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莺歌海盆地浅层天然气藏的地震识别技术研究
引用本文:李绪宣. 莺歌海盆地浅层天然气藏的地震识别技术研究[J]. 中国海上油气, 2000, 0(3)
作者姓名:李绪宣
作者单位:中国海洋石油南海西部公司!广东湛江524057
摘    要:岩石物理参数是地震反射特征分析的基础。对莺歌海盆地 11口探井纯泥岩、纯砂岩及含气砂岩 (气层 )的测井信息进行了统计分析和模型实验 ,并结合钻井、地震资料做了分析 ,其结果表明 ,本区埋深不超过 2 5 0 0 m的含气砂岩 ,其地震纵波速度比围岩 (泥岩 )低 30 %以上。这类含气砂岩的顶界面在地震剖面上表现为强振幅异常 (亮点 ) ;在 CDP道集上表现出振幅随偏移距增大而增强的第三类 AVO异常 ;在道积分剖面上显示为相对低速。在钻预探井前 ,利用亮点、AVO和道积分技术可有效识别这类含气砂岩。文中首次建立了以亮点、AVO和道积分技术为核心的天然气藏综合预测流程 ,并在莺歌海盆地浅层天然气勘探中推广应用 ,相继发现了乐东 2 0 - 1、乐东 2 2 - 1、乐东 8- 1等一批中、小型气田 ,取得了显著的社会效益和经济效益

关 键 词:莺歌海盆地  浅层天然气藏  识别  亮点  AVO  道积分  综合预测

SEISMIC RECOGNITION TECHNIQUES OF SHALLOW GAS RESERVOIRS IN YINGGEHAI BASIN
Li Xuxuan. SEISMIC RECOGNITION TECHNIQUES OF SHALLOW GAS RESERVOIRS IN YINGGEHAI BASIN[J]. China Offshore Oil and Gas, 2000, 0(3)
Authors:Li Xuxuan
Abstract:Petrophysical parameters are the basis of seismic reflection character analysis.In Yinggehai Basin,logging information picked from clean mudstones,sandstones and gas reservoirs in 11 wells are statistically analysed,and a model test for geological conditions is run.Based on the above results and combined with drilling and seismic data,it is demonstrated that the seismic compressional wave velocity of gas bearing sandstones deep less than 2500m is 30% or more lower than that of surrounding rocks(mudstones).The tops of such gas bearing sandstones may show strong amplitude anomalies(bright spots)on seismic cross sections and the third AVO anomalies whose amplitude increases with offset on CDP gathers and whose velocity is relatively lower on trace intergration section.Using bright spot,AVO and trace integration tool,such gas bearing sandstones can be effectively recognized before exploratory drilling.For the first time,this paper set up a synthetic gas reservoir prediction procedure focusing on the above three techniques,which has been put into use in shallow gas exploration in Yinggehai Basin,resulting in successive discoveries of several medium to small gas fields such as LD20 1,LD22 1 and LD8 1.
Keywords:Yinggehai Basin  shallow gas reservoir  recognition  bright spot  AVO  trace integration  synthetic prediction  
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