L-M neural network for data mining of oil saturation

Basing on the geological data base, the oil saturation of the reservoir is evaluated by data mining and knowledge discovering with artificial neural network. To get better convergence, faster convergent speed and higher precision of the neural network, Levenberg-Marquardt algorithm is adopted to imp...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 1117 - 1120
Main Author He Xiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Artificial neural network
Artificial neural networks
Convergence
Data mining
Geology
Hydrocarbon reservoirs
Knowledge discovery
Levenberg- Marquardt algorithm
Machine learning
Machine learning algorithms
Neural networks
Neurons
Oil saturation
Petroleum
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Summary:Basing on the geological data base, the oil saturation of the reservoir is evaluated by data mining and knowledge discovering with artificial neural network. To get better convergence, faster convergent speed and higher precision of the neural network, Levenberg-Marquardt algorithm is adopted to improve the learning algorithm of the neural network, which leads to global convergence with faster convergent speed than BP algorithm. The principle and the procedures of oil saturation data mining as well as knowledge discovering with L-M neural network are then discussed. An engineering case is also presented to explain and testify the method proposed.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212374