石油学报 ›› 2008, Vol. 29 ›› Issue (6): 865-869.DOI: 10.7623/syxb200806014

• 地质勘探 • 上一篇    下一篇

基于小波振幅谱和复小波相位谱的高分辨率层序划分

王志坤1,2, 钟建华1, 艾合买提江• 阿布都热合曼1, 郑希民3   

  1. 1. 中国石油大学地球资源与信息学院 山东东营 25706;
    2. 中国石油冀东油田公司勘探开发研究院 河北唐山 063004;
    3. 中国石油勘探开发研究院西北分院 甘肃兰州 730020
  • 收稿日期:2008-01-11 修回日期:2008-03-21 出版日期:2008-11-25 发布日期:2010-05-21
  • 基金资助:
    中国石油天然气集团公司创新基金(2002F70108)资助

Division of high-resolution sequence based on wavelet amplitude spectrum and complex wavelet phase spectrum

WANG Zhikun1,2, ZHONG Jianhua1, Ahmatjan• Abdurahman1, ZHENG Ximin3   

  1. 1. Faculty of Geo-Resource and Information, China University of Petroleum, Dongying 257061, China;
    2. Research Institute of Exploration and Development, PetroChina Jidong Oilfield Company, Tangshan 063004, China;
    3. Northwest Branch, PetroChina Exploration and Development Research Institute, Lanzhou 730020, China
  • Received:2008-01-11 Revised:2008-03-21 Online:2008-11-25 Published:2010-05-21

摘要: 为了从多分辨率的角度充分挖掘测井信号的内部信息,应用小波变换多尺度分解技术,对测井信号进行了复小波连续变换和连续小波分析。利用小波振幅谱能谱带尺度偏移特征、复小波相位谱相位转换线和相位零线特征识别出了层序级次、旋回叠加模式、层序界面和湖泛面。对于不同类型的层序界面和湖泛面,其小波振幅谱和复小波相位谱的响应特征不同。以鄂尔多斯盆地池10井为例,联合应用振幅谱和相位谱进行了层序划分。与传统方法相比,该方法能有效地利用测井信号内部结构信息准确地识别层序界面,为层序划分提供一定依据。

关键词: 高分辨率层序划分, 层序界面, 振幅谱, 相位谱, 小波分析

Abstract: In order to detect the inner information of well logging signals with the view of multi-resolutions adequately, the multi-scale decomposition technique was used to analyze the complex wavelet and continuous wavelet of well logging. The wavelet analyses in amplitude spectrum and phase spectrum are valid in detecting the sequences level, cyclic deposition patterns and flood surface. The different sequence boundary and flood surface have the different response in the amplitude spectrum and phase spectrum. The amplitude spectrum and the phase spectrum were applied to sequence analysis in Well Chi-10 of Ordos Basin. The method was effective for analyzing the sedimentary cycle system and could detect the sequence boundaries accurately using the information of internal structure of logging data.

Key words: high-resolution sequence division, sequence boundary, amplitude spectrum, phase spectrum, wavelet analysis

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