APPLIED GEOPHYSICS
 
        首页  |  版权声明  |  期刊介绍  |  编 委 会  |  收录情况  |  期刊订阅  |  下载中心  |  联系我们  |  English
应用地球物理  2017, Vol. 14 Issue (2): 247-257    DOI: 10.1007/s11770-017-0617-x
论文 最新目录 | 下期目录 | 过刊浏览 | 高级检索 Previous Articles  |  Next Articles  
频域谱反演提高转换波薄层分辨率方法研究与应用
张华1,2,3,4 ,贺振华1,2,李亚林3,4,李瑞1,2,何光明3,4,李忠3,4
1. “油气藏地质与开发工程”国家重点实验室,四川成都 610059
2. 成都理工大学地球物理学院,四川成都 610059
3. 中国石油集团川庆钻探工程有限公司地球物理勘探公司,四川成都 610213
4. 中国石油天然气集团公司山地地震技术试验基地,四川成都 610213
Research and application of spectral inversion technique in frequency domain to improve resolution of converted PS-wave
Zhang Hua1,2,3,4, He Zhen-Hua1,2, Li Ya-Lin3,4, Li Rui1,2, He Guamg-Ming3,4, and Li Zhong3,4
1.State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu 610059, China.
2. College of Geophysics,Chengdu University of Technology, Chengdu 610059, China.
3. Geophysical Exploration Company, Chuanqing Drilling Engineering Co.Ltd., CNPC, Chengdu 610213, China.
4. Mountain Geophysical Technology Test Center, CNPC, Chengdu 610213, China.
 全文: PDF (1131 KB)   HTML ( KB)   输出: BibTeX | EndNote (RIS)      背景资料
摘要 多波勘探是提高致密、低渗透等复杂油气藏勘探开发精度的有效手段,但转换波属于典型的低信噪比、低分辨率资料,如何最大程度提高转换波纵向分辨率已成为多波处理的一个难点。谱反演技术能有效解决常规反褶积提频技术受频带限制影响,提高分辨率能力有限的问题,能最大程度提高数据分辨率,便于识别薄层,但该技术的难点是如何通过稳定的反演算法得到高精度反射系数,如何把反射系数恢复为宽频的地震数据?本文在前人研究的基础上利用全变差作为先验信息有助于求解欠定问题的优势,提出了一种基于全变差约束的最小二乘反演算法,提高反演的精度和稳定性,并利用高斯拟合振幅谱模拟得到宽频子波数据,通过在蓬莱地区的高分辨率数据恢复处理,得到分辨率更高的转换波。理论试验和实际资料应用证明了该方法能较大幅度地提高转换波资料分辨率,为后续速度反演、储层反射信息提取提供更准确的数据。
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
关键词谱反演   分辨率   宽频子波   薄储层     
Abstract: Multi-wave exploration is an effective means for improving precision in the exploration and development of complex oil and gas reservoirs that are dense and have low permeability. However, converted wave data is characterized by a low signal-to-noise ratio and low resolution, because the conventional deconvolution technology is easily affected by the frequency range limits, and there is limited scope for improving its resolution. The spectral inversion techniques is used to identify λ/8 thin layers and its breakthrough regarding band range limits has greatly improved the seismic resolution. The difficulty associated with this technology is how to use the stable inversion algorithm to obtain a high-precision reflection coefficient, and then to use this reflection coefficient to reconstruct broadband data for processing. In this paper, we focus on how to improve the vertical resolution of the converted PS-wave for multi-wave data processing. Based on previous research, we propose a least squares inversion algorithm with a total variation constraint, in which we uses the total variance as a priori information to solve under-determined problems, thereby improving the accuracy and stability of the inversion. Here, we simulate the Gaussian fitting amplitude spectrum to obtain broadband wavelet data, which we then process to obtain a higher resolution converted wave. We successfully apply the proposed inversion technology in the processing of high-resolution data from the Penglai region to obtain higher resolution converted wave data, which we then verify in a theoretical test.  Improving the resolution of converted PS-wave data will provide more accurate data for subsequent velocity inversion and the extraction of reservoir reflection information.
Key wordsspectral inversion   resolution   broadband wavelet   thin reservoir   
收稿日期: 2016-02-25;
基金资助:

本研究由中国石油天然气集团科学研究与技术开发项目(编号:2013E-38-08)资助。

引用本文:   
. 频域谱反演提高转换波薄层分辨率方法研究与应用[J]. 应用地球物理, 2017, 14(2): 247-257.
. Research and application of spectral inversion technique in frequency domain to improve resolution of converted PS-wave[J]. APPLIED GEOPHYSICS, 2017, 14(2): 247-257.
 
[1] Chopra, S., Castagna, J. P., and Portniaguine, O., 2006, Seismic resolution and thin-bed reflectivity inversion: CSEG Recorder, 31(1), 19−25.
[2] Chopra, S., Castagna, J. P., and Xu. Y., 2009, Thin-bed reflectivity inversion and some applications: First Break 27(5), 55−62.
[3] Chen, X. H., He, Z. H., Zhu, S. X., et al, 2012, Seismic low-frequency-based calculation of reservoir fluid mobility and its application: Applied Geophysics, 9(3), 326−332.
[4] Cai, X. L., 2000, Application of Yu wavelet to seismic data processing: Oil Geophysical Prospecting, 35(4), 497−507.
[5] Liu, X., Yin, X., Wu, G., et al., 2016, Identification of deep reservoir fluids based on basis pursuit inversion for elastic impedance: Chinese Journal of Geophysics, 59(1), 277−286
[6] Li, G. F., Xiong, J. L., Zhou, H., et al, 2008, Seismic reflection characteristics of fluvial sand and shale inter-bedded layers: Applied Geophysics, 5(3), 219−229.
[7] Liu, C., Li, B. N., Zhao, X., et al, 2014, Fluid identification based on frequency-dependent AVO attribute inversion in multi-scale fracture media: Applied Geophysics, 11(4), 384−394.
[8] Li, G. F., Qin, D. H., Peng, G. X., et al., 2013, Experimental analysis and application of sparsity constrained deconvolution: Applied Geophysics, 10(2), P191−200.
[9] Long, Y., Han, L. G., Han, L., et al, 2012, Ll norm optimal solution match processing in the wavelet domain: Applied Geophysics, 9(4), P451−458.
[10] Nguyen, T., and Castagna, J., 2010, High resolution seismic reflectivity inversion: Journal of Seismic Exploration, 303-320.
[11] Oyem, A., and Castagna, J., 2013, Layer thickness estimation from the frequency spectrum of seismic reflection data: 83th Annual International Meeting, SEG, Expanded Abstracts, 1451−1455.
[12] Partyka, G. A., Gridley, J. A., and Lopez, J. A., 1999, Interpretational applications of spectral decomposition in reservoir characterization: The Leading Edge, 18, 353-360
[13] Portniaguine, O., and Castagna, J. P., 2004, Inverse spectral decomposition: 74th Annual International: Meeting, SEG, Expanded Abstracts, 1786-1789.
[14] Portniaguine, O., Castagna J. P., 2005, Spectral inversion: Lessons from modeling and Boonesville case study: 75th Annual International Meeting, SEG, Expanded Abstracts, 1638−164.
[15] Puryear, C. I., and Castagna, J. P., 2006, An algorithm for calculation of bed thickness and reflection coefficients from amplitude spectrum: 76th Annual International Meeting, SEG, Expanded Abstracts, 1767-1770.
[16] Puryear, C. I., and Castagna, J. P., 2008, Layer-thickness determination and stratigraphic interpretation using spectral inversion: Theory and application: Geophysics, 73(2), R37-R48.
[17] Yu, S. P., 1996, Wide-band Ricker wavelet: OGP, V31(5), 615.
[18] Yuan, S. Y., Wang, S. X., and Tian, N., 2009, Swarm intelligence optimization and its application in geophysical data inversion: Applied Geophysics, 6(2), 166-174.
[19] Yang, H., Zheng, X. D., Ma, S. F., et al., 2011, Thin-bed reflectivity inversion based on matching pursuit: SEG Technical Program Expanded Abstracts 2011, 2586−2590.
[20] Yuan, S. Y., and Wang, S. X., 2013, Spectral sparse Bayesian learning reflectivity inversion: Geophysical Prospecting, 61(4), 735-746.
[21] Zhou, D., H., Wang, B., Shen, Z., H., and Peng, G., 2014, Geostatistical spectral inversion: the thin layer study using spectral inversion method with Geostatistical Information: SEG Technical Program Expanded Abstracts, 3272−3276.
[22] Zhang, S. Q., Han, L. G., Li, C., et al., 2015, Computation method for reservoir fluid mobility based on high-resolution inversion spectral decomposition: Geophysical Prospecting for Petroleum, 54(2), 142−149.
[23] Zhang, L. Y., Wang, Y. C., and Pei, J. Y., 2015, Three-component seismic data in thin interbedded reservoir exploration: Applied Geophysics, 13(1), 79−85.
[24] Zhang, J. H., Zhang, B. B., Zhang, Z. J., et al., 2015, Low-frequency data analysis and expansion: Applied Geophysics, 12(2), 212−220.
[25] Zhang, R., and Castagna, J., 2011, Seismic sparse-layer reflectivity inversion using basis pursuit decomposition: Geophysics, 76(6), R147-R158.
[1] 王德营,孔雪,董烈乾,陈立华,王永军,王晓晨. 一种非白噪反射系数序列的预测反褶积方法*[J]. 应用地球物理, 2019, 16(1): 109-123.
[2] 武绍江,王一博,马玥,常旭. 基于L0范数的超高分辨率最小二乘叠前Kirchhoff深度偏移[J]. 应用地球物理, 2018, 15(1): 69-77.
[3] 姬战怀,严胜刚. 改进的Gabor小波变换的特性在地震信号处理和解释中的应用[J]. 应用地球物理, 2017, 14(4): 529-542.
[4] 王德营,黄建平,孔雪,李振春,王姣. 基于时频二次谱的提高地震资料分辨率方法[J]. 应用地球物理, 2017, 14(2): 236-246.
[5] 田雨,徐洪,张兴阳,王红军,郭同翠,张良杰,龚幸林. 基于图论多分辨率聚类分析的测井岩相识别研究——以阿姆河盆地台内滩气田为例[J]. 应用地球物理, 2016, 13(4): 598-607.
[6] 陈波,贾晓峰,谢小碧. 基于染色算法的宽频带地震照明及分辨率分析[J]. 应用地球物理, 2016, 13(3): 480-490.
[7] 车小花,乔文孝,鞠晓东,王瑞甲. 基于相控圆弧阵的方位声波固井质量评价:数值模拟与现场测试[J]. 应用地球物理, 2016, 13(1): 194-202.
[8] 宋建国, 宫云良, 李珊. 斜缆数据频率域高分辨率Radon变换与虚反射消除方法[J]. 应用地球物理, 2015, 12(4): 564-572.
[9] 王雄文, 王华忠. 稀疏时频分解方法的研究与运用[J]. 应用地球物理, 2014, 11(4): 447-458.
[10] 周怀来, 王峻, 王明春, 沈铭成, 张昕锟, 梁平. 基于广义S变换的地震资料振幅谱补偿和相位谱校正方法研究[J]. 应用地球物理, 2014, 11(4): 468-478.
[11] 李志娜, 李振春, 王鹏, 徐强. λ-f域高分辨率Radon变换多次波压制方法研究[J]. 应用地球物理, 2013, 10(4): 433-441.
[12] 李国发, 秦德海, 彭更新, 岳英, 翟桐立. 稀疏约束反褶积方法实验分析与应用研究[J]. 应用地球物理, 2013, 10(2): 191-200.
[13] 沈洪垒, 田钢, 石战结. 基于高灵敏度检波器数据的分频匹配滤波方法及其应用[J]. 应用地球物理, 2013, 10(1): 15-24.
[14] 李国发, 彭更新, 岳英, 王万里, 崔永福. 基于信号纯度谱的有色反褶积[J]. 应用地球物理, 2012, 9(3): 333-340.
[15] 李子顺. 高密度偏移技术原理与应用[J]. 应用地球物理, 2012, 9(3): 286-292.
版权所有 © 2011 应用地球物理
技术支持 北京玛格泰克科技发展有限公司