Please wait a minute...
E-mail Alert Rss
 
物探与化探  2025, Vol. 49 Issue (6): 1363-1371    DOI: 10.11720/wtyht.2025.0117
  方法研究信息处理仪器研制 本期目录 | 过刊浏览 | 高级检索 |
径向道变换域叠前Q值估计
唐传章1(), 王金宽1, 魏涛1, 黄新亚1, 程万里2,3,4, 王守东2,3, 李莹2,3
1.中国石油 华北油田分公司, 河北 任丘 062552
2.中国石油大学(北京) 地球物理学院, 北京 102249
3.中国石油大学(北京) 油气资源与探测国家重点实验室, 北京 102249
4.中海石油(中国)有限公司 海南分公司, 海南 海口 570100
Estimation of pre-stack Q-values in the radial trace transform domain
TANG Chuan-Zhang1(), WANG Jin-Kuan1, WEI Tao1, HUANG Xin-Ya1, CHENG Wan-Li2,3,4, WANG Shou-Dong2,3, LI Ying2,3
1. Huabei Oilfield Company, PetroChina, Renqiu 062552, China
2. College of Geophysics, China University of Petroleum(Beijing), Beijing 102249, China
3. State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum(Beijing), Beijing 102249, China
4. CNOOC(China) Limited Hainan Branch, Haikou 570100, China
全文: PDF(2483 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 

准确估计品质因子Q值对于提高地震数据分辨率和储层描述至关重要。传统Q值估算方法多利用叠后数据进行估计,忽略了射线路径对Q值估计的影响,同时叠加的平均效应能够改变地震数据的衰减特性,降低了Q值估计的准确性。叠前数据较准确地保留了地下介质的衰减特征,利用叠前数据能够进行更准确的Q值估计。本文用径向道变换将叠前数据转换到视速度—旅行时(R-T)域,结合对数谱面积双重差(LSADD)方法,提出QVAV_LSADD叠前Q值估计方法,该方法在不需要精确的层速度的情况下,考虑了射线路径的影响。通过模拟数据与实际数据处理,验证了该方法具备较高的精度与抗噪能力。

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
唐传章
王金宽
魏涛
黄新亚
程万里
王守东
李莹
关键词 介质Q叠前数据QVAVLSADD    
Abstract

Accurate estimation of the quality factor(Q) is essential for enhancing seismic data resolution and reservoir characterization.Conventional Q estimation methods generally utilize post-stack data, which neglect the impacts of raypaths.Moreover,the average effect of stacking alters the attenuation of seismic data,reducing the accuracy of Q estimation.Compared to post-stack data,the pre-stack data more faithfully preserve the attenuation properties of subsurface media,enabling more accurate Q estimation.Therefore,this study converted pre-stack data into the apparent velocity and travel time(R-T) domain,using the radial trace(RT) transform.Combined with the logarithmic spectral area double difference(LSADD) method,a pre-stack Q estimation method named QVAV_LSADD was proposed.This method accounted for the impacts of raypaths under imprecise interval velocities.Its high accuracy and strong noise resistance were validated through the processing of both synthetic and real data.

Key wordsquality factor(Q-value) of medium    pre-stack data    Q versus apparent velocity(QVAV)    logarithmic spectral area double difference(LSADD)
收稿日期: 2025-06-15      修回日期: 2025-09-22      出版日期: 2025-12-20
ZTFLH:  P631.4  
基金资助:中国石油天然气股份有限公司华北油田校企合作项目(HBYT-KTY-2023);国家重点研发计划课题(2019YFC0312003)
引用本文:   
唐传章, 王金宽, 魏涛, 黄新亚, 程万里, 王守东, 李莹. 径向道变换域叠前Q值估计[J]. 物探与化探, 2025, 49(6): 1363-1371.
TANG Chuan-Zhang, WANG Jin-Kuan, WEI Tao, HUANG Xin-Ya, CHENG Wan-Li, WANG Shou-Dong, LI Ying. Estimation of pre-stack Q-values in the radial trace transform domain. Geophysical and Geochemical Exploration, 2025, 49(6): 1363-1371.
链接本文:  
https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2025.0117      或      https://www.wutanyuhuatan.com/CN/Y2025/V49/I6/1363
Fig.1  带参数的三层模型
Fig.2  合成的X-T域(a)与转换到R-T域(b)的CMP道集
X-T域的径向射线(红色虚线)上的AB两点映射为R-T域同一视速度(1 000 m/s)上的CD两点
Fig.3  RT变换域Q值估计示意
Fig.4  带参数的五层模型
Fig.5  合成的X-T域(a)与转换到R-T域(b)的CMP道集
Fig.6  不同方法(LSADD、LSR和PFS)估计的Q值随炮检距的变化曲线
Fig.7  利用不同方法从CMP道集估计出的Q值对比
Fig.8  实际的X-T域(a)与转换到R-T域(b)的CMP道集
Fig.9  利用QVAV_LSADD方法(a)和QVAV方法(b)估计得到的等效Q值和层Q
[1] Tonn R. The determination of the seismic quality factor Q from vsp data:A comparison of different computational methods[J]. Geophysical Prospecting, 1991, 39(1):1-27.
[2] 王本锋, 陈小宏, 李景叶, 等. 基于反演的稳定高效衰减补偿方法[J]. 地球物理学报, 2014, 57(4):1265-1274.
[2] Wang B F, Chen X H, Li J Y, et al. A stable and efficient attenuation compensation method based on inversion[J]. Chinese Journal of Geophysics, 2014, 57(4):1265-1274.
[3] 刘国昌, 陈小宏, 杜婧, 等. 基于整形正则化和S变换的Q值估计方法[J]. 石油地球物理勘探, 2011, 46(3):417-422,500,327.
[3] Liu G C, Chen X H, Du J, et al. Seismic Q estimation using S-transform with regularized inversion[J]. Oil Geophysical Prospecting, 2011, 46(3):417-422,500,327.
[4] Luo C, Huang G T, Li X Y, et al. Q estimation by combining ISD with LSR method based on shaping-regularized inversion[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9):1457-1461.
[5] Cheng W L, Wang S D, Zhou C, et al. Q estimation based on the logarithmic spectral area double difference[J]. Geophysics, 2022, 87(2):V155-V167.
[6] Dasgupta R, Clark R A. Estimation of Q from surface seismic reflection data[J]. Geophysics, 1998, 63(6):2120-2128.
[7] Reine C, Clark R, van der Baan M. Robust prestack Q-determination using surface seismic data:Part 1—Method and synthetic examples[J]. Geophysics, 2012, 77(1):R45-R56.
[8] Reine C, Clark R, van der Baan M. Robust prestack Q-determination using surface seismic data:Part 2—3D case study[J]. Geophysics, 2012, 77(1):B1-B10.
[9] Hackert C L, Parra J O. Improving Q estimates from seismic reflection data using well-log-based localized spectral correction[J]. Geophysics, 2004, 69(6):1521-1529.
[10] Zheng J J, Wang Y G, Liu H J, et al. The stratum Q estimated by instantaneous seismic wavelets in prestack domain[C]//Houston: SEG Technical Program Expanded Abstracts 2017, Society of Exploration Geophysicists, 2017:3325-3329.
[11] Wu Z W, Wu Y J, Xu M H, et al. Q estimation on CMP gather based on continuous spectral ratio slope method:A Case study in Ahdeb Oil Field,Iraq[C]// Anaheim:SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists, 2018:1544-1548.
[12] Zhang C J, Ulrych T J. Estimation of quality factors from CMP records[J]. Geophysics, 2002, 67(5):1542-1547.
[13] Behura J, Tsvankin I. Estimation of interval anisotropic attenuation from reflection data[J]. Geophysics, 2009, 74(6):A69-A74.
[14] Beckwith J, Clark R, Hodgson L. Estimating frequency-dependent attenuation quality factor values from prestack surface seismic data[J]. Geophysics, 2017, 82(1):O11-O22.
[15] De S Oliveira F, de Figueiredo J J S, Oliveira A G, et al. Estimation of quality factor based on peak frequency-shift method and redatuming operator:Application in real data set[J]. Geophysics, 2017, 82(1):N1-N12.
[16] Claerbout J F. Slant-stacks and radial traces[J]. Stanford Exploration Project, 1975:1-12.
[17] Claerbout J F. Ground roll and radial traces[J]. Stanford Exploration Project, 1983:43-53.
[18] Li F, Wang S D, Chen X H, et al. Prestack nonstationary deconvolution based on variable-step sampling in the radial trace domain[J]. Applied Geophysics, 2013, 10(4):423-432.
[1] 张向宇, 张瑶, 邢琮琮. CM4模型在南海南部某工区磁测数据重处理中的应用[J]. 物探与化探, 2025, 49(6): 1311-1318.
[2] 苑书金, 李发有, 陆文明. 加密井快速优化技术在西非深水浊积砂油藏精细描述中的应用——以Bata油田为例[J]. 物探与化探, 2025, 49(6): 1303-1310.
[3] 刘苗, 邢雯淋, 杨雨松, 任静, 赵秀莲, 李振伟, 陈琳枝. 高阶动校正速度拾取方法在海域超压分布中的应用[J]. 物探与化探, 2025, 49(6): 1386-1392.
[4] 罗宇晨, 罗章清, 刘胜, 欧成华, 王泽宇, 刘畅. 面向高陡构造的棱柱波地震干涉成像方法[J]. 物探与化探, 2025, 49(6): 1372-1379.
[5] 王腾宇, 邓丁丁, 郑多明, 刘洋, 张振, 罗文君. 基于深度学习的零井源距VSP上、下行波分离方法[J]. 物探与化探, 2025, 49(6): 1319-1332.
[6] 周江辉, 刘晓晶, 熊晨皓, 胡鑫, 吴益名. 地震属性与地质力学联合的裂缝建模技术及裂缝有效性分析——以四川盆地涪陵地区侏罗系页岩为例[J]. 物探与化探, 2025, 49(6): 1271-1280.
[7] 国运东. 川东北探区层析与全波形反演联合建模方法及应用[J]. 物探与化探, 2025, 49(5): 1090-1098.
[8] 依尔繁·阿西木江, 卢志明, 艾尼·买买提, 米尔扎提·迪力木拉提, 多力坤·买买提明. 多属性融合技术预测薄互砂体储层厚度——以哈萨克斯坦W油田为例[J]. 物探与化探, 2025, 49(5): 1110-1117.
[9] 周成刚, 苑恒超, 田军, 王云超, 陈彦奇, 杨秋红. 刻画非均质储层边界的梯度结构张量属性阈值选取策略[J]. 物探与化探, 2025, 49(5): 1118-1125.
[10] 杨晓东, 张建强, 耿利强, 张学启, 程慧慧, 康苒. 煤矿井下巷道的地震识别效果及分析[J]. 物探与化探, 2025, 49(5): 1133-1140.
[11] 曹绍贺, 黄中群, 袁春艳, 马百征, 王群武, 张奎. 基于麻雀搜索算法的致密砂岩储层参数非线性反演方法[J]. 物探与化探, 2025, 49(5): 1141-1154.
[12] 汪昆, 吴国忱, 贾宗锋, 杨凌云. 反射波时频域地震波形反演方法[J]. 物探与化探, 2025, 49(5): 1155-1163.
[13] 潘远, 罗聪, 徐林, 陈品雄, 傅宏毅. SOTEM法分量探测能力对比及应用实例[J]. 物探与化探, 2025, 49(5): 1164-1172.
[14] 吴怡, 周长所, 徐国贤, 袁俊亮, 宋晓麟, 曾勇坚, 王群武, 张奎. 基于方位各向异性反演的裂缝型储层预测及流体识别方法[J]. 物探与化探, 2025, 49(5): 1173-1189.
[15] 石学文, 王畅, 张洞君, 冯艳雯. 基于叠前直接反演的页岩气储层脆性及裂缝参数预测方法[J]. 物探与化探, 2025, 49(4): 826-837.
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 , whtbjb@163.com