A synthetic computational-intelligence-based method and its application in identifying water-flooded zones in oil field

Author

Shi, X.H. ; Wang, S.M. ; Sui, X.G. ; Gao, Y.C. ; Lee, H.P. ; Liang, Y.C.

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China

Volume

3

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

1741

Abstract

Inspired by the natural features of the variable size of the population, an improved genetic algorithm with variable population-size (VPGA) is presented in this paper. Based on the VPGA, a novel synthetic computational-intelligence-based method (SCIBM) is proposed and applied to the identification of water-flooded zones in the oil field. This method integrates evolutionary neural networks and recognition technique for multi-point-data neural networks. Simulation results applying the SCIBM to actual logging data in Daqing Oil Field show that the proposed method works well in the identification of water-flooded zones.

Keywords

disasters; floods; genetic algorithms; learning (artificial intelligence); neural nets; well logging; Daqing Oil Field; evolutionary neural networks; genetic algorithm; logging data; multipoint-data neural networks; population; recognition technique; synthetic computational-intelligence-based method; variable population-size; water-flooded zones; Application software; Biological cells; Biological system modeling; Computational modeling; Computer networks; Educational institutions; Evolution (biology); Genetic algorithms; Neural networks; Petroleum;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1259778

Filename

1259778