预测油藏岩石物性及不确定性分析研究
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摘要
预测油藏岩石物性的方法论是存在的。这个方法论的目的是为预测油藏属性提供一个全面的方案,其中包括估计和不确定性分析。这个方法从以下两个方面进行:(1)不确定性分析。(2)用一个直接的方法,综合多中地球物理学数据集,岩石物理分析信息和先验信息。这个直接的方法利用了贝叶斯定理。在这篇论文中,主要目的是解决孔隙度估计问题。信息来源是叠前地震数据,测井数据和岩心样本。波形弹性叠前反演被包含在这个方法中,目的是从叠前地震数据中得到多孔介质物理属性信息。地质统计学模型被包含在这个方法中,目的是从测井数据中得到多孔介质空间变异信息。应用这个方法,需要把一个油藏分成许多个单元格。对每一个油藏单元格,一个后验概率密度函数被计算出来。最后,两个数据体包含了最终的结果。
A methodology of forecasting reservoir petrophysical characteristics was presented.The goal of this methodology was to provide a complete solution for determination of reservoir properties,including estimation and uncertainty analysis.The strengths of this reservoir characterization methodology were as follows:(a) uncertainty analysis;(b) integration of multiple geophysical data-sets,rock physics analysis and prior information in a straightforward way provided by the Bayesian framework.The inference problem reported in this paper was formulated to solve the problem of porosity estimation.The sources of information were pre-stack seismic data,well log data and core samples.The waveform elastic pre-stack inversion was incorporated in this methodology to access the porous medium physical property information from pre-stack seismic data.Geostatistical modeling was incorporated to access the porous medium spatial variability information from well-log data.The methodology was implemented considering a reservoir composed of block cells.One posterior PDF was computed for each reservoir cell.Two cell volumes contained the final result.
A methodology of forecasting reservoir petrophysical characteristics was presented.The goal of this methodology was to provide a complete solution for determination of reservoir properties,including estimation and uncertainty analysis.The strengths of this reservoir characterization methodology were as follows:(a) uncertainty analysis;(b) integration of multiple geophysical data-sets,rock physics analysis and prior information in a straightforward way provided by the Bayesian framework.The inference problem reported in this paper was formulated to solve the problem of porosity estimation.The sources of information were pre-stack seismic data,well log data and core samples.The waveform elastic pre-stack inversion was incorporated in this methodology to access the porous medium physical property information from pre-stack seismic data.Geostatistical modeling was incorporated to access the porous medium spatial variability information from well-log data.The methodology was implemented considering a reservoir composed of block cells.One posterior PDF was computed for each reservoir cell.Two cell volumes contained the final result.
引文
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[3]Muller,G.,1985,The reflectivity method:a tutorial:Journal of Geophysics,58,153-174.
[4]Zellner,A.,1996.An Introduction to Bayesian Inference to Econometrics,Willey Intercience.
[5]王家华,张团峰.油气储层随机建模.北京:石油工业出版社[M]2001.
[6]刘芳.从地震资料到储层特征的地质统计学反演[J].国外油气勘探,2000.
[7]陆基孟.地震勘探原理[M].石油大学出版社,2005.
[8]Torres-Verdín C,Grijalba-Cuenca A,Debeye H.A com-parison between geostatistical inversion and conventional geostatistical simulation practices for reservoir delineation[C]//AAPG Hedberg Research Conference on Applied Reservoir Characterization UsingGeostatistics.Woodlands,2005,3216.
[2]Jeffreys,H.,1936,Theory of Probability:Oxford University Press,London.Han,D.H.,Nur,A.and Morgan,D.,1986,Effects of porosity and clay content in wave velocities in sandstones.Geophysics,51,2093 2107.
[3]Muller,G.,1985,The reflectivity method:a tutorial:Journal of Geophysics,58,153-174.
[4]Zellner,A.,1996.An Introduction to Bayesian Inference to Econometrics,Willey Intercience.
[5]王家华,张团峰.油气储层随机建模.北京:石油工业出版社[M]2001.
[6]刘芳.从地震资料到储层特征的地质统计学反演[J].国外油气勘探,2000.
[7]陆基孟.地震勘探原理[M].石油大学出版社,2005.
[8]Torres-Verdín C,Grijalba-Cuenca A,Debeye H.A com-parison between geostatistical inversion and conventional geostatistical simulation practices for reservoir delineation[C]//AAPG Hedberg Research Conference on Applied Reservoir Characterization UsingGeostatistics.Woodlands,2005,3216.
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