Volume 7, Issue 1, January 2019, Page: 1-6
The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
Jingjing Zheng, China University of Geosciences, School of Geophysics and Information Technology, Beijing, China
Yun Wang, China University of Geosciences, School of Geophysics and Information Technology, Beijing, China
Chunying Yang, China University of Geosciences, School of Geophysics and Information Technology, Beijing, China
Received: Nov. 8, 2018;       Accepted: Dec. 6, 2018;       Published: Jan. 21, 2019
DOI: 10.11648/j.ogce.20190701.11      View  169      Downloads  26
Abstract
The principal component analysis (PCA) is the most common attribute optimization analysis techniques, but it is a linear method and exists the problem of lack of probability model and the absence of higher-order statistics information. It has poor comprehensive ability to complex non-linear attributes. Therefore, in order to overcome two shortcomings of the principal component analysis (PCA) and improve the effect of attribute optimization, this paper studies the probability kernel principal component analysis (PKPCA) method which is based on Bayesian theory and kernel principal component analysis (KPCA). First, the sample data are mapped to the high dimensional feature space, then define probability model of the data in high-dimensional space, and finally, expectation maximization (EM) estimated is used to get the best results. This method has both the advantage of probability analysis and kernel principal component analysis (KPCA). It is able to effectively adapt to more complex reservoir conditions and can realize the non-linear probability analysis. The probability kernel principal component analysis (PKPCA) method is applied to reservoir prediction of the Southern oil fields in China. The predicted results show that the method can improve the precision of the attribute optimization, while improving the accuracy of the forecasts of reservoir.
Keywords
Kernel Principal Component Analysis, the Probability Analysis, Kernel Function, Attribute Optimization Analysis, Reservoir Prediction
To cite this article
Jingjing Zheng, Yun Wang, Chunying Yang, The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application, International Journal of Oil, Gas and Coal Engineering. Vol. 7, No. 1, 2019, pp. 1-6. doi: 10.11648/j.ogce.20190701.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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