地震属性在天然气水合物识别中的应用  

Seismic attribute recognition in the application of natural gas hydrate

作　　者：吕琳[1,2,3] 王明君[2] 范继璋[1] 

机构地区：[1]吉林大学,长春130026 [2]中国地质科学院,北京100037 [3]中央司法警官学院,保定071000

出　　处：《地球物理学进展》2011年第2期596-601,共6页Progress in Geophysics

基　　金：国家863高技术研究发展计划子项目"天然气水合物勘探开发关键技术"(2006A.A09202-03-02)子课题"天然气水合物矿体的高精度地震定量评价软件开发"资助

摘　　要：地震属性包含了大量的地质特征,对天然气水合物矿体识别起着重要作用.在对天然气水合物进行识别时,如何选取适用于工区的地震属性和分类方法是解决问题的关键.本文对神狐地区地震数据进行了优化处理,提取了15种地震属性.通过自组织神经网络分析技术对地震属性数据所反映的地质特征进行自动识别和分类,与实际测井数据进行反复对比,获得了工区水合物的识别图和雕刻图.研究结果与已知的钻井区域进行反复对比和综合分析,发现自组织神经网络在水合物识别研究中起了良好的作用.Seismic attributes contains a large number of geological features. These attributes are important to the identification of natural gas hydrate. During Identification of gas hydrate, how to select a suitable attribute for an work area and classification of seismic attributes is the key for solving the problem. This article optimized seismic data of ShenHu area, extracted 15 types of seismic attributes. Through the techniques of self-organizing neural network analysis, automatic identification and classification of the geological features which seismic attributes data reflects, comparing the actual logging data and obtained identification chart of gas hydrate and carving hydrate map. Through the comparison and comprehensive analysis between the analysis results and the actual results of the drilling area, we found that self-organizing neural network is a suitable way for the hydrate recognition.

关 键 词：天然气水合物 地震属性 自组织神经网络 

分 类 号：P631[天文地球—地质矿产勘探]