%0 Journal Article
%T Characterization of petroleum reservoir based on self organizing maps<br>基于自组织映射网络的油藏表征模型*
%A CHENG Guo jian
%A LIU Shu ying
%A <br>程国建
%A 刘淑英
%J 计算机应用研究
%D 2007
%I 
%X SOM neural networks can implement feature preserving, dimension reduction and data visualization. This paper proposed the SOM neural network for the petroleum parameter characterization. Simulation on the well-log data of Zhenyuan- Jingchuan area, in Gansu, shows that this algorithm is practicable and effective for actual reservoir modeling.
%K petroleum reservoir characterization
%K self organization maps(SOM)
%K clustering analysis
%K data mining
%K lithology identification<br>油藏表征
%K 自组织映射
%K 聚类分析
%K 数据挖掘
%K 岩性识别
%K 自组织
%K 映射网络
%K 油藏表征
%K 表征模型
%K maps
%K based
%K reservoir
%K petroleum
%K 性能
%K 操作
%K 结果
%K 仿真实验
%K 测井数据
%K 地区
%K 泾川
%K 甘肃镇原
%K 技术模型
%K 问题
%K 结合
%K 处理
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=A375D94749B999E3&yid=A732AF04DDA03BB3&vid=B91E8C6D6FE990DB&iid=F3090AE9B60B7ED1&sid=E1D946F217E3B046&eid=D9AE183D3F5C3C75&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=7