Inpainting borehole images using Generative Adversarial Networks

Belmeskine, Rachid; Benaichouche, Abed

doi:10.48550/arxiv.2301.06152

Cited by 1 publication

(1 citation statement)

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“…After an extensive literature review, it was found that generative adversarial networks (GANs) [28] have found extensive applications in the domain of image generation. Thanks to their ability to simulate complex functional relationships and their powerful generation capabilities, several papers have used GANs to process image data and have achieved good results [29][30][31][32]. Residual Network (ResNet) [33] is another highly influential network following the three classic CNNs: AlexNet, VGG, and GoogleNet.…”

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confidence: 99%

Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network

Sun

Gong

et al. 2023

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The Fullbore Formation Micro Imager (FMI) represents a proficient method for examining subterranean oil and gas deposits. Despite its effectiveness, due to the inherent configuration of the borehole and the logging apparatus, the micro-resistivity imaging tool cannot achieve complete coverage. This limitation manifests as blank regions on the resulting micro-resistivity logging images, thus posing a challenge to obtaining a comprehensive analysis. In order to ensure the accuracy of subsequent interpretation, it is necessary to fill these blank strips. Traditional inpainting methods can only capture surface features of an image, and can only repair simple structures effectively. However, they often fail to produce satisfactory results when it comes to filling in complex images, such as carbonate formations. In order to address the aforementioned issues, we propose a multiscale generative adversarial network-based image inpainting method using U-Net. Firstly, in order to better fill the local texture details of complex well logging images, two discriminators (global and local) are introduced to ensure the global and local consistency of the image; the local discriminator can better focus on the texture features of the image to provide better texture details. Secondly, in response to the problem of feature loss caused by max pooling in U-Net during down-sampling, the convolution, with a stride of two, is used to reduce dimensionality while also enhancing the descriptive ability of the network. Dilated convolution is also used to replace ordinary convolution, and multiscale contextual information is captured by setting different dilation rates. Finally, we introduce residual blocks on the U-Net network in order to address the degradation problem caused by the increase in network depth, thus improving the quality of the filled logging images. The experiment demonstrates that, in contrast to the majority of existing filling algorithms, the proposed method attains superior outcomes when dealing with the images of intricate lithology.

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