Image identification is a major means to achieve quantitative characterization of the microscopic oil displacement process. Traditional digital image processing techniques usually uses a series of pixel-based algorithms, which is difficult to achieve real-time processing of large-scale images. Deep learning methods have the characteristics of fast speed and high accuracy. This paper proposes a four-channel image segmentation method based on RGB color and rock particle mask. First, the micro model rock particle mask is divided together with the RGB component to form four-channel input data through image processing technology. Pixel-level training set labels are then created through traditional image processing techniques. Through the U-Net semantic segmentation network, the pixel-level oil and water identification and recovery factor calculation of the polymer microscopic oil displacement process were carried out. Combined with the pore distance transformation algorithm, the lower limit of pore utilization for different displacement media was clarified. The results show that U-Net can achieve accurate division of oil and water areas. Compared with conventional three-channel images, the improved four-channel image proposed in this paper has significantly improved the segmentation accuracy due to the addition of the constraints of the rock particle mask, and the global accuracy can be Up to 99%. Combining some post-processing methods, this paper found that polymer flooding increased the mobilization degree of small pores on the basis of water flooding and lowered the lower limit of pore mobilization from 25 μm to 16 μm. In microscopic experiments, the recovery factor was increased by 24.01%, finally achieving rapid and accurate quantitative characterization of the microscopic oil displacement process. The four-channel image method based on the U-Net semantic segmentation network and the improved rock particle mask proposed in this article has strong adaptability to the identification of flow channels in the microscopic oil displacement process. Quantitative characterization of the lower limit of pore movement and recovery degree during microscopic oil displacement provides a new method for microscopic image processing.

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