Petroleum Reservoir Evaluation and Development
|
2026, 16(4): 926-933
• Digital and Intelligent Energy •
Seepage simulation of CO2 immiscible flooding in low-permeability reservoirs based on physics-informed neural networks
Full
QIONGLIN SHI1
, 2
, JUNJIAN LI1, LYU XIAOCONG1, HUIQING LIU1, XIAOLIANG YANG2
Affiliations
- 1 College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
- 2 Onshore Operation Area, PetroChina Jidong Oilfield Company, Tangshan, Hebei 063200, China
Published: 2026-07-26
doi: 10.13809/j.cnki.cn32-1825/te.2025057
Outline
physics-informed neural networks
/
low-permeability reservoirs
/
CO2 immiscible flooding
/
threshold pressure gradient
/
CO2 dissolution
/
Buckley-Leverett equation
石琼林, 李俊键, 吕晓聪, 刘慧卿, 杨小亮.
基于物理信息神经网络的低渗透油藏CO2非混相驱渗流模拟.
油气藏评价与开发,
2026
, 16
(4)
: 926
-933
.
DOI: 10.13809/j.cnki.cn32-1825/te.2025057
QIONGLIN SHI, JUNJIAN LI, LYU XIAOCONG, HUIQING LIU, XIAOLIANG YANG.
Seepage simulation of CO2 immiscible flooding in low-permeability reservoirs based on physics-informed neural networks[J].
Petroleum Reservoir Evaluation and Development,
2026
, 16
(4)
: 926
-933
.
DOI: 10.13809/j.cnki.cn32-1825/te.2025057
Year 2026 volume 16 Issue 4
14
5
Cite this Article
BibTeX
Article Info
doi: 10.13809/j.cnki.cn32-1825/te.2025057
- Receive Date:2026-01-12
- Online Date:2026-10-30
- Published:2026-07-26