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
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;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1022220
