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The pore types of intersalt shale reservoirs are diverse, and the pore structures are relatively complex. The size of the pores ranges from a few nanometers to a few microns, showing obvious heterogeneity and multiscale. Image segmentation is an important link in the study of micropore structures of intersalt shale using digital core technology. It can identify characteristics such as pore category, shape, size, spatial distribution, and connectivity characteristics. Therefore, how to improve the accuracy of image segmentation becomes very important. In this study, the research object is the 10 rhythmic layers of the Qian 34 oil group in the Wangping 1 well area of oil field A. First, focused ion beam scanning electron microscopy was used to obtain core imaging data. Then, in order to realize efficient processing of two-dimensional image information and compensate for the shortcomings of conventional segmentation methods such as long iteration period, slow convergence speed, and low accuracy, the backpropagation (BP) neural network segmentation method with a genetic algorithm (GA) was adopted. Also, the segmentation results before and after the improvement were compared. The results show the following: (1) Among the selected intersalt shale core samples, 90% of the pore radius is less than 150 nm and more than 90% of the throats are less than 100 nm. (2) Compared with the conventional BP neural network, the number of convergence steps is reduced to 10, the speed is 10 times faster, and the porosity prediction accuracy is increased by 4.03% on average, which is closer to the gas-measured porosity value. It shows that the BP neural network image segmentation method with a GA has the advantages of small calibration error, fast convergence speed, high efficiency, and high precision.

acs omega

Application of the Backpropagation Neural Network Image Segmentation Method with Genetic Algorithm Optimization in Micropores of Intersalt Shale Reservoirs