Inpainting borehole images using Generative Adversarial Networks

Rachid Belmeskine; Abed Benaichouche

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arXiv · 2301.06152

Inpainting borehole images using Generative Adversarial Networks

Rachid Belmeskine·Abed Benaichouche

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Abstract

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.

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Rachid Belmeskine, Abed Benaichouche. 2023-01-15. Inpainting borehole images using Generative Adversarial Networks. https://arxiv.org/abs/2301.06152

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