Combined Deep-Fill and Histogram Equalization Algorithm for Full-Borehole Electrical Logging Image Restoration

Abstract

Electrical borehole imaging tools cannot achieve full-borehole images due to their structure limitation. Gaps always occur between pads, and it is necessary to fill in the gaps for subsequent interpretation. In this paper, an improved model for borehole image restoration and enhancement is established by combining a “Deep-Fill” image repair algorithm based on generative adversarial networks (GANs) with histogram equalization principles. Firstly, resistivity data is converted into images, and the anomalous areas are manually repaired. Then, the manually repaired images undergo iterative training using the “Deep-Fill” model. Finally, the repaired images are further enhanced through histogram equalization principles. Results show the overall restoration quality of the model surpasses that of the original GAN-based restoration model, particularly in terms of texture coherence at junctions. This approach not only enhances the quality of repaired images but also improves the interpretability of geological features of the electrical imaging logs.