Inpainting borehole images using Generative Adversarial Networks

• Published in: arXiv.org
• Main Authors: Belmeskine, Rachid, Abed Benaichouche
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 15.01.2023
• Subjects: Boreholes; Coders; Discriminators; Generative adversarial networks; Reservoirs

In this paper, we propose a GAN-based approach for gap filling in borehole images created by wireline microresistivity imaging tools. The proposed method utilizes a generator, global discriminator, and local discriminator to inpaint the missing regions of the image. The generator is based on an auto-encoder architecture with skip-connections, and the loss function used is the Wasserstein GAN loss. Our experiments on a dataset of borehole images demonstrate that the proposed model can effectively deal with large-scale missing pixels and generate realistic completion results. This approach can improve the quantitative evaluation of reservoirs and provide an essential basis for interpreting geological phenomena and reservoir parameters.