石油勘探与开发2012，Vol.39Issue(2)：243-248,6.
复杂岩性及多相流体智能识别方法
Computational intelligent methods for predicting complex lithologies and multiphase fluids
摘要
Abstract
On the basis of the basic principles of optimization algorithms and classification algorithms, the Self-Organizing feature Map neural network (SOM) is applied to establish the predictive model of lithology for the K-Means optimized data set including core data, logging data and well tests data. Additionally, the decision tree and support vector machine are used to build the predictive model of fluid on the basis of the lithology identification. The optimization algorithms, including genetic, grid and quadratic, are adopted to optimize the important parameters of C-SVC and o-SVC, such as C, v and y, so as to accurately identify the complex lithologies and multiphase fluids of complicated reservoirs. The SOM model and the decision tree and support vector machine are utilized to process four new wells in the complicated Carboniferous reservoirs of the Wucaiwan Sag, eastern Junggar Basin. The accuracy of lithology identification is 91.30%, and the accuracy of fluid identification is 95.65%. The lithologic complexity is not the main factor leading to the differences of fluids in the reservoirs. Because the complexity and nonlinearity of data set are not strong enough, the accuracy of the decision tree model is better than that of the support vector machine. Their accuracy rates are 94.31% and 86.97%, respectively. The performance of linear polynomial function is better than that of the radial basis function RBF and the neural function Sigmoid. The classification performance and generalization ability of C-SVC are stronger than that of the o-SVC.关键词
岩性识别/流体识别/储集层评价/计算智能/预测模型Key words
lithology identification/ fluid identification/ reservoir evaluation/ computational intelligence/ predictive model分类
能源科技引用本文复制引用
李雄炎,周金昱,李洪奇,张少华,陈亦寒..复杂岩性及多相流体智能识别方法[J].石油勘探与开发,2012,39(2):243-248,6.基金项目
国家高技术研究发展计划(863)项目(2009AA062802) （863）
