Attribute optimization based on the probability kernel principal component analysis
Zheng Jingjing1, Wang Yanguang1, Du Lei2, Yin Xingyao3, Zhang Guangzhi3
1. Geophysical Research Institute, Shengli Oilfield Branch Co., SINOPEC, Dongying, Shandong 257022, China; 2. Exploration and Development Research Institute, Liaohe Oilfield Company, CNPC, Panjin, Liaoning 124010, China; 3. China University of Petroleum (East China), Qingdao, Shandong 266555, China
Abstract：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.
郑静静, 王延光, 杜磊, 印兴耀, 张广智. 基于概率核主成分分析的属性优化方法及其应用[J]. 石油地球物理勘探, 2014, 49(3): 567-571.
Zheng Jingjing, Wang Yanguang, Du Lei, Yin Xingyao, Zhang Guangzhi. Attribute optimization based on the probability kernel principal component analysis. OGP, 2014, 49(3): 567-571.