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利用Kohonen神经网络划分二维地震相——以柴达木盆地E区风险勘探为例
引用本文:刘小洪,冯明友,杨午阳,孙辉,魏新建,刘哲.利用Kohonen神经网络划分二维地震相——以柴达木盆地E区风险勘探为例[J].岩性油气藏,2011,23(4):115-118,132.
作者姓名:刘小洪  冯明友  杨午阳  孙辉  魏新建  刘哲
作者单位:1. 西南石油大学资源与环境学院,610500
2. 中国石油勘探开发研究院西北分院
3. 中国石油长庆油田分公司勘探部
摘    要:为研究柴达木盆地E区大型背斜构造沉积相及砂体展布规律,在二维地震数据闭合差校正、邻区井标定引入及精细层位解释基础上,利用改进算法的Kohonen神经网络技术开展二维地震相划分研究,识别出三角洲前缘水下分流河道、分流间湾及滩坝等微相。本文研究认为,研究区古流向为南东—北西向,储集砂体较发育,主要富集于研究区中部,现今构造...

关 键 词:Kohonen  神经网络  地震相  沉积相  柴达木盆地

Application of Kohonen neural network to 2D seismic facies division in E area of Qaidam Basin
LIU Xiao-hong,FENG Ming-you,YANG Wu-yang,SUN Hui,WEI Xin-jian,LIU Zhe.Application of Kohonen neural network to 2D seismic facies division in E area of Qaidam Basin[J].Northwest Oil & Gas Exploration,2011,23(4):115-118,132.
Authors:LIU Xiao-hong  FENG Ming-you  YANG Wu-yang  SUN Hui  WEI Xin-jian  LIU Zhe
Affiliation:LIU Xiao-hong1,FENG Ming-you1,YANG Wu-yang2,SUN Hui2,WEI Xin-jian2,LIU Zhe3(1.School of Resources and Environment,Southwest Petroleum University,Chengdu 610500,China,2.Research Institute of Petroleum Exploration & Development-Northwest,PetroChina,Lanzhou 730020,3.Department of Exploration,Changqing Oilfield Company,Xi'an 710018,China)
Abstract:The situation of E area is hard to proceed deep research and risk assessment by the absent of prospecting well data.Based on seismic mis-tie calibration,adjacent well calibration and fine horizon interpretation,Kohonen neural network method is applied to carry out two-dimensional seismic facies classification of target zone.Microfacies of delta front such as distributary channel,interdistributary bay and sand bar are recognized.The paleo-current direction is suspected from southeast to northwest.Reservoir sand bodies developed well in the middle of the study area,preliminary prospecting well is located in favorable sedimentary facies.Sedimentary facies division are supplied and refined by the result of seismic facies,which can supply significant foundation for geometric arrangement of risk wells and regional breakthrough.
Keywords:Kohonen  neural network  seismic facies  sedimentary facies  Qaidam Basin
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