Digital Rock Reconstruction Using Wasserstein GANs with Gradient Penalty

Abstract

Due to the scarcity and vulnerability of physical rock samples, digital rock reconstruction plays an important role in the numerical study of reservoir rock properties and fluid flow behaviors. With the rapid development of deep learning technologies, generative adversarial networks (GANs) have become a promising alternative to reconstruct complex pore structures. Numerous GAN models have been applied in this field, but many of them suffer the unstable training issue. In this study, we apply the Wasserstin GAN with gradient penalty, also known as the WGAN-GP network, to reconstruct Berea sandstone and Ketton limestone. Unlike many other GANs using the Jesnen-Shannon divergence, the WGAN-GP network exhibits a stable training performance by using the Wasserstin distance to measure the difference between generated and real data distributions. Moreover, the generated image quality correlates with the discriminator loss. This provides us an indicator of the training state instead of frequently subjective assessments in the training of deep convolutional GAN (DCGAN) based models. An integrated framework is presented to automate the entire workflow, including training set generation, network setup, model training and synthetic rock validation. Numerical results show that the WGAN-GP network accurately reconstructs both Berea sandstone and Ketton limestone in terms of two-point correlation and morphological properties.

A Hybrid GAN-Based DL Approach for the Automatic Detection of Shockable Rhythms in AED for Solving Imbalanced Data Problems

“…We proposed a hybrid GAN-based DL architecture, external classifier-Wasserstein conditional generative adversarial network (EC-WCGAN), for detecting shockable rhythms in an AED on an unbalanced ECG dataset. Our proposed model used the EC-GAN model proposed by Haque [21] by replacing DC-GAN used by the author with the Wasserstein conditional GAN with a gradient penalty (WCGAN-GP), proposed by W. Manhar et al [22]. The EC-WCGAN comprises a deep neural network (DNN) as an external classifier (EC) to classify heart rhythms into shockable or non-shockable rhythms and the WCGAN with a gradient penalty as a synthetic tabular data generator for the low-class to overcome the class imbalance problem.…”

“…Walia et al [22] proposed the WCGAN-GP model for generating synthetic tabular data. The WCGAN-GP model is an extension of WGAN-GP by inputting the conditional vector or target labels.…”

Overview of Machine Learning and Deep Learning Approaches for Detecting Shockable Rhythms in AED in the Absence or Presence of CPR

“…Recently, generative adversarial networks (GAN) have been very successful in image generation [56] and speech emotion recognition [57]. In addition, it can be used to generate synthetic tabular data instead of images [58,59]. The GAN-generated samples are more realistic and superior to oversampling techniques for generating synthetic data [56].…”

“…In addition, this approach can capture the true data distribution to generate new samples for the minority class, addressing the class imbalance problem [60]. In the case of standard data augmentation, they generate unrealistic or overgeneralized samples [58]. Even though they addressed the imbalanced class problem by generating a minority class, the performance matrices using these synthetic data might be less than GAN.…”

Locating CO2 Leakage in Subsurface Traps Using Bayesian Inversion and Deep Learning

“…The long short-term memory (LSTM) network is designed to capture the time-series information, and it has been successfully used in the recovery factor prediction (He et al, 2022b;Santoso et al, 2021a; and the CO2 leakage rate prediction (He et al, 2021c;Zhang et al, 2022b). The generative adversarial network (GAN) showed great potential in boosting digital rock resolution (Li et al, 2022;Zhang et al, 2022a). In this work, we use LSTM to predict the pressure response under different CO2 leakage locations.…”