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
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;
Conference_Titel :
Information and Computing Science, 2009. ICIC '09. Second International Conference on
Conference_Location :
Manchester
Print_ISBN :
978-0-7695-3634-7
DOI :
10.1109/ICIC.2009.178