Inpainting borehole images using Generative Adversarial Networks

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Belmeskine, Rachid, Abed Benaichouche
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.01.2023
Subjects
Boreholes
Coders
Discriminators
Generative adversarial networks
Reservoirs
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Summary: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.
ISSN:2331-8422