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朱振宇1 高佳伦2 姜秀娣1 孙文博1 薛东川1 王清振1

(1.中海油研究总院有限责任公司, 北京 100028)
(2.中国石油大学 (北京) , 北京 102200)

【摘要】首先阐述了频谱分解方法的基本原理, 然后介绍了三参数小波, 重点研究了每个参数对小波的影响, 随后设计了正演模型, 利用三参数小波进行时频分析;最后利用基于三参数小波的频谱分解方法预测储层, 发现三参数小波能更好地显示细微的地质信息。模型测试及实际地震资料应用表明:①三参数小波的灵活性高, 小波的调制频率σ影响小波的震荡程度。能量衰减因子τ影响小波的宽窄, 当τ较大时, σ对小波的影响减弱。能量延迟因子β的影响颇为复杂, 当其为周期的整数倍时, 小波只产生时移, 可以匹配零相位子波;当其为周期的非整数倍时, 小波不仅产生时移, 而且产生相位延迟, 可以匹配非零相位子波。同时需要注意, β非零会造成河道埋深解释错误。②三参数小波变换较Morlet小波变换具有较好的时频分辨率, 能更好地刻画薄互层内细微的地质沉积结构。③实际处理中可将从目的层段提取的地震子波与三参数小波做相关, 优选相关性大的参数组合, 从而获得最佳的三参数小波。

【关键词】 频谱分解;三参数小波;小波变换;时频分析;储层预测;


【基金资助】 国家重点研发计划课题“南海多类型天然气水合物成藏地质过程与富集规律 (2017YFC0307301) ”; 中海石油 (中国) 有限公司科技项目“珠江口盆地时移地震技术应用先导研究” (YXKY-2017-ZY-14) 联合资助;

Spectrum decomposition based on three-parameter wavelet

ZHU Zhenyu1 GAO Jialun2 JIANG Xiudi1 SUN Wenbo1 XUE Dongchuan1 WANG Qingzhen1

(1.CNOOC Research Institute , Beijing, China 100028)
(2.China University of Petroleum (Beijing) , Beijing, China 102200)
【Knowledge Link】caprock

【Abstract】We first describe the basic principle of spectrum decomposition, then introduce three-parameter (TP) wavelet, and study the influence of each parameter on wavelet. After that, we conduct time-frequency analysis with three parameters based on forward modeling. Finally, we perform reservoir characterization with TP wavelet. It is found that subtle geological information can be highlighted. The following understanding are obtained based on model and real data tests: ① the TP wavelet has high flexibility, the modulation frequency σ of the wavelet affects the vibration degree of the wavelet, the energy attenuation parameter τ controls attenuation speed of attenuation function. When the value of τ is relatively big, the σ has less influence on the wavelet. The influence of the energy lag parameter β on the wavelet shape is more complex. When β is an integer multiple of trigonometric function period, only wavelet time shift occurs, it can be used to match the zero phase wavelet; when the β is not an integer multiple of trigonometric function period, wavelet time shift and deformation are generated, it can be used to match the non-zero phase wavelet. Deeply buried channels may be misinterpreted when β is not equal to zero; ② compared with Morlet wavelet, TP wavelet can more precisely depict subtle sedimentary structures in thin interbeds; ③ in real data processing, the optimal parameter combinations can be obtained according to the correlation of various forms of basic wavelets with the real wavelet extracted from targets.

【Keywords】 spectrum decomposition; three-parameter wavelet ; wavelet transform ; time-frequency analysis ; reservoir characterization ;


【Funds】 National Key Research and Development Project (2017YFC0307301) ; CNOOC Science and Technology Project (YXKY-2017-ZY-14) ;

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This Article


CN: 13-1095/TE

Vol 53, No. 06, Pages 1299-1306+1116

December 2018


Article Outline



  • 1 Introduction
  • 2 Spectrum decomposition method
  • 3 TP wavelet transform
  • 4 Model testing and application of actual seismic data
  • 5 Conclusions
  • References