods such as rotation， scaling，and luminosity change are used to construct augmentation strategies to expand the number of datasets；And finally，the BlendMask network is improved by using the atention mechanism and the depth separable convolution which is used to realize the component segmentation and recognition of images.The experimental results of shale SEM images applied to Haita basin show that the segmentation accuracy and recall of the improved method are improved by O.02 -O.2O and O - 0.59， respectively，and the segmentation time is reduced by 1.29-2.70 s compared to the BlendMask model.

Key words： shale oil “sweet spot” reservoirs； BlendMask； scanning electron microscope images; mineralogical composition; segmentation and identification

0 引言

以页岩油为代表的非常规油气储层资源潜力巨大，准确评价页岩微观结构有助于提高储层评价精度和开发效率[1-2]。（剩余19669字）