Inpainting of electrical imaging logging images based on fast Fourier convolution

Imaging logging is an important technique for complex reservoir evaluation. It provides a two-dimensional image of the wellbore, showing the wellbore’s structural features and playing an important role in evaluating seam holes and sedimentary structures. However, the lack of a resistivity imaging logging device causes blank streaks to show up on the logging images, which makes computer data processing more challenging and affects visual continuity in manual identification. Currently, traditional image inpainting methods and neural networks do not perform well on filling the well logging images. Therefore, there is an urgent need to research a deep learning-based image inpainting method for blank streaks in imaging logging images. A dataset was constructed using electrical imaging logging images from the LG area of the Southwest Oil and Gas Field. This dataset was used to train a new deep learning algorithm for intelligent restoration of blank streaks based on fast Fourier convolution, based on a fast Fourier convolution neural network for filling blank streaks in imaging logging images. This technique solves the challenge of acquiring entire wellbore photos and makes it possible to quickly, accurately, and intelligently inpaint blank streaks in well logging images.