Thin interbed identification method based on seismic waveform similarity
ZHAO Shudong1, SONG Jianguo1, LEI Ganglin2
1. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China; 2. Tarim Oilfield Company, PetroChina, Korla, Xinjiang 841000, China
Abstract：As degrees of oil field exploration and development deepen， most target reservoirs are found to be thin， small， deep， and fractured. High-precision identification of thin interbedding has become the focus of re-servoir prediction. At present， time-frequency analysis methods and spectral decomposition techniques commonly used in thin interbedding identification are limited by seismic data， and the processing results fail to meet the requirements of accurate characterization of thin interbedding. Due to the nonlinear characteristics of geophysical inversion， the results of high-resolution inversion have multiple solutions. In this paper， a thin interbedding identification method based on the similarity of seismic waves is proposed. Firstly， the idea of a waveform library is used to construct the waveform library of well bypass-sensitive logging curves. Secondly， the improved Manhattan distance combined linear correlation coefficient method is then used to calculate the waveform similarity. Finally the waveform similarity is taken as the only driver to establish the mathematical relationship between seismic data and high-resolution logging data， until the high-resolution processing profile is obtained. This method makes full use of the high transverse resolution of seismic data and the high longitudinal resolution of logging data， effectively identifies thin interbedding， and greatly reduces the multi-solution of inversion results. The feasibility of this method is verified by model test. The actual data processing results show that the proposed method significantly improves the longitudinal resolution and provides technical support for the identification of thin interbedding within a tuned scale.
MORLET J,ARENS G,FOURGEAU E,et al.Wave propagation and sampling theory—Part I: sampling theory and complex waves[J].Geophysics,1982,47(2): 206-221.
[2]
MORLET J,ARENS G,FOURGEAU E,et al.Wave propagation and sampling theory—Part II: Sampling theory and complex waves[J].Geophysics,1982,47(2): 222-236.
[3]
PARTYKA G,GRIDLEY J,LOPEZ J.Interpretational applications of spectral decomposition in reservoir characterization[J].The Leading Edge,1999,18(3): 353-360.
[4]
李林峡.高精度谱反演方法研究与应用[D].四川成都: 成都理工大学,2012.
[5]
SINHA S,ROUTH P S,ANNO P D,et al.Spectral decomposition of seismic data with continuous-wavelet transform[J].Geophysics,2005,70(6): P19-P25.
[6]
SUN S,CASTAGNA J P,SIEGFRIED R W.Examples of wavelet transform time-frequency analysis in direct hydrocarbon detection[C].SEG Technical Program Expanded Abstracts,2002,21: 457-460.
[7]
CASTAGNA J P,SUN S,SIEGFRIED R W.Instantaneous spectral analysis:Detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2): 120-127.
[8]
张繁昌,李传辉.非平稳地震信号匹配追踪时频分析[J].物探与化探,2011,35(4): 546-552.ZHANG Fanchang,LI Chuanhui.Matching pursuit time-frequency analysis of non-stationary seismic signals[J].Geophysical and Geochemical Exploration,2011,35(4): 546-552.
[9]
LIU J.Spectral Decomposition and its Application in Mapping Stratigraphy and Hydrocarbons[D].University of Houston,Houston,2006.
[10]
WEI X,WANG X,ZHANG Y,et al.Application of spectral decomposition in hydrocarbon detection[C].SEG Technical Program Expanded Abstracts,2011,30: 1041-1045.
[11]
YIN X,LI K,ZONG Z.Resolution enhancement of robust Bayesian pre-stack inversion in the frequency domain[J].Journal of Geophysics and Engineering,2016,13(5): 646-656.
[12]
LI K,YIN X Y,ZONG Z Y.Bayesian seismic multi-scale inversion in complex Laplace mixed domains[J].Petroleum Science,2017,14: 694-710.
[13]
LIU X X,YIN X Y,LI H S.Optimal variable-grid finite-difference modeling for porous media[J].Journal of Geophysics and Engineering,2014,11(6): 065011.
[14]
王圣川.正则化约束稀疏脉冲地震反演方法及应用研究[D].四川成都: 电子科技大学,2014.
[15]
赵晨,张广智,张佳佳,等.基于Metropolis优化的叠前全局迭代地质统计学反演方法[J].地球物理学报,2020,63(8): 3116-3130.ZHAO Chen,ZHANG Guangzhi,ZHANG Jiajia,et al.Prestack global iteration geostatistical inversion method based on Metropolis sampling algorithm[J].Chinese Journal of Geophysics,2020,63(8): 3116-3130.
[16]
孙瑞莹.先验信息构建与地震随机反演方法研究[D].山东青岛: 中国石油大学(华东),2015.
[17]
杨锴,艾迪飞,耿建华.测井、井间地震与地面地震数据联合约束下的地质统计学随机建模方法研究[J].地球物理学报,2012,55(8): 2695-2704.YANG Kai,AI Difei,GENG Jianhua.A new geostatistical inversion and reservoir modeling technique constrained by well-log,crosshole and surface seismic data[J].Chinese Journal of Geophysics,2012,55(8): 2695-2704.
[18]
王小丹,印兴耀,金惠,等.叠前地震随机反演方法及实际资料应用[J].地球物理学进展,2018,33(6): 2471-2476.WANG Xiaodan,YIN Xingyao,JIN Hui,et al.Pre-stack seismic stochastic inversion and application on real data[J].Progress in Geophysics,2018,33(6): 2471-2476.
王保丽,印兴耀,丁龙翔,等.基于FFT-MA谱模拟的快速随机反演方法研究[J].地球物理学报,2015,58(2): 664-673.WANG Baoli,YIN Xingyao,DING Longxiang,et al.Study of fast stochastic inversion based on FFT-MA spectrum simulation[J].Chinese Journal of Geophy-sics,2015,58(2): 664-673.
[21]
张广智,潘新朋,孙昌路,等.纵横波联合叠前自适应MCMC反演方法[J].石油地球物理勘探,2016,51(5): 938-946.ZHANG Guangzhi,PAN Xinpeng,SUN Changlu,et al.PP- & PS-wave prestack nonlinear inversion based on adaptive MCMC algorithm[J].Oil Geophysical Prospecting,2016,51(5): 938-946.
[22]
李坤,印兴耀,宗兆云.岩石物理驱动的相约束叠前地震概率化反演方法[J].中国科学(地球科学),2020,50(6): 832-854.LI Kun,YIN Xingyao,ZONG Zhaoyun.Facies-constrained prestack seismic probabilistic inversion driven by rock physics[J].Science China (Earth Sciences),2020,50(6): 832-854.
[23]
陈彦虎.地震波形指示反演方法、原理及其应用[D].北京: 中国地质大学(北京),2020.
[24]
高君,毕建军,赵海山,等.地震波形指示反演薄储层预测技术及其应用[J].地球物理学进展,2017,32(1): 142-145.GAO Jun,BI Jianjun,ZHAO Haishan,et al.Seismic waveform inversion technology and application of thinner reservoir prediction[J].Progress in Geophysics,2017,32(1): 142-145.
[25]
陈彦虎,毕建军,邱小斌,等.地震波形指示反演方法及其应用[J].石油勘探与开发,2020,47(6): 1149-1158.CHEN Yanhu,BI Jianjun,QIU Xiaobin,et al.A method of seismic meme inversion and its application[J].Petroleum Exploration and Development,2020,47(6): 1149-1158.
[26]
陈彦虎,陈佳.波形指示反演在煤层屏蔽薄砂岩分布预测中的应用[J].物探与化探,2019,43(6): 1254-1261.CHEN Yanhu,CHEN Jia.The application of seismic meme inversion to thin sand distribution prediction under coal shield[J].Geophysical and Geochemical Exploration,2019,43(6): 1254-1261.
[27]
熊晓军,罗海龙,肖尧,等.基于波形指示反演方法的川中二叠系栖霞组薄储层预测研究[J].地球物理学进展,2022,37(6): 2460-2468.XIONG Xiaojun,LUO Hailong,XIAO Yao,et al.Thin reservoir prediction of Permian Qixia Formation in central Sichuan Basin based on waveform indication inversion method[J].Progress in Geophysics,2022,37(6): 2460-2468.
[28]
胡玮,齐鹏,杨江峰,等.波形指示反演在超深层致密砂岩薄储层中的应用[J].地球物理学进展,2018,33(2): 620-624.HU Wei,QI Peng,YANG Jiangfeng,et al.Application of seismic motion inversion in identification of tight thin super deep reservoirs[J].Progress in Geophysics,2018,33(2): 620-624.
[29]
黄勇,徐立恒,杨会东,等.反演约束的多点地质统计学建模——以大庆长垣陆相油田为例[J].石油地球物理勘探,2022,57(6): 1445-1452.HUANG Yong,XU Liheng,YANG Huidong,et al.Multi-point geostatistical modeling with inversion constraints: a case study of continental oil fields in Daqing placanticline[J].Oil Geophysical Prospecting,2022,57(6): 1445-1452.
[30]
鲁秀芹,汪关妹,杨延辉,等.表示叠前AVO数据的波形指示反演预测煤体结构[J].石油地球物理勘探,2022,57(增刊1): 117-121.LU Xiuqin,WANG Guanmei,YANG Yanhui,et al.Prediction of coal body structure by waveform indication inversion based on prestack AVO data[J].Oil Geophysical Prospecting,2022,57(S1): 117-121.
Churlin V V,Sergeyev L A.Application of seismic surveying to recognition of productive part of gas-oil strata[J].Journal of Emergency Nursing Jen Official Publication of the Emergency Department Nurses Association,1963, 20(6): 497-504
[33]
徐伯勋,白爱萍,杨积凯.利用波形特征参数识别和预测储层含油性[J].石油物探,1995,34(3): 78-83.XU Baixun,BAI Aiping,YANG Jikai.The identification and prediction if oil-bearing beds through characteristic parameters of wave form[J].Geophysical Prospecting for Petroleum,1995,34(3): 78-83.
[34]
SIBILLE G,KESKES N,FONTAINE L.Enhancement of perception of seismic facies and sequences by image analysis techniques[C].SEG Technical Program Expanded Abstracts,1984,3: 477-480.
[35]
ZAHRAA A,ZAILANI A,GHOSH D P.Characte- rizing geological facies using seismic waveform classification in Sarawak basin[J].IOP Conference Series: Earth and Environmental Science,2017,88: 021001.
[36]
PRIEZZHEV I I,VEEKEN P C H,EGOROV S V,et al.Seismic waveform classification based on Kohonen 3D neural networks with RGB visualization[J].First Break,2019,37(2): 37-43.
[37]
郑和忠.基于支持向量机的地震波形分类方法的研究[D].山东青岛: 青岛大学,2018.
[38]
蔡涵鹏,胡浩炀,吴庆平,等.基于叠前地震纹理特征的半监督地震相分析[J].石油地球物理勘探,2020,55(3): 504-509.CAI Hanpeng,HU Haoyang,WU Qingping,et al.Semi-supervised seismic facies analysis based on pre-stack seismic texture[J].Oil Geophysical Prospecting,2020,55(3): 504-509.
[39]
高雪.基于波形库的波形结构特征对比方法探讨[D].四川成都: 成都理工大学,2013.
[40]
李鸿吉.模糊数学基础及实用算法[M].北京: 科学出版社,2005.LI Hongji.Fundamentals of Fuzzy Mathematics and Practical Algorithms[M].Science Press,Beijing,2005.
