The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application

Jingjing Zheng; Yun Wang; Chunying Yang

doi:doi:10.11648/j.ogce.20190701.11

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The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application

Jingjing Zheng, Yun Wang, Chunying Yang

Published in International Journal of Oil, Gas and Coal Engineering (Volume 7, Issue 1)

Received: 8 November 2018 Accepted: 6 December 2018 Published: 21 January 2019

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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.

Published in	International Journal of Oil, Gas and Coal Engineering (Volume 7, Issue 1)
DOI	10.11648/j.ogce.20190701.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Kernel Principal Component Analysis, the Probability Analysis, Kernel Function, Attribute Optimization Analysis, Reservoir Prediction

References

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[8]	P. Dejtrakulwong, T. Mukerji, and G. Mavko. Using kernel principal component analysis to interpret seismic signatures of thin shaly-sand reservoirs, SEG Technical Program Expanded Abstracts 2012: pp. 1-5.
[9]	Z. N. Liu, C. Y. Song, H. P Cai, X. M Yao, and G. M. Hu. ”Enhanced coherence using principal component analysis.” Interpretation, 2017, vol.5, pp. T351-T359.
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[11]	P. Dejtrakulwong, T. Mukerji, and G. Mavko. Using kernel principal component analysis to interpret seismic signatures of thin shaly-sand reservoirs, SEG Technical Program Expanded Abstracts 2012: pp. 1-5.
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APA Style

Jingjing Zheng, Yun Wang, Chunying Yang. (2019). The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application. International Journal of Oil, Gas and Coal Engineering, 7(1), 1-6. https://doi.org/10.11648/j.ogce.20190701.11

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ACS Style

Jingjing Zheng; Yun Wang; Chunying Yang. The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application. Int. J. Oil Gas Coal Eng. 2019, 7(1), 1-6. doi: 10.11648/j.ogce.20190701.11

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AMA Style

Jingjing Zheng, Yun Wang, Chunying Yang. The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application. Int J Oil Gas Coal Eng. 2019;7(1):1-6. doi: 10.11648/j.ogce.20190701.11

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@article{10.11648/j.ogce.20190701.11,
  author = {Jingjing Zheng and Yun Wang and Chunying Yang},
  title = {The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {7},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ogce.20190701.11},
  url = {https://doi.org/10.11648/j.ogce.20190701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20190701.11},
  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.},
 year = {2019}
}

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TY  - JOUR
T1  - The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
AU  - Jingjing Zheng
AU  - Yun Wang
AU  - Chunying Yang
Y1  - 2019/01/21
PY  - 2019
N1  - https://doi.org/10.11648/j.ogce.20190701.11
DO  - 10.11648/j.ogce.20190701.11
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20190701.11
AB  - 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.
VL  - 7
IS  - 1
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Jingjing Zheng, Yun Wang, Chunying Yang. (2019). The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application. International Journal of Oil, Gas and Coal Engineering, 7(1), 1-6. https://doi.org/10.11648/j.ogce.20190701.11

Copy | Download

ACS Style

Jingjing Zheng; Yun Wang; Chunying Yang. The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application. Int. J. Oil Gas Coal Eng. 2019, 7(1), 1-6. doi: 10.11648/j.ogce.20190701.11

Copy | Download

AMA Style

Jingjing Zheng, Yun Wang, Chunying Yang. The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application. Int J Oil Gas Coal Eng. 2019;7(1):1-6. doi: 10.11648/j.ogce.20190701.11

Copy | Download

@article{10.11648/j.ogce.20190701.11,
  author = {Jingjing Zheng and Yun Wang and Chunying Yang},
  title = {The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {7},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ogce.20190701.11},
  url = {https://doi.org/10.11648/j.ogce.20190701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20190701.11},
  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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
AU  - Jingjing Zheng
AU  - Yun Wang
AU  - Chunying Yang
Y1  - 2019/01/21
PY  - 2019
N1  - https://doi.org/10.11648/j.ogce.20190701.11
DO  - 10.11648/j.ogce.20190701.11
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20190701.11
AB  - 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.
VL  - 7
IS  - 1
ER  -

Copy | Download