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基于人工免疫系统的沉积微相自动识别
引用本文:李国和,赵决正,江希.基于人工免疫系统的沉积微相自动识别[J].计算机工程与应用,2008,44(11):220-222.
作者姓名:李国和  赵决正  江希
作者单位:中国石油大学(北京) 计算机科学与技术系,北京 102249
基金项目:国家自然科学基金(the National Natural Science Foundation of China under Grant No.60473125),中国石油(CNPC)石油科技中青年创新基金资助项目(No.05E7013)
摘    要:为了采用测井曲线实现沉积微相的自动识别,通过测井曲线变化趋势的编码和人工免疫系统的克隆免疫、变异等算子,建立基于人工免疫系统的测井曲线识别模型,实现了不等长特征曲线匹配过程的快速收敛。对胜利油田150个沉积微相进行识别,正确率达到95%,证实了该模型应用的有效性。

关 键 词:人工免疫算法  模式识别  时序数据  测井曲线  沉积微相  
文章编号:1002-8331(2008)11-0220-03
收稿时间:2007-7-31
修稿时间:2007年7月31日

Recognition of sedimentary microfacies based on Artificial Immune System
LI Guo-he,ZHAO Jue-zheng,JIANG Xi.Recognition of sedimentary microfacies based on Artificial Immune System[J].Computer Engineering and Applications,2008,44(11):220-222.
Authors:LI Guo-he  ZHAO Jue-zheng  JIANG Xi
Affiliation:Department of Computer Science and Technology,China University of Petroleum,Beijing 102249,China
Abstract:In order to recognize sedimentary microfacies automatically by well-logging curves,by means of coding the tendency of well-logging curves and implementing the operators such as clone immunity and aberrance,the clustering well-logging curves is presented by variant feature vectors,and then recognition model of sedimentary macrofacies with the well-logging curves is constructed by the basis on Artificial Immune System(AIS).The recognition model is applied to recognizing 150 sedimentary microfacies of ShengLi oil field,and the accuracy of recognition is up to 95%,which proves the recognition model based on AIS is very efficient in recognition of sedimentary microfacies.
Keywords:artificial immune algorithm  pattern recognition  time-series data  well-logging curve  sedimentary microfacies
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