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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 ;

【DOI】

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

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This Article

ISSN:1000-7210

CN: 13-1095/TE

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

December 2018

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Abstract

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