DocumentCode :
3290769
Title :
Modeling of Oil and Water Migration Based on Chaos Genetic Algorithm Neural Network
Author :
Tian, Jingwen ; Zhou, Shiru ; Gao, Meijuan
Author_Institution :
Coll. of Autom., Beijing Union Univ., Beijing, China
fYear :
2009
fDate :
16-17 May 2009
Firstpage :
763
Lastpage :
766
Abstract :
An actual physical simulation model was constructed to simulate the course of oil and water migration. Under certain physical property conditions, we simulated the water injection well and the oil well on the physical simulation model, and continuous measured online the oil and water content of different area of model in three-dimensional space using the 512 routes resistivity measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is a complicated and nonlinear and the chaos genetic algorithm neural network has the ability of strong nonlinear function approach and global optimization, in this paper, the chaos genetic algorithm neural network was used to establish the oil and water migration model. We construct the structure of chaos genetic algorithm neural network. The experimental results show that this method is feasible and effective.
Keywords :
chaos; crude oil; genetic algorithms; neural nets; production engineering computing; chaos genetic algorithm neural network; crude oil; nonlinear function approach; oil field; oil migration; oil saturation; oil well; water injection well; water migration; Area measurement; Automation; Chaos; Circuit simulation; Educational institutions; Genetic algorithms; Neural networks; Petroleum; Production; Water; chaos genetic algorithm; modeling; neural network; oil and water migration; water displacing oil;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits, Communications and Systems, 2009. PACCS '09. Pacific-Asia Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3614-9
Type :
conf
DOI :
10.1109/PACCS.2009.196
Filename :
5232437
Link To Document :
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