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频谱分解技术在储层预测中的应用
引用本文:袁志云,孔令洪,王成林. 频谱分解技术在储层预测中的应用[J]. 石油地球物理勘探, 2006, 0(Z1)
作者姓名:袁志云  孔令洪  王成林
作者单位:北京大学地空学院 北京市海淀区北京大学地空学院(袁志云),中国石油勘探开发研究院 100871(孔令洪),中国石油勘探开发研究院 中国石油大学.北京(王成林)
摘    要:频谱分解成像反映油藏物理属性要比其他地震属性更加直接。本文根据频谱分解技术的基本原理,进行了断裂系统识别、沉积环境分析和储层横向预测。其具体思路为:首先基于地震叠后数据体完成了研究区主要目的层的频谱分解处理,得到了一系列离散频率的调谐数据体,然后采用地质成像和动画解释技术,识别断裂体系、沉积环境、储层分布等地质现象。应用实例表明,在识别断裂系统时,不仅能指导剖面的断层解释和平面组合,而且可以精细断层解释;在进行沉积环境分析时,不仅可以确定沉积相类型,而且可以确定沉积相的形态;在进行储层预测时可同时确定砂体的展布和厚度。

关 键 词:频谱分解  调谐体  傅氏变换  短时窗  储层预测

Application of spectrum decomposition in reservoir prediction.
Yuan Zhi-yun,Kong Ling-hong and Wang Cheng-lin.Yuan Zhi-yun,College of Earth and Space,Beijing University,Beijing City,,China. Application of spectrum decomposition in reservoir prediction.[J]. Oil Geophysical Prospecting, 2006, 0(Z1)
Authors:Yuan Zhi-yun  Kong Ling-hong  Wang Cheng-lin.Yuan Zhi-yun  College of Earth  Space  Beijing University  Beijing City    China
Affiliation:Yuan Zhi-yun,Kong Ling-hong and Wang Cheng-lin.Yuan Zhi-yun,College of Earth and Space,Beijing University,Beijing City,100871,China
Abstract:Spectrum decomposition imaging more directly reflects the physical attributes of reservoir than other seismic attributes.Using the basic principle of spectrum decomposition,the paper conducted the recognition of fault system,analysis of sedimentary environment and lateral prediction of reservoir.The concrete ideas are: firstly,based on the poststack seismic data volume,the spectrum decomposition processing of major targets in studied zone is implemented and a series of tuning data volume with discrete frequencies is obtained;then,using geologic imaging and animation interpretation technique to identify fault system,sedimentary environment and distribution of reservoirs.The application cases showed it can not only guide the interpretation of faults on the sections and their planar combination,but also finely interpret the faults when identifying the fault system;the technique can define not only the type of sedimentary facies,but also their configuration in analysis of sedimentary environment the distribution and thickness of sand body can be simultaneously defined in prediction of reservoirs.
Keywords:spectrum decomposition  tuning volume  Fourier transform  short window  reservoir prediction
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