DocumentCode
495091
Title
Data Mining of Oil Productive Index with Artificial Neural Network
Author
He, X. ; Liu, J.J.
Author_Institution
Civil Eng. Dept., Wuhan Polytech. Univ., Wuhan, China
Volume
2
fYear
2009
fDate
21-22 May 2009
Firstpage
265
Lastpage
268
Abstract
Levenberg-Marquardt (shorted as L-M) algorithm is improved and adopted to train the neural network. The improved training algorithm leads to better convergence, faster convergent speed and higher precision. The proposed L-M neural network is used for geological data mining of oil productive index basing on the geological database. The process of geological spatial data mining and the geological knowledge discovering with L-M neural network are discussed. As an engineering case, data mining and knowledge discovering of the oil productive index basing on the reservoir property stored in the geological database are presented to explain the method proposed.
Keywords
data mining; geology; geophysics computing; hydrocarbon reservoirs; learning (artificial intelligence); neural nets; petroleum industry; L-M neural network; Levenberg-Marquardt algorithm; artificial neural network; geological data mining; geological database; geological knowledge discovering; geological spatial data mining; oil productive index; reservoir property; training algorithm; Artificial neural networks; Convergence; Data engineering; Data mining; Geology; Indexes; Knowledge engineering; Neural networks; Petroleum; Spatial databases; Levenberg-Marquardt algorithm; artificial neural network; data mining; knowledge discover; oil productive index;
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Computing Science, 2009. ICIC '09. Second International Conference on
Conference_Location
Manchester
Print_ISBN
978-0-7695-3634-7
Type
conf
DOI
10.1109/ICIC.2009.178
Filename
5169062
Link To Document