Application of kernel fuzzy C-means method to reservoir prediction
Guangzhi Zhang · Zhongguo Shiyou Daxue xuebao. Ziran kexue ban · 2012
The kernel fuzzy C-means(FCM) method is a novel method for pattern recognition.The problems such as non-hyperspherical data and non-linear inter-class boundary are prevalent during seismic attributes clustering process,which could not be resolved effectively by traditional FCM method.The kernel function was introduced into traditional FCM method for these problems in reservoir prediction.The parameters including feature weights and fuzzy coefficient were optimized for different sensibility of seismic attributes,which could improve the effectiveness of this new kernel FCM method for reservoir prediction.The results of experiments on the artificial and real data show that the new kernel FCM method can describe the boundaries of gas-bearing carbonate reservoir more accurately.