多波联合属性提取及油气预测
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摘要
多波多分量地震勘探技术是油藏综合地球物理研究的一种重要的技术手段,其优势不仅仅是多波资料的相互验证,更体现在综合利用多波多分量地震数据直接提取与油气有关的联合属性,进行储层预测和油气预测。传统的地震属性提取大都是针对单道地震信号进行的,没有考虑储层的横向变化,而且传统的属性提取只是截取了地震波形的某一个或几个特征,没有全面的利用地震波形,为了满足油藏开发后期进一步提高采收率的需求,传统的属性提取技术需要进行改进来适应越来越复杂的油气勘探环境。
针对传统的属性提取没有考虑横向变化的不足,本文将常规Hilbert变换由单纯时间域变换扩展到地震剖面的二维时间-空间? t ,x ?域,对地震剖面进行二维解析信号的处理,即考虑地质结构的空间相变造成的地震剖面的横向变化来进行油气预测;结合油气对地震信号影响的分析以测井信息为约束,利用二维时频分析方法,寻找油气和水差异性最大的频段,称之为“有利频段”,提取有利频段内的地震多波属性,进而进行油气预测;依据该地区油气在多波地震资料上的表现特点,采用了的相对变化,即这个函数组合达到突出油气异常进而进行油气预测的目的。针对传统的地震属性提取没有充分利用地震波形的不足,结合地球物理勘探的“灰色”的本质通过灰色预测方法,直接利用地震波波形信息提取灰异常属性全面的开发地震波形所携带的地下储层的信息来进行油气预测。针对多波联合属性运用没有理论基础的不足,结合该地区目的层的岩石物理分析,采用具有物理意义的多波联合比值类属性进行油气预测。
将上述多波联合属性运用于胜利油田某薄互层区块多波实际资料,实际应用效果表明,通过横向信息的加入和全面波形信息的利用,对于平均砂体厚度只有十米的薄互层目的层,多波联合属性预测的效果无论是与构造图、小层含油面积图的吻合程度,还是与多井测井解释结果的对比验证都取得了不错的应用效果,验证了以上多波联合属性在该地区的可行性和实用性。
The multi-wave and multi-component seismic exploration is an important technology applied to the study of comprehensive reservoir geophysics. This new geophysical technology of multi-wave and multi-component can not only bring us the mutual verification of multi-wave information, but its deeper effect is integrated utilization of multi-wave and multi-component data to directly extract joint attributes related to hydrocarbon for reservoir prediction and hydrocarbon prediction. The traditional method of attributes extraction is only applied to one single seismic trace, without consideration of the transverse variation of reservoir, and just exacts one or several characteristics of seismic waveform. Therefore, it can not comprehensively exploit the seismic waveform. For the raised recovery in the latter period of oilfield development, we need to improve the traditional method for the more and more complicated and difficult exploration.
To solve the lack of transverse changes in conventional attributes extraction, we attend the conventional Hilbert transform from single time domain to time-spatial domain make 2-D analytical signal measures applied to seismic data, and contain the lateral variation of seismic data resulted from spatial change of sedimentary facies of geological structure for hydrocarbon prediction. Combined with the influence of hydrocarbon to seismic signal and logging information, we use 2-D time frequency analysis to find a certain frequency band in which the difference between hydrocarbon and water is the biggest, named“the favorable frequency band”, and then extract multi-wave seismic attributes from the seismic data in the favorable frequency band for hydrocarbon prediction. Based on the reflection feature of hydrocarbon on practical multi-wave data, we introduced the relative change of R p Rps, i.e., to outburst the hydrocarbon anomaly for hydrocarbon prediction. For the comprehensive use of seismic waveform, combined with grey inherent of geophysical exploration, we adopt the method of extracting the grey abnormal attribute from the seismic waveform directly to comprehensively exploit the waveform information for the purpose of hydrocarbon prediction. To solve the lack of theoretical foundation of multi-wave combination, based on the petrophysical analysis of target zone, we use multi-wave joint ratio attributes with physical significance for hydrocarbon prediction.
Applying the above-mentioned multi-wave joint attributes to the practical multi-wave data of some interbedded thin layers zone in Shengli Oilfield, the actual data processing results show that the prediction of multi-wave joint attributes not only inosculates the structural map and oil area map of substratum, but also corresponds well with logging interpretation to the target zone of interbedded thin layers of only ten meters thick with the help of lateral information and comprehensive utilization of seismic waveform. Therefore, it verifies the feasibility and validity of the multi-wave joint attributes.
针对传统的属性提取没有考虑横向变化的不足,本文将常规Hilbert变换由单纯时间域变换扩展到地震剖面的二维时间-空间? t ,x ?域,对地震剖面进行二维解析信号的处理,即考虑地质结构的空间相变造成的地震剖面的横向变化来进行油气预测;结合油气对地震信号影响的分析以测井信息为约束,利用二维时频分析方法,寻找油气和水差异性最大的频段,称之为“有利频段”,提取有利频段内的地震多波属性,进而进行油气预测;依据该地区油气在多波地震资料上的表现特点,采用了的相对变化,即这个函数组合达到突出油气异常进而进行油气预测的目的。针对传统的地震属性提取没有充分利用地震波形的不足,结合地球物理勘探的“灰色”的本质通过灰色预测方法,直接利用地震波波形信息提取灰异常属性全面的开发地震波形所携带的地下储层的信息来进行油气预测。针对多波联合属性运用没有理论基础的不足,结合该地区目的层的岩石物理分析,采用具有物理意义的多波联合比值类属性进行油气预测。
将上述多波联合属性运用于胜利油田某薄互层区块多波实际资料,实际应用效果表明,通过横向信息的加入和全面波形信息的利用,对于平均砂体厚度只有十米的薄互层目的层,多波联合属性预测的效果无论是与构造图、小层含油面积图的吻合程度,还是与多井测井解释结果的对比验证都取得了不错的应用效果,验证了以上多波联合属性在该地区的可行性和实用性。
The multi-wave and multi-component seismic exploration is an important technology applied to the study of comprehensive reservoir geophysics. This new geophysical technology of multi-wave and multi-component can not only bring us the mutual verification of multi-wave information, but its deeper effect is integrated utilization of multi-wave and multi-component data to directly extract joint attributes related to hydrocarbon for reservoir prediction and hydrocarbon prediction. The traditional method of attributes extraction is only applied to one single seismic trace, without consideration of the transverse variation of reservoir, and just exacts one or several characteristics of seismic waveform. Therefore, it can not comprehensively exploit the seismic waveform. For the raised recovery in the latter period of oilfield development, we need to improve the traditional method for the more and more complicated and difficult exploration.
To solve the lack of transverse changes in conventional attributes extraction, we attend the conventional Hilbert transform from single time domain to time-spatial domain make 2-D analytical signal measures applied to seismic data, and contain the lateral variation of seismic data resulted from spatial change of sedimentary facies of geological structure for hydrocarbon prediction. Combined with the influence of hydrocarbon to seismic signal and logging information, we use 2-D time frequency analysis to find a certain frequency band in which the difference between hydrocarbon and water is the biggest, named“the favorable frequency band”, and then extract multi-wave seismic attributes from the seismic data in the favorable frequency band for hydrocarbon prediction. Based on the reflection feature of hydrocarbon on practical multi-wave data, we introduced the relative change of R p Rps, i.e., to outburst the hydrocarbon anomaly for hydrocarbon prediction. For the comprehensive use of seismic waveform, combined with grey inherent of geophysical exploration, we adopt the method of extracting the grey abnormal attribute from the seismic waveform directly to comprehensively exploit the waveform information for the purpose of hydrocarbon prediction. To solve the lack of theoretical foundation of multi-wave combination, based on the petrophysical analysis of target zone, we use multi-wave joint ratio attributes with physical significance for hydrocarbon prediction.
Applying the above-mentioned multi-wave joint attributes to the practical multi-wave data of some interbedded thin layers zone in Shengli Oilfield, the actual data processing results show that the prediction of multi-wave joint attributes not only inosculates the structural map and oil area map of substratum, but also corresponds well with logging interpretation to the target zone of interbedded thin layers of only ten meters thick with the help of lateral information and comprehensive utilization of seismic waveform. Therefore, it verifies the feasibility and validity of the multi-wave joint attributes.
引文
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[3]朱广生.地震资料储层预测方法[M] .石油工业出版社,1995: 30-110
[4] Barnes A. E. Theory of 2D Complex Seismic trace analysis[J]. Geophysics, 1996,V61(8):264-272
[5] Bulow T. Hypercomplex spectral signal representations for the processing and analysis of images[D]. Kiel:University of Ckristian,1999
[6] Hahn S. L. Multidimensional complex signals with single-orthant spectra[J]. Proc IEEE, 1992,V80(8):1287–1300
[7] Grunland G. H. Signal processing for computer version[M] .Kluwer academic publishers,1995:134-199
[8] Hahn S. L. Hilbert transform in signal processing[M] .Artech house,1996:29-63
[9] Bulow T. Quaternionic Gabor filters for local structure classification[C]. 14th International Conference on Pattern Recognition,1998,808-810
[10] Nicolas L. B, Jerome M. New 2D attributes based on complex and hypercomplex analytical signal[C]. SEG Expanded Abstracts,2001,532-661
[11]刘百红,郑四连,任鹏.时频分析技术及其在储层预测中的应用[J] .勘探地球物理进展, 2008,31(3):219-224
[12] Morlet, Arens, Giard. Wave propagation and sampling theory-Part Two: sampling theory and complex wave[J]. Geophysics, 1982, V47: 222-236
[13] Stockwell , Mansinha, Lowe. Localization of the complex spectrun:the S transform[J]. IEEE transactions on signal processing, 1996, V44(4):998-1001
[14] Partyka, Lopez. Interpretational applications of spectral decompositon in reservoir characterization[J].The leading edge, 1999, V18:353-360
[15] Marfurt, Kirlin. Narrow-band spectral analysis and thin-bed tunning[J]. Geophysics, 2001, V66: 1274-1283
[16] Sinha, Routh, Castagna. Spectral decomposition of seismic data with continuous-wavelet transform[J]. Geophysics, 2005, V70: 19-25
[17] Taner, Treitel. Harmonic attributes[C]. SEG Expanded Abstracts, 2004
[18] Matos, Mundim, Moraces. Characterization of thin beds through joint time-frequency analysis applied to a turbidite reservoir in Campos Basin[C]. SEG Expanded Abstracts, 2005
[19] Odebeatu, Champman. Application of spectral decompositon to detection of dispersion of anomalies associated with gas saturation[J].The leading edge, 2006, V25:206-210
[20] Liu J.L. Spectral decomposition and its application in mapping stratigraphy and hydrocarbons[D]. Houston: University of Houston, 2006
[21]李庆忠.走向精确勘探的道路[M].石油工业出版社, 1994,5
[22]黄绪德.反褶积与地震道反演[M].石油工业出版社, 1992,11
[23]刘财,陈业全,刘洋.勘探地震资料处理新方法及新技术[M].北京:科学出版社, 2006
[24] Fatti, Vail, Smith. Detection of gas in sandstone reservoirs using AVO analysis: A 3-D seismic case history using the Geostack technique[J]. Geophysics, 1994, V59(4) :1362-1376
[25] Smith, Sutherland. The fluid factor as an AVO indicator[J]. Geophysics, 1996, V61(5): 1425-1428
[26] Goodway, Downton. Improved AVO fluid detection and lithology discrimination using lame petrophysical parameters from P and S inversion[C]. Expanded Abstracts of 68th Annual SEG meeting, 1997:183-186
[27] Russel, Hedlin. Fluid property discrimination with AVO: a Biot-Gassmann perspective[J]. Geophysics, 2003, V68(1):29-39
[28] Eissa, Castagna. Case study: AVO analysis in a high-impedence[J] The Leading Edge, 2003, V22(10): 988-997
[29] Dillon, Guilherme. A multiscale DHI elastic attributes evaluation[J]. The Leading Edge, 2003, V22(10):1024-1029
[30]宁忠华,贺振华,黄德济.基于地震资料的高灵敏度流体识别因子[J].石油物探, 2006, 45(3): 239-241
[31]孙翠娟,张文,张旗.利用纵横波反射系数差检测含油气性方法及其应用效果[J].中国海上油气, 2007, 19(1): 13-17
[32]刘思峰,谢乃明.灰色系统理论及其应用[M].科学出版社, 2008,12
[33]曹辉.应用GM(1,1)模型分析反射波振幅与频率特征[J].石油地球物理勘探, 1995,30 (1): 91-99
[34]施冬,张春生,郭甲世.灰色综合评判法在低阻油气层中的应用[J].地球物理学进展, 1998, 13(1) :73-78
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