自适应最优核时频分布在地震储层预测中的应用
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摘要
提高时频分辨率是频谱成像技术研究的重点。自适应最优核时频分布采用随信号特征自适应变化的核函数,在模糊域对远离原点的互分量进行抑制,并尽可能的保留集中在原点附近的自分量。通过理论模型验证,该方法较好地抑制了交叉项干扰,同时较连续小波变换和平滑伪Wigner-Ville分布等方法具有更高时频分辨率。最后在营尔凹陷长沙岭地区的实例应用中,利用自适应最优核时频分布对该区目标储层进行了频谱成像处理,并结合沉积相特征对长沙岭地区进行有利区带预测。结果表明:该方法适用于实际地震信号的时频分析,对储层刻画优于传统方法,且对研究区储层预测具有有效性。
To improve resolution is the key point of spectral imaging.Adaptive optimal kernel time-frequency representation used a kernel function which can change adaptively with signal characteristics to weight the ambiguity function.The cross-components located away from the origin of the ambiguity plane are suppressed,and the auto-components centered at the origin are passed.Adaptive optimal kernel time-frequency representation decreases the impact of cross terms obviously and obtains a better time-frequency resolution than the methods as CWT and SPWVD,which is proved by the model data.Based on adaptive optimal kernel time-frequency representation,the seismic data of the target layer in Changshaling area of Ying'er Sag are processed by spectral imaging.On the basis of spectral imaging results and sedimentary environment,a prediction of favorable zones in Changshaling area was made.The result shows that this method is not only suitable for the time-frequency analysis of seismic signal and excels the traditional methods in reservoir description,but also takes an effectiveness in reservoir prediction.
To improve resolution is the key point of spectral imaging.Adaptive optimal kernel time-frequency representation used a kernel function which can change adaptively with signal characteristics to weight the ambiguity function.The cross-components located away from the origin of the ambiguity plane are suppressed,and the auto-components centered at the origin are passed.Adaptive optimal kernel time-frequency representation decreases the impact of cross terms obviously and obtains a better time-frequency resolution than the methods as CWT and SPWVD,which is proved by the model data.Based on adaptive optimal kernel time-frequency representation,the seismic data of the target layer in Changshaling area of Ying'er Sag are processed by spectral imaging.On the basis of spectral imaging results and sedimentary environment,a prediction of favorable zones in Changshaling area was made.The result shows that this method is not only suitable for the time-frequency analysis of seismic signal and excels the traditional methods in reservoir description,but also takes an effectiveness in reservoir prediction.
引文
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[18]Leadholm R H,Austin T J F,Ostendorf P F.Using spectral decomposition as a hydrocarbon indicator tool in the Norwegian Sea[J].Petroleum Geology Conference Series,2005,6:1533-1542.
[2]Burnett M D,Castagna J P.Application of spectral decomposition to gas basins in Mexico[J].The Leading Edge,2003,18:1130-1134.
[3]Castagna J P,Sun S,Siegfried R W.Nstantaneous spectral analysis:Detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22:129-127.
[4]Sinha S,Routh P S,Anno P D,Castagna J P.Spectral decomposition of seismic data with continuous-wavelet transforms[J].Geophysics,2005,70:19-25.
[5]陈学华,贺振华,黄德济,等.时频域油气储层低频阴影检测[J].地球物理学报,2009,52(1):215-221.CHEN Xue-hua,HE Zhen-hua,HUANG De-ji,et al.Low frequency shadow detection of gas reservoirs in time-frequency domain[J].Chinese Journal of Geophysics,2009,52(1):215-221.
[6]Marianne R D,Matt R.Spectral decomposition-transform methods and fluid and reservoir prediction case[C]//.EAGE67th Conference&Exhibition,Madrid,Spain Presentation Paper,2005.
[7]蔡瑞.基于谱分解技术的缝洞型碳酸盐岩溶洞识别方法[J].石油勘探与开发,2005,32(2):82-85.CAI Rui.Carbonate cave identification by using a spectral decomposition technique[J].Petroleum Exploration AndDevelopment,2005,32(2):82-85.
[8]肖学,罗涛,柳运景,等.储层频谱成像技术在塔里木盆地阿北-顺北地区的应用[J].石油天然气学报,2006,28(3):261-263.XIAO Xue,LUO Tao,LIU Yun-jing,et al.Application of reservoir frequency-spectrum technique in Abei-Shunbei region[J].Journal of Oil and Gas Technology,2006,28(3):261-263.
[9]邹红星,周小波,李衍达.时频分析:回溯与前瞻[J].电子学报,2000,28(9):78-84.ZOU Hong-xing,ZHOU Xiao-bo,LI Yan-da.Which time-frequency analysis-a survey[J].Acta Eleactronica Sinica,2000,28(9):78-84.
[10]Chio H L,Williams W J.Improved time-frequency representation of multi-component signals using exponential kernels[J].IEEE Transaction on ASSP,1989,37(6):861-871.
[11]Cohen L.Time-frequency distributions-a review[J].IEEE Proceedings,1989,77(7):941-981.
[12]Baraniuk R G,Jones D L.Signal-dependent time-frequency analysis using a radially Gaussian kernel[J].Signal Processing,1993,32(3):263-284.
[13]Jones D L,Baraniuk R G.An adaptive optimal-kernel time-frequency representation[J].IEEE Trans Signal Processing,1995,43(10):2361-2371.
[14]任战利,刘池阳,张小会,等.酒东盆地热演化史与油气关系研究[J].沉积学报,2000,18(4):619-623.REN Zhan-li,LIU Chi-yang,ZHANG Xiao-hui,et al.Research on the relations between geothermal history and oil-gas generation in Jiudong basin[J].Acta Sedimentologica Sinica,2000,18(4):619-623.
[15]赵鹏,陈世加,李丽萍,等.酒东盆地营尔凹陷下白垩统油源对比[J].天然气地球科学,2006,17(2):192-195.ZHAO Peng,CHEN Shi-jia,LI Li-ping,et al.Down cretaceous oil sources comparative study of Ying'er depression in Jiudong basin[J].Natural Gas Geoscience,2006,17(2):192-195.
[16]韩永科,刘永昌,岳智忠,等.酒泉盆地营尔凹陷长沙岭构造带油气勘探前景[J].新疆石油地质,2003,24(6):490-493.HAN Yong-ke,LIU Yong-chang,YUE Zhi-zhong,et al.Prospect of petroleum exploration in Changshaling structural belt of Ying'er sag in Jiuquan basin[J].Xinjiang Petroleum Geology,2003,24(6):490-493.
[17]侯秀林,谷丽冰.酒泉盆地营尔凹陷下白垩统层序地层划分及沉积体系特征[J].现代地质,2009,23(5):809-815.HOU Xiu-lin,GU Li-bing.Stratigraphic sequence division and the characteristics of sedimentary system of lower cretaceous in Ying'er depression,Jiuquan basin[J].Geoscicence,2009,23(5):809-815.
[18]Leadholm R H,Austin T J F,Ostendorf P F.Using spectral decomposition as a hydrocarbon indicator tool in the Norwegian Sea[J].Petroleum Geology Conference Series,2005,6:1533-1542.
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