Aiming at the problem that the characteristics of micro-resistivity imaging logging tools lead to the regular blank zone of the measured wellbore image, this paper proposes a filling model based on unsupervised learning framework, which integrates multi-scale and multi-level features, and a full-well section filling framework to fill the blank zone. The filling model adopts the UNet architecture, and uses the statistical prior of the non-blank zone resistivity data itself to perform unsupervised training filling based on MAE loss. The model is improved mainly through the following two measures : (1) The multi-scale residual convolution is introduced into the encoder to improve the multi-scale representation ability of the single-layer network; (2) The multi-layer feature fusion module and information guidance module are introduced in the encoding and decoding feature connection link to enrich the feature scale of upsampling and reduce the information loss in the decoding process. The experimental results show that compared with UNet, the visual effect and objective indicators of the model proposed in this paper are significantly improved on the natural scene dataset. The average pixel error is reduced by 19.03 %, SSIM is increased by 2.9 %, and PSNR is increased by 4.66 %. The whole well section filling framework applies the filling model to train the filling blank zone resistivity data in sections and then merge them to realize the end-to-end filling of the micro-resistivity imaging logging blank zone of a single well. The filling results have certain robustness and fit the actual production scene.