Geological model automatic reconstruction based on conditioning Wasserstein generative adversarial network with gradient penalty

For finely representation of complex reservoir units, higher computing overburden and lower spatial resolution are limited to traditional stochastic simulation. Therefore, based on Generative Adversarial Networks (GANs), spatial distribution patterns of regional variables can be reproduced through high-order statistical fitting. However, parameters of GANs cannot be optimized under insufficient training samples. Also, a higher computing consumption and overfitting issue easily occurred by stacking Convolutional Neural Networks (CNNs). Therefore, a hybrid framework combined with octave convolution and multi-stage GAN (OctSinGAN) is proposed to perform reservoir simulation. Specifically, a pyramid structure is introduced to perform multiscale representation based on single Training Image (TI), with feature information being captured under different scales. Then, the octave convolution is used to perform multi-frequency feature representation on different feature maps. Finally, a joint loss function is defined to optimize network parameters to improve simulation qualities. Three different kinds of TIs are used to verify the simulation performance of OctSinGAN. Results show that different simulations are similar to the corresponding TIs in terms of spatial variability, channel connectivity and spatial structures, with a relatively high simulation performance overall.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Geological model automatic reconstruction based on conditioning Wasserstein generative adversarial network with gradient penalty

Cited by 3 publications

References 44 publications

Cross-sections compilation-adjustment method based on 2D-3D linkage in regional three dimensional geological modeling

Cross-sections compilation-adjustment method based on 2D-3D linkage in regional three dimensional geological modeling

Automatic reconstruction of geological reservoir models based on conditioning data constraints and BicycleGAN

Reservoir Stochastic Simulation Based on Octave Convolution and Multistage Generative Adversarial Network

Contact Info

Product

Resources

About