Please wait a minute...
E-mail Alert Rss
 
物探与化探  2023, Vol. 47 Issue (6): 1588-1594    DOI: 10.11720/wtyht.2023.0009
  方法研究·信息处理·仪器研制 本期目录 | 过刊浏览 | 高级检索 |
时变分频反褶积在提高薄砂体预测精度方面的应用
赵泽茜1(), 成丽芳2, 范殿佐1
1.中国地质大学(北京) 地球物理与信息技术学院,北京 100083
2.运城市规划和自然资源局,山西 运城 044000
Application of time-varying frequency-division deconvolution in improving the prediction accuracy of thin sand bodies
ZHAO Ze-Xi1(), CHENG Li-Fang2, FAN Dian-Zuo1
1. School of Geophysics and Information Technology, China University of Geosciences (Beijing), Beijing 100083, China
2. Yuncheng Municipal Bureau of Planning and Natural Resources, Yuncheng 044000, China
全文: PDF(6233 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 

地震资料分辨率可以直接影响油藏描述精度。为了提升地震资料的分辨率,针对复杂断块、含油砂体厚度薄、砂体预测难等问题,建立了时变分频反褶积提频技术流程。首先,针对地震信号进行分时窗,在每一时窗内求地震子波,从而得到子波的振幅谱;然后,在每一时窗内利用对应地震子波进行反褶积,求取反射系数;最后,综合整个地震资料的反射系数和褶积高频率、低频率子波,得到高分辨率的宽频地震信号。将此方法应用于中原油田文南地区实际三维地震资料的处理中,结果表明:该方法能够明显拓宽采集的三维叠后地震资料高频有效信息,对单砂体的刻画能力有较大提高,得到的数据效果更有利于识别薄层,且预测结果与实际钻井结果吻合程度更高。该技术在复杂断块地区有较大的应用前景。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
赵泽茜
成丽芳
范殿佐
关键词 时变分频反褶积时变子波砂体预测文南地区    
Abstract

The resolution of seismic data directly influences the characterization accuracy of oil reservoirs. To improve the resolution for effective sand body prediction, this study established a frequency enhancement technology process based on time-varying frequency-division deconvolution for thin oil-bearing sand bodies occurring in complex fault blocks. First, seismic signals were separated into different time windows, in which seismic wavelets were computed to obtain their amplitude spectra. Then, the corresponding seismic wavelets were deconvoluted within each time window to obtain the reflection coefficients. Finally, high-resolution broadband seismic signals were attained by integrating the reflection coefficients of the entire seismic data and convolving high-and low-frequency wavelets. This technology process was employed to process the actual 3D seismic data from the Wennan area of the Zhongyuan Oilfield. As indicated by the results, this technology process had a significantly elevated capacity to depict a single sand body by expanding the high-frequency effective information in acquired 3D post-stack seismic data, thus yielding high-quality data for the identification of thin sand bodies. Moreover, the prediction results were highly consistent with the actual drilling results. Therefore, the time-varying frequency-division deconvolution has great potential for application in complex fault blocks.

Key wordstime-varying frequency-division deconvolution    time-varying wavelet    sand body prediction    Wennan area
收稿日期: 2023-01-04      修回日期: 2023-04-21      出版日期: 2023-12-20
:  P631.4  
基金资助:中国石油化工股份有限公司项目“东濮凹陷高密度地震勘探关键技术研究与应用”(P22146)
作者简介: 赵泽茜(1998-),女,硕士研究生,主要研究方向为三维地震综合解释。Email:HyggeZhao@163.com
引用本文:   
赵泽茜, 成丽芳, 范殿佐. 时变分频反褶积在提高薄砂体预测精度方面的应用[J]. 物探与化探, 2023, 47(6): 1588-1594.
ZHAO Ze-Xi, CHENG Li-Fang, FAN Dian-Zuo. Application of time-varying frequency-division deconvolution in improving the prediction accuracy of thin sand bodies. Geophysical and Geochemical Exploration, 2023, 47(6): 1588-1594.
链接本文:  
https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2023.0009      或      https://www.wutanyuhuatan.com/CN/Y2023/V47/I6/1588
Fig.1  地质模型及其正演结果
Fig.2  对比正演模型的不同时窗的子波
Fig.3  预测反褶积与时变分频反褶积提频后地震剖面及频谱
Fig.4  时变分频反褶积提频前后地震剖面及频谱
Fig.5  时变分频反褶积提频前后地震剖面的细节对比
Fig.6  时变分频反褶积提频前后时间切片对比
[1] 刁瑞. 分频带预测反褶积方法研究[J]. 断块油气田, 2015, 22(1):53-57.
[1] Diao R. Method of separate frequency predictive deconvolution[J]. Fault-Block Oil & Gas Field, 2015, 22(1):53-57.
[2] 刘俊, 吴淑玉, 高金耀, 等. 南黄海中部浅水区多次波衰减技术及其效果分析[J]. 物探与化探, 2016, 40(3):568-577.
[2] Liu J, Wu S Y, Gao J Y, et al. An effectiveness analysis of multiple depression technique in the Shallow water of the central uplift in South Yellow Sea[J]. Geophysical and Geochemical Exploration, 2016, 40(3):568-577.
[3] 朱恒, 文晓涛, 金炜龙, 等. 基于反褶积短时傅立叶变换的油气检测[J]. 地球物理学进展, 2015, 30(5):2354-2359.
[3] Zhu H, Wen X T, Jin W L, et al. Oil and gas detection based on deconvolutive short-time Fourier transform[J]. Progress in Geophysics, 2015, 30(5):2354-2359.
[4] 徐倩茹, 孙成禹, 乔志浩, 等. 基于Gabor变换的地震资料高分辨率处理方法研究[J]. 断块油气田, 2016, 23(4):460-464.
[4] Xu Q R, Sun C Y, Qiao Z H, et al. High-resolution processing method of seismic data based on Gabor transform[J]. Fault-Block Oil & Gas Field, 2016, 23(4):460-464.
[5] Morlet J, Arens G, Fourgeau E, et al. Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media[J]. Geophysics, 1982, 47(2):203-203.
doi: 10.1190/1.1441328
[6] 王西文, 刘全新, 高静怀, 等. 地震资料在小波域的分频处理与重构[J]. 石油地球物理勘探, 2001, 36(1):78-85.
[6] Wang X W, Liu Q X, Gao J H, et al. Frequency-shared processing and reconstitution of seismic data in wavelet domain[J]. Oil Geophysical Prospecting, 2001, 36(1):78-85.
[7] 李雨青, 水鹏朗, 林英. 基于多谱图叠加阈值的抑制WVD交叉项的新方法[J]. 电子与信息学报, 2006, 28(8):1435-1438.
[7] Li Y Q, Shui P L, Lin Y. New method to suppress cross-terms of WVD via thresholding superimposition of multiple spectrograms[J]. Journal of Electronics & Information Technology, 2006, 28(8):1435-1438.
[8] 李合群, 孟小红, 赵波. 地震数据Q吸收补偿应用研究[J]. 石油地球物理勘探, 2010, 45(2):190-195.
[8] Li H Q, Meng X H, Zhao B. Application studies on Q absorption and compensation for seismic data[J]. Oil Geophysical Prospecting, 2010, 45(2):190-195.
[9] Wang Y H. A stable and efficient approach of inverse Q filtering[J]. Geophysics, 2002, 67(2): 657-663.
doi: 10.1190/1.1468627
[10] 王珊, 于承业, 王云专, 等. 稳定有效的反Q滤波方法[J]. 物探与化探, 2009, 33(6):696-699.
[10] Wang S, Yu C Y, Wang Y Z, et al. Researches on stabilized and effective inverse Q filtering[J]. Geophysical and Geochemical Exploration, 2009, 33(6):696-699.
[11] Stockwell R G, Mansinha L, Lowe R. Localization of the complex spectrum: The S transform[J]. IEEE Transactions on Signal Processing, 1996, 44(4):98-1001.
[12] 杜斌山, 雍学善, 王建功, 等. 测井约束下高精度叠前地震速度预测[J]. 岩性油气藏, 2019, 31(4):92-100.
[12] Du B S, Yong X S, Wang J G, et al. High precision prestack seismic velocity prediction based on well logging constraint[J]. Lithologic Reservoirs, 2019, 31(4):92-100.
[13] 杨培杰, 印兴耀. 地震子波提取方法综述[J]. 石油地球物理勘探, 2008, 43(1):123-128.
[13] Yang P J, Yin X Y. Summary of seismic wavelet pick-up[J]. Oil Geophysical Prospecting, 2008, 43(1):123-128.
[14] 蔡剑华, 肖永良. 基于广义S变换时频滤波的MT数据去噪[J]. 地质与勘探, 2021, 57(6):1383-1390.
[14] Cai J H, Xiao Y L. Denoising of MT data by time-frequency filtering based on the generalized S transform[J]. Geology and Exploration, 2021, 57(6):1383-1390.
[15] Yeh J R, Shieh J S, Huang N E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2):135-156.
doi: 10.1142/S1793536910000422
[16] 李振春, 张军华. 地震数据处理方法[M]. 东营: 中国石油大学出版社, 2004.
[16] Li Z C, Zhang J H. Seismic data processing method[M]. Dongying: China University of Petroleum Press, 2004.
[17] Leinbach J. Wiener spiking deconvolution and minimum-phase wavelets: A tutorial[J]. The Leading Edge, 1995, 14(3):189-192.
doi: 10.1190/1.1437110
[18] Zhang P, Dai Y S, Zhang H Q, et al. Combining CEEMD and recursive least square for the extraction of time-varying seismic wavelets[J]. Journal of Applied Geophysics, 2019, 170(6):103854.
doi: 10.1016/j.jappgeo.2019.103854
[19] 陈学华, 贺振华, 黄德济. 广义S变换及其时频滤波[J]. 信号处理, 2008, 24(1):28-31.
[19] Chen X H, He Z H, Huang D J. Generalized S transform and its time-frequency filtering[J]. Journal of Signal Processing, 2008, 24(1):28-31.
[1] 陈德元, 张保卫, 岳航羽, 范含周. 基于特征曲线构建的地质统计反演在薄砂体预测中的应用[J]. 物探与化探, 2018, 42(5): 999-1005.
[2] 张, 曹思远, 郑晓东, 路交通. 基于高阶统计的时变子波估计及其应用[J]. 物探与化探, 2014, 38(5): 989-995.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-3
版权所有 © 2021《物探与化探》编辑部
通讯地址:北京市学院路29号航遥中心 邮编:100083
电话:010-62060192;62060193 E-mail:whtbjb@sina.com