Attribute optimization based on the probability kernel principal component analysis

作者：Zheng Jingjing^*; Wang Yanguang; Du Lei; Yin Xingyao; Zhang Guangzhi

来源：Oil Geophysical Prospecting, 2014, 49(3): 567-571.

• Expectation Maximization
• High dimensional spaces
• High-dimensional feature space
• Higher-order statistics
• Kernel function
• Kernel principal component analyses (KPCA)
• Optimization analysis
• Reservoir prediction

The principal component analysis (PCA) is the most common attribute optimization analysis techniques, but with lack of probability model and the absence of higher-order statistics information. In order to overcome its shortcomings, this paper proposes the probability kernel principal component analysis (PKPCA) based on Bayesian theory and kernel principal component analysis (KPCA). First, we map the sample data to the high dimensional feature space, then define probability model of the data in high-dimensional space, and finally, use expectation maximization (EM) estimated to get the best results. This method has both the advantage of the probability analysis and the kernel principal component analysis (KPCA), and can realize the non-linear probability analysis in more complex reservoir conditions. The probability of kernel principal component analysis (PKPCA) is applied to reservoir prediction of an oilfield. The predicted results show that the method not only improves the precision of attribute optimization, but also the accuracy of reservoir prediction.

• 出版日期2014
• 单位中国石油大学（华东）