A fast curtain‐removal method for 3D FIB‐SEM images of heterogeneous minerals

Summary The focused ion beam‐scanning electron microscope (FIB‐SEM) system plays a crucial role in the research of shale reservoirs. It enables visualise nano‐scale pores and helps characterise unconventional reservoirs. In this system, FIB removes a thin layer and SEM generates a high‐resolution grey‐scale image of this fresh surface. By iteratively using FIB and SEM, we can create a series of 2D images. Through stacking these images, a 3D model of a rock sample is generated. However, curtain noise of varying intensity often appears in FIB‐SEM data. Its presence can cause a severe effect on estimation of rock properties, such as porosity and permeability, and lead to incorrect interpretation of the FIB‐SEM image. Because curtain noise can be falsely identified as needle‐shaped pore throats based on grey‐scale image segmentation. Thus it is imperative to decrease curtain noise before segmentation in order to obtain a better understanding of a rock sample. In this paper, we propose a novel approach considering mineral density to decrease curtain noise and compare its results with several conventional used methods. Lay description The focused ion beam‐scanning electron microscope (FIB‐SEM) system plays a crucial role in the research of shale reservoirs. It enables visualise nano‐scale pores and helps characterise unconventional reservoirs. In this system, FIB removes a thin layer and SEM generates a high‐resolution grey‐scale image of this fresh surface. By iteratively using FIB and SEM, we can create a series of 2D images. Through stacking these images, a 3D model of a rock sample is generated. However, curtain noise of varying intensity often appears in FIB‐SEM data. Its presence can cause a severe effect on estimation of rock properties, such as porosity and permeability, and lead to incorrect interpretation of the FIB‐SEM image. Because curtain noise can be falsely identified as needle‐shaped pore throats based on grey‐scale image segmentation. Thus it is imperative to decrease curtain noise before segmentation in order to obtain a better understanding of a rock sample. In this paper, we propose a novel approach considering mineral density to decrease curtain noise and compare its results with several conventional used methods.