Author
Listed:
- Xianlin Ma
(College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Engineering Research Center of Development and Management for Low to Extra-Low Permeability Oil & Gas Reservoirs in Western China, Ministry of Education, Xi’an Shiyou University, Xi’an 710065, China)
- Chengde Li
(College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China)
- Jie Zhan
(College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Engineering Research Center of Development and Management for Low to Extra-Low Permeability Oil & Gas Reservoirs in Western China, Ministry of Education, Xi’an Shiyou University, Xi’an 710065, China)
- Yupeng Zhuang
(College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China)
Abstract
Efficient and economical hydrocarbon extraction relies on a clear understanding of fluid flow dynamics in subsurface reservoirs, where multiphase flow in porous media poses complex modeling challenges. Traditional numerical methods for solving the governing partial differential equations (PDEs) provide effective solutions but struggle with the high computational demands required for accurately capturing fine-scale flow dynamics. In response, this study introduces a physics-informed generative adversarial network (GAN) framework for addressing the Buckley–Leverett (B-L) equation with non-convex flux functions. The proposed framework consists of two novel configurations: a Physics-Informed Generator GAN (PIG-GAN) and Dual-Informed GAN (DI-GAN), both of which are rigorously tested in forward and inverse problem settings for the B-L equation. We assess model performance under noisy data conditions to evaluate robustness. Our results demonstrate that these GAN-based models effectively capture the B-L shock front, enhancing predictive accuracy while embedding fluid flow equations to ensure model interpretability. This approach offers a significant advancement in modeling complex subsurface environments, providing an efficient alternative to traditional methods in fluid dynamics applications.
Suggested Citation
Xianlin Ma & Chengde Li & Jie Zhan & Yupeng Zhuang, 2024.
"Physics-Informed Generative Adversarial Network Solution to Buckley–Leverett Equation,"
Mathematics, MDPI, vol. 12(23), pages 1-14, December.
Handle:
RePEc:gam:jmathe:v:12:y:2024:i:23:p:3833-:d:1536588
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3833-:d:1536588. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.