基于弹性参数加权统计的储层物性参数反演方法（英文）
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摘要
储层物性参数的改变伴随着弹性参数某种程度的改变,而这种改变并非简单的线性关系。加上观测信息的缺失、重叠、噪声破坏及模型理想化等原因,致使这种改变关系的获取具有较大的不确定性。针对传统统计岩石物理物性参数反演方法的不足,以利用弹性参数反演储层物性参数为目的,依据贝叶斯反演框架,我们建立了新的储层物性参数目标反演函数。首先,采用兼具确定性与随机性特点的统计岩石物理模型,考虑到不同弹性参数间的精度存在差异,引入权重系数,建立起储层物性参数与弹性参数间的加权统计关系。其次,基于这种加权统计关系,结合马尔科夫链蒙特卡洛随机模拟技术产生储层物性参数、弹性参数随机联合样本空间作为目标函数求解样本空间。最后,建立解的快速求解准则,求取最大后验概率密度对应的储层物性参数取值作为最终解。实际应用表明,该方法具有较高的反演效率,应用前景较好。
Variation of reservoir physical properties can cause changes in its elastic parameters.However,this is not a simple linear relation.Furthermore,the lack of observations,data overlap,noise interference,and idealized models increases the uncertainties of the inversion result.Thus,we propose an inversion method that is different from traditional statistical rock physics modeling.First,we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters.Second,based on the weighted statistical relation,we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function.Finally,we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters.The method has high efficiency and application potential.
Variation of reservoir physical properties can cause changes in its elastic parameters.However,this is not a simple linear relation.Furthermore,the lack of observations,data overlap,noise interference,and idealized models increases the uncertainties of the inversion result.Thus,we propose an inversion method that is different from traditional statistical rock physics modeling.First,we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters.Second,based on the weighted statistical relation,we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function.Finally,we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters.The method has high efficiency and application potential.
引文
Bachrach,R.,2006,Joint estimation of porosity and saturation using stochastic rock-physics modeling:Geophysics,71(5),053-063.
Blangy,J.P.,1992,Integrated seismic lithologic interpretation:The petrophysical basis:Ph.D.thesis,Stanford University.
Doyen,P.M.,1988,Porosity from seismic data:A geostatistical approach:Geophysics,53,1263-1275.
Fan,J.J.,and Liu,P.,2008,Research on Naive Bayesian Classifier's independence assumption:Computer Engineering and Applications(in Chinese),44,131-141.
Fournier,F.,1989,Extraction of quantitative geologic information from seismic data with multidimensional statistical analysis:Part I,methodology,and Part II,a case study:59th Annual International Meeting,SEG,Expanded Abstracts,726-733.
Friedman,N.,Geiger,D.,and Goldszmidt,M.,1997,Bayesian Network Classifiers:Machine Learning,29,131-163.
Grana,D.,and Rossa,E.D.,2010,Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion:Geophysics,75(3),021-037.
Hastie,T.,Tibshirani,R.,and Friedman,J.,2002,The elements of statistical learning:Springer.
Marion,D.,and Jizba,D.,1997,Acoustic properties of carbonate rocks:Use in quantitative interpretation of sonic and seismic measurements,in I.Palaz,and K.J.Marfurt,eds.,Carbonate Seismology:Geophysical Developments,SEG,75-93.
Mavko,G.,Mukerji,T.,and Dvorkin,J.,2003,The rock physics handbook:Cambridge University Press,UK,150-151.
Kabir,N.,Lavaud,B.,and Chavent G.,2000,Estimation of the density contrast by AVO inversion beyond the linearized approximation:an indicator of gas saturation.70th Annual International Meeting,SEG,Expanded Abstracts,243-246.
Russell,B.EL,Gray,D.,and Hampson,D.P.,2011,Linearized AVO and poroelasticity:Geophysics,76(3),C19-C29.
Spikes,K.,Mukerji,T.,Dvorkin,J.,and Mavko,G.,2008,Probabilistic seismic inversion based on rock-physics models:Geophysics,72(5),R87-R97.
Tarantola,A.,2005,Inverse problem theory and methods for model parameter estimation:Society for Industrial and Applied Mathematics Press,USA,20.
Yin,X.Y.,Sun,R.Y,Wang,B.L.,and Zhang,G.Z.,2014,Simultaneous inversion of petrophysical parameters based on geostatistical a priori information:Applied Geophysics,11(3),311-320.
Blangy,J.P.,1992,Integrated seismic lithologic interpretation:The petrophysical basis:Ph.D.thesis,Stanford University.
Doyen,P.M.,1988,Porosity from seismic data:A geostatistical approach:Geophysics,53,1263-1275.
Fan,J.J.,and Liu,P.,2008,Research on Naive Bayesian Classifier's independence assumption:Computer Engineering and Applications(in Chinese),44,131-141.
Fournier,F.,1989,Extraction of quantitative geologic information from seismic data with multidimensional statistical analysis:Part I,methodology,and Part II,a case study:59th Annual International Meeting,SEG,Expanded Abstracts,726-733.
Friedman,N.,Geiger,D.,and Goldszmidt,M.,1997,Bayesian Network Classifiers:Machine Learning,29,131-163.
Grana,D.,and Rossa,E.D.,2010,Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion:Geophysics,75(3),021-037.
Hastie,T.,Tibshirani,R.,and Friedman,J.,2002,The elements of statistical learning:Springer.
Marion,D.,and Jizba,D.,1997,Acoustic properties of carbonate rocks:Use in quantitative interpretation of sonic and seismic measurements,in I.Palaz,and K.J.Marfurt,eds.,Carbonate Seismology:Geophysical Developments,SEG,75-93.
Mavko,G.,Mukerji,T.,and Dvorkin,J.,2003,The rock physics handbook:Cambridge University Press,UK,150-151.
Kabir,N.,Lavaud,B.,and Chavent G.,2000,Estimation of the density contrast by AVO inversion beyond the linearized approximation:an indicator of gas saturation.70th Annual International Meeting,SEG,Expanded Abstracts,243-246.
Russell,B.EL,Gray,D.,and Hampson,D.P.,2011,Linearized AVO and poroelasticity:Geophysics,76(3),C19-C29.
Spikes,K.,Mukerji,T.,Dvorkin,J.,and Mavko,G.,2008,Probabilistic seismic inversion based on rock-physics models:Geophysics,72(5),R87-R97.
Tarantola,A.,2005,Inverse problem theory and methods for model parameter estimation:Society for Industrial and Applied Mathematics Press,USA,20.
Yin,X.Y.,Sun,R.Y,Wang,B.L.,and Zhang,G.Z.,2014,Simultaneous inversion of petrophysical parameters based on geostatistical a priori information:Applied Geophysics,11(3),311-320.