基于非线性能量分析的鲕滩储层预测方法
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摘要
Teager-Kaiser非线性能量(T-K能量)可以归为振幅类地震属性,但有别于常规的振幅类地震属性,它与振幅的平方、频率的平方成正比。鲕滩储层中流体为油气时地震记录往往呈现"高频衰减"现象,由于T-K能量与频率平方成正比,因此含油气鲕滩储层内部T-K能量弱。鲕滩储层与围岩的物性差别使储层顶和底边界形成"亮点"反射,相应的T-K能量高,T-K属性剖面背景越发"明亮",而内部相对较"暗",利用这些特征可以有效地进行鲕滩储层预测,实际资料的计算结果表明,基于非线性能量分析的鲕滩储层预测方法得到的鲕滩储层分布与实钻完全一致。
Non-linear Teager-Kaiser energy (T-K energy) can be classified as the amplitude of the seismic attributes,but it is different from the conventional seismic attributes,because it is proportional to the square of amplitude and frequency.When the oolitic reservoir is full of oil or gas,seismic records often present "high-frequency attenuation" phenomenon.As T-K energy is proportional to the square of frequency,T-K energy of the reservoir which contains oil or gas is weak.Since the oolitic reservoir's top and bottom is different from the surrounding in rock properties,"bright spot" reflection often presents on top and bottom of the reservoir,so T-K energy of the top and bottom of the reservoir is stronger.With seismic profile after extraction of seismic attribute of T-K energy,the top and bottom of the reservoir on the seismic profile is much "brighter" ,while T-K energy is relatively weak in the internal reservoir.These characteristics can be used to predict the oolitic reservoir.The computational result of the real data shows that the predicted distribution of oolitic reservoir based on TK energy method is completely consistent with the real work data.
Non-linear Teager-Kaiser energy (T-K energy) can be classified as the amplitude of the seismic attributes,but it is different from the conventional seismic attributes,because it is proportional to the square of amplitude and frequency.When the oolitic reservoir is full of oil or gas,seismic records often present "high-frequency attenuation" phenomenon.As T-K energy is proportional to the square of frequency,T-K energy of the reservoir which contains oil or gas is weak.Since the oolitic reservoir's top and bottom is different from the surrounding in rock properties,"bright spot" reflection often presents on top and bottom of the reservoir,so T-K energy of the top and bottom of the reservoir is stronger.With seismic profile after extraction of seismic attribute of T-K energy,the top and bottom of the reservoir on the seismic profile is much "brighter" ,while T-K energy is relatively weak in the internal reservoir.These characteristics can be used to predict the oolitic reservoir.The computational result of the real data shows that the predicted distribution of oolitic reservoir based on TK energy method is completely consistent with the real work data.
引文
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[4]Marfurt.Instantaneous spectral attributes to detect chan-nels[J].Geophysics,2007,72(2):375-378.
[5]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(1):120-127.
[6]Zhang Zhiyi.Reconstruction of conductivity and suscepti-bility from the inversion of EMdata[D].Vancouver:Uni-versity of British Columbia,1997.
[7]Guthe S,Wand M,Gonser J,et al.Interactive rendering of large volume data sets[C].Boston:Visualization2002conference,IEEE,2002.
[8]刘微,曾云贤,杨雨.飞仙关组鲕滩储层常规地震相识别技术[J].西南石油大学学报,2007,29(1):30-33.
[9]龚洪林,王振卿,蔡刚.分频解释技术在碳酸盐岩储层预测中应用[J].西南石油大学学报,2007,29(S1):5-8.
[10]魏嘉,朱文斌,朱海龙,等.地震沉积学———地震解释新思路及沉积研究的新工具[J].勘探地球物理进展,2008,31(2):95-101.
[11]郭泽清,钟建华,刘卫红,等.柴西第三纪湖相生物礁储层特征及意义[J].沉积学报,2004,22(3):425-430.
[12]高亚召,刘霞.基于Teager能量算子的自适应小波语音增强[J].语音技术,2009,33(1):59-61.
[13]李辉,郑海起.基于EMD和Teager能量算子的轴承故障诊断研究[J].振动与冲击,2008,27(10):16-18.
[14]Kaiser J F.On a simple algorithm to calculate the energy of a signal[J].IEEE,1990(2):381-384.
[15]Partyka G,Gridley J,Lopez J.Interpretational applica-tions of spectral decomposition in reservoir characterza-tion[J].The Leading Edge,1999,18(3):353-360.
[16]Castagna J,Sun S.Comparison of spectral decomposition methods[J].First Break,2006,24(3):75-79.
[17]韩纪庆.语音信号处理[M].北京:清华大学出版社,2004.
[18]陈永武.储集层与油气分布[M].北京:石油工业出版社,1995.
[19]张凤生,司马立强,赵辉.狮子沟地区裂缝识别和参数计算方法研究[J].西南石油大学学报,2007,29(S2):16-19.
[20]黄绪德.薄层陷频法[J].勘探地球物理进展,2002,25(5):193-196.
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