Self-adaptively inpainting gaps between electrical image pads according to the image fabric

Images created from measurements made by wireline microresistivity imaging tools have longitudinal gaps when the well circumference exceeds the total width of the pad-mounted electrode arrays. The gap size depends on the tool design and borehole size, and the null data in these gaps negatively affect the quantitative evaluation of reservoirs. Images with linear and texture features obtained from microresistivity image logs have distinct dual fabric features because of logging principles and various geological phenomena. Linear image features usually include phenomena such as fractures, bedding, and unconformities. Contrarily, texture-based image features usually indicate phenomena such as vugs and rock matrices. According to the characteristics of this fabric-based binary image structure and guided by the practice of geological interpretation, an adaptive inpainting method for the blank gaps in microresistivity image logs is proposed. For images with linear features, a sinusoidal tracking inpainting algorithm based on an evaluation of the validity and continuity of pixel sets is used. Contrarily, the most similar target transplantation algorithm is applied to texture-based images. The results obtained for measured electrical imaging data showed that the full borehole image obtained by the proposed method, whether it was a linear structural image reflecting fracture and bedding or texture-based image reflecting the matrix and pore of rock, had substantially good inpainting quality with enhanced visual connectivity. The proposed method was effective for inpainting electrical image logs with large gaps and high angle fractures with high heterogeneity. Moreover, ladder and block artifacts were rare, and the inpainting marks were not obvious. In addition, detailed full borehole images obtained by the proposed method will provide an essential basis for interpreting geological phenomena and reservoir parameters.