基于分形高频初始模型和低频先验信息的物性参数随机反演
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摘要
基于分形高频初始模型和低频先验信息的储层物性参数随机反演方法综合利用混合先验信息和统计岩石物理模型,在贝叶斯理论框架下直接反演储层物性参数。首先通过分形高斯模型算法和克里金插值得到物性参数的先验信息;然后根据统计岩石物理模型建立弹性参数与储层物性参数之间的关系,构建似然函数;最终利用基于模拟退火法(Simulated Annealing,SA)改进的粒子群优化(Particle Swarm Optimization,PSO)算法(SAPSO)实现后验概率密度的抽样。与确定性反演结果相比,该方法能够有效地融合测井资料中的高频信息,提高反演结果的分辨率,并且先验信息中融合了低频成分,可以得到宽频带的反演结果。一维和二维实际资料反演得到的孔隙度、泥质含量和含水饱和度与井资料吻合很好,分辨率较高,验证了该方法的可行性。
Stochastic inversion of reservoir physical property parameters based on high-frequency initial model from fractal and low-frequency prior information is direct inversion of reservoir parameters utilizing mixed prior information and statistical petrophysical model in a Bayesian framework.Firstly,we can get the prior information of reservoir physical parameters through fractal Gaussian model algorithm and Kriging interpolation.Then according to the statistical petrophysical model,we get the relationship between elastic parameters and reservoir physical parameters and build a likelihood function.Finally we apply SA-PSO optimization algorithm in order to obtain the sampling of the posterior probability density.Compared with deterministic inversion,the method we proposed can effectively integrate high-frequency information in logging data to improve the resolution of inversion results.And prior information contains low-frequency components,so the inversion results are of broadband.Porosity,clay content and water saturation from inversion of 1Dand 2Dactural data have a good agreement with the well data and higher resolution,verify the feasibility of this method.
Stochastic inversion of reservoir physical property parameters based on high-frequency initial model from fractal and low-frequency prior information is direct inversion of reservoir parameters utilizing mixed prior information and statistical petrophysical model in a Bayesian framework.Firstly,we can get the prior information of reservoir physical parameters through fractal Gaussian model algorithm and Kriging interpolation.Then according to the statistical petrophysical model,we get the relationship between elastic parameters and reservoir physical parameters and build a likelihood function.Finally we apply SA-PSO optimization algorithm in order to obtain the sampling of the posterior probability density.Compared with deterministic inversion,the method we proposed can effectively integrate high-frequency information in logging data to improve the resolution of inversion results.And prior information contains low-frequency components,so the inversion results are of broadband.Porosity,clay content and water saturation from inversion of 1Dand 2Dactural data have a good agreement with the well data and higher resolution,verify the feasibility of this method.
引文
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[17]朱丽莉,杨志鹏,袁华.粒子群优化算法分析及研究进展[J].计算机工程与应用,2007,43(5):24-27.Zhu L L,Yang Z P,Yuan H.Analysis and development of particle swarm optimization[J].Computer Engineering and Applications,2007,43(5):24-27
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[19]Metropolis N,Rosenbluth A,Rosenbluth M,et a1.Equation of state calculations by fast computing machines[J].The Journal of Chemical Physics,1953,21(6):1087-1092
[20]Rothman D H.Nonlinear inversion statistical mechanics and residual statics estimation[J].Geophysics,1985,50(12):2784-2796
[21]Rothman D H.Automatic estimation of large residual statics correction[J].Geophysics,1986,51(2):337-346
[2]Tao Y,Spikes K,Sen M K.Stochastic seismic inversion using both fractal and low-frequency priors[J].Expanded Abstracts of 81st Annual Internat SEG Mtg,2011,2732-2735
[3]Mukerji T,JΦrstad A,Avseth P,et al.Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir:seismic inversions and statistical rock physics[J].Geophysics,2001,66(4):988-1001
[4]Mukerji T,Avseth P,Mavko G,et al.Statistical rock physics:combining rock physics,information theory,and geostatistics to reduce uncertainty in seismic reservoir characterization[J].The Leading Edge,2001,20(3):313-319
[5]Eidsvik J,Avseth P,Omre H,et al.Stochastic reservoir characterization using prestack seismic data[J].Geophysics,2004,69(4):978-993
[6]Spikes K,Mukerji T,Dvorkin J,et al.Probabilistic seismic inversion based on rock-physics models[J].Geophysics,2007,72(5):R87-R97
[7]Grana D,Della Rossa E.Probabilistic petrophysicalproperties estimation integrating statistical rock physics with seismic inversion[J].Geophysics,2010,75(3):O21-O37
[8]Grana D,Dvorkin J.The link between seismic inversion,rock physics,and geostatistical simulations in seismic reservoir characterization studies[J].The Leading Edge,2011,30(1):54-61
[9]胡华锋,印兴耀,吴国忱.基于贝叶斯分类的储层物性参数联合反演方法[J].石油物探,2012,51(3):225-232Hu H F,Yin X Y,Wu G C.Joint inversion of petrophysical parameters based on Bayesian classification[J].Geophysical Prospecting for Petroleum,2012,51(3):225-232
[10]夏丽娜.基于贝叶斯理论框架的储层参数地震反演方法研究[D].青岛:中国石油大学(华东),2013Xia L N.The study of petro-physical properties seismic inversion based on Bayesian theory[D].Qingdao:China University of Petroleum(Huadong),2013
[11]KjΦnsberg H,Hauge R,Kolbjrnsen O,et al.Bayesian Monte Carlo method for seismic predrill prospect assessment[J].Geophysics,2010,75(2):O9-O19
[12]李宏兵,张佳佳,姚逢昌.岩石的等效孔隙纵横比反演及其应用[J].地球物理学报,2013,56(2):608-615Li H B,Zhang J J,Yao F C.Inversion of effective pore aspect ratios for porous and its applications[J].Chinese Journal of Geophysics,2013,56(2):608-615
[13]Scales J A,Smith M L,Treitel S.Introductory geophysical inverse theory[M].Colorado:Samizdat Press,2001:1-193
[14]Hewett T A.Fractal distributions of reservoir heterogeneity and their influence on fluid transport[J].Expanded Abstracts of 61st SPE Annual Conference,1986,15386
[15]韩忠玲.分形高斯噪声Hurst参数估计的实验评价[D].上海:华东师范大学,2008Han Z L.Experimental evaluation on methods of the Hurst parameter estimation using fractional Gausssian noise[D].Shanghai:East China Normal University,2008
[16]Kennedy J,Eberhart R.Particle swarm optimization[C]∥Proceedings of IEEE international conference on neural networks.Neural Networks:Institute of Electrical and Electronics Engineers,1995:1942-1948
[17]朱丽莉,杨志鹏,袁华.粒子群优化算法分析及研究进展[J].计算机工程与应用,2007,43(5):24-27.Zhu L L,Yang Z P,Yuan H.Analysis and development of particle swarm optimization[J].Computer Engineering and Applications,2007,43(5):24-27
[18]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,40(1):47-50Gau Y,Xie S L.Particle swarm optimization algorithms based on simulated annealing[J].Computer Engineering and Applications,2004,40(1):47-50
[19]Metropolis N,Rosenbluth A,Rosenbluth M,et a1.Equation of state calculations by fast computing machines[J].The Journal of Chemical Physics,1953,21(6):1087-1092
[20]Rothman D H.Nonlinear inversion statistical mechanics and residual statics estimation[J].Geophysics,1985,50(12):2784-2796
[21]Rothman D H.Automatic estimation of large residual statics correction[J].Geophysics,1986,51(2):337-346