DocumentCode
2026080
Title
Neural-net-based modeling used in the ASP complicated flooding systems
Author
Chen, Guangyi ; Liu, Leiming ; Li, Yiqiang ; Lei, Fei
Author_Institution
Foshan Univ., Guangdong, China
Volume
1
fYear
2002
fDate
2002
Firstpage
772
Abstract
Constructs models of the nonlinear functional relationships between the petrophysical properties of rocks and their electrical properties and of the ASP complicated flooding systems based on neural networks. The learning algorithm is a kind of variable metric method, and it has fast convergence rate and good precision. The research result shows that the method is suitable for the modeling and identification of nonlinear systems.
Keywords
geology; hydrology; identification; learning (artificial intelligence); neural nets; nonlinear systems; rocks; ASP complicated flooding systems; convergence rate; electrical properties; identification; learning algorithm; neural-net-based modeling; nonlinear functional relationships; nonlinear systems; petrophysical properties; rocks; variable metric method; Application specific processors; Automation; Convergence; Floods; Intelligent control; Neural networks; Nonlinear systems; Petroleum;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN
0-7803-7268-9
Type
conf
DOI
10.1109/WCICA.2002.1022220
Filename
1022220
Link To Document