Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty

Abstract

Generative Adversarial Networks (GANs), as most popular artificial intelligence models in the current image generation field, have excellent image generation capabilities. Based on Wasserstein GANs with gradient penalty, this paper proposes a novel digital core reconstruction method. First, a convolutional neural network is used as a generative network to learn the distribution of real shale samples, and then a convolutional neural network is constructed as a discriminative network to distinguish reconstructed shale samples from real ones. Through this confrontation training method, realistic digital core samples of shale can be reconstructed. The paper uses two-point covariance function, Fréchet Inception Distance and Kernel Inception Distance, to evaluate the quality of digital core samples of shale reconstructed by GANs. The results show that the covariance function can test the similarity between generated and real shale samples, and that GANs can efficiently reconstruct digital core samples of shale with high-quality. Compared with multiple point statistics, the new method does not require prior inference of the probability distribution of the training data, and directly uses noise vector to generate digital core samples of shale without using constraints of "hard data" in advance. It is easy to produce an unlimited number of new samples. Furthermore, the training time is also shorter, only 4 hours in this paper. Therefore, the new method has some good points compared with current methods.

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Digital Rock Reconstruction with User-Defined Properties Using Conditional Generative Adversarial Networks

“…On top of GANs, Shams et al (2020) integrated it with auto-encoder networks to produce sandstone samples with multiscale pores, enabling GANs to predict inter-grain pores while auto-encoder networks provide GANs with intragrain pores. Some other representative applications also exist, such as adopting GANs to reconstruct shale digital cores (Zha et al 2020), utilizing GANs to augment resolution and recover the texture of micro-CT images of rocks (Wang et al 2019(Wang et al , 2020, and reconstructing three-dimension structures from two-dimension slices with GANs (Feng et al 2020;Kench and Cooper 2021;You et al 2021).…”

Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks

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“…Previous studies have shown that high-quality digital core models can be obtained by using network models that generative adversarial neural networks and its variants. 3 Geofluids However, the training of GANs is a complicated process, and it is easy to encounter problems such as gradient disappearance or mode collapse during the training process [38]; it usually takes thousands or even tens of thousands of training times to achieve a better generation effect. Therefore, it is of great significance to find a way to effectively reduce the number of trainings of GANs.…”