基于贝叶斯分类的储层物性参数联合反演方法
|
摘要
应用地球物理资料进行储层物性参数反演是储层预测及综合评价的重要步骤。基于贝叶斯分类的储层物性参数联合反演方法综合应用统计岩石物理模型和蒙特卡罗仿真模拟技术,在贝叶斯反演框架下,基于贝叶斯分类算法计算储层物性参数后验概率分布,实现多种储层物性参数的联合反演。该方法不需要进行复杂的模型初始化,而是通过统计岩石物理模型建立储层物性参数与岩石弹性参数之间的关系,进而与叠前地震反演相结合,不仅能模拟地球物理随机特性,还能解决常规物性参数反演方法对测井资料过度依赖的问题。海上某区块实际资料应用结果表明,该方法能为储层精细描述提供多种物性参数,并可对反演结果的误差进行定量评价。
Petrophysical parameters inversion based on geophysical data is an important step in reservoir prediction and comprehensive evaluation.We proposed a new petrophysical parameter inversion method based on Bayesian classification.This method involves multiple theories and techniques,such as statistical rock physical model,Monte Carlo simulation technology.The posterior probability distribution of petrophysical parameters is calculated based on Bayesian classification algorithm and can realize multiple petrophysical parameters joint inversion based on Bayesian inversion framework.This method does not need to carry out complex model initialization.It obtains the relationship between petrophysical parameters and elastic parameters through statistical rock physics model,and then combined with pre-stack seismic inversion.This method can not only simulate geophysical stochastic properties,but also can well solve the problem of excessive dependence on log data in conventional petrophysical parameters inversion methods.The practice shows that it can provide various petrophysical parameters for fine reservoir description and quantitatively evaluate the errors of inversion result.
Petrophysical parameters inversion based on geophysical data is an important step in reservoir prediction and comprehensive evaluation.We proposed a new petrophysical parameter inversion method based on Bayesian classification.This method involves multiple theories and techniques,such as statistical rock physical model,Monte Carlo simulation technology.The posterior probability distribution of petrophysical parameters is calculated based on Bayesian classification algorithm and can realize multiple petrophysical parameters joint inversion based on Bayesian inversion framework.This method does not need to carry out complex model initialization.It obtains the relationship between petrophysical parameters and elastic parameters through statistical rock physics model,and then combined with pre-stack seismic inversion.This method can not only simulate geophysical stochastic properties,but also can well solve the problem of excessive dependence on log data in conventional petrophysical parameters inversion methods.The practice shows that it can provide various petrophysical parameters for fine reservoir description and quantitatively evaluate the errors of inversion result.
引文
[1]魏友华,郭科,陈聆,等.信息融合在复杂油气储层物性参数综合研究中的应用[J].地球物理学进展,2008,23(1):153-156Wei Y H,Guo K,Chen L,et al.The application ofdata fusion in the research of physic parameters ofcomplicated oil-gas reservoir[J].Progress in Geo-physics,2008,23(1):153-156
[2]吴媚,符力耘,李维新.高分辨率非线性储层物性参数反演方法和应用[J].地球物理学报,2008,51(2):546-557Wu M,Fu L Y,Li W X.A high-resolution nonlin-ear inversion method of reservoir parameters and itsapplication to oil/gas exploration[J].Chinese Jour-nal of Geophysics,2008,51(2):546-557
[3]印兴耀,杨风丽,吴国忱.神经网络在CB油田储层预测和储层厚度计算中的应用[J].石油大学学报(自然科学版),1998,22(2):17-20Yin X Y,Yang F L,Wu G C.Application of neuralnetwork to predicting reservoir and calculatingthickness in CB oilfield[J].Journal of the Universi-ty of Petroleum,1998,22(2):17-20
[4]吴秋波,吴元,王允诚.混合学习法前向网络多属性储层参数预测[J].西南石油大学学报(自然科学版),2011,33(2):68-72Wu Q B,Wu Y,Wang Y C.Reservoir parameterprediction by multi-attribute based on hybrid learn-ing algorithm of feed-forward network[J].Journalof Southwest Petroleum University(Science&Technology Edition),2011,33(2):68-72
[5]宋维琪.应用地震属性与测井数据反演储层参数[J].勘探地球物理进展,2003,26(3):216-219Song W Q.Inversion of reservoir parameters withseismic attributes and logging data[J].2003,26(3):216-219
[6]刘百红,李建华,魏晓东,等.随机反演在储层预测中的应用[J].地球物理学进展,2009,24(2):581-589Liu B H,Li J H,Wei X D,et al.The applicationof stochastic seismic inversion in reservoir prediction[J].Progress in Geophysics,2009,24(2):581-589
[7]Spikes K,Mukerji T,Dvorkin J,et al.Probabilis-tic seismic inversion based on rock-physics models[J].Geophysics,2007,72(5):R87-R97
[8]Mukerji T,Avseth P,Mavko G,et al.Statisticalrock physics:Combining rock physics,informationtheory and geostatistics to reduce uncertainty in seis-mic reservoir characterization[J].The LeadingEdge,2001,20(3):313-319
[9]Gunning J,Michael E G.Detection of reservoirquality using Bayesian seismic inversion[J].Geo-physics,2007,72(3):R37-R49
[10]Ulvmoen M,Omre H.Improved resolution inbayesian lithology/fluid inversion from prestackseismic data and well observations:Part1-Methodol-ogy[J].Geophysics,2010,75(2):R21-R35
[11]胡华锋.基于叠前道集的储层参数反演方法研究[D].青岛:中国石油大学(华东),2011Hu H F.The study of petrophysical-properties in-version based on pre-stack seismic data[D].Qingd-ao:China University of Petroleum(EastChina),2011
[12]Grana D,Rossa E D.Probabilistic petrophysical-properties estimation integrating statistical rockphysics with seismic inversion[J].Geophysics,2010,75(3):O21-O37
[13]秦鑫.一种蒙特卡罗贝叶斯分类的改进方法[D].武汉:华中师范大学,2004Qin X.An improved method of Monte Carlo Bayes-ian classification[D].Wuhan:Central China NormalUniversity,2004
[14]Larsen A L,Ulvmoen M,Omre H,et al.Bayesianlithology/fluid prediction and simulation on the basisof a Markov-chain prior model[J].Geophysics,2006,71(5):R69-R78
[15]Berryman J G.Mixture theories for rock proper-ties:A handbook of physical constants[M].Liver-more:American Geophysical Union,1995:205-228
[16]Hashin Z,Shtrikman S.A variational approach tothe theory of effective magnetic permeability of mul-tiphase materials[J].Journal of Applied Physics,1962,33(10):3125-3131
[17]Gassmann F.Elastic waves through a packing ofspheres[J].Geophysics,1951,16(4):673-685
[18]Mavko G,Mukerji T,Dvorikin J.The rock physicsHandbook:Tools for seismic analysis in porousmedia[M].HeFei:University of Science and Techo-nology of China Press,2008:93-94
[2]吴媚,符力耘,李维新.高分辨率非线性储层物性参数反演方法和应用[J].地球物理学报,2008,51(2):546-557Wu M,Fu L Y,Li W X.A high-resolution nonlin-ear inversion method of reservoir parameters and itsapplication to oil/gas exploration[J].Chinese Jour-nal of Geophysics,2008,51(2):546-557
[3]印兴耀,杨风丽,吴国忱.神经网络在CB油田储层预测和储层厚度计算中的应用[J].石油大学学报(自然科学版),1998,22(2):17-20Yin X Y,Yang F L,Wu G C.Application of neuralnetwork to predicting reservoir and calculatingthickness in CB oilfield[J].Journal of the Universi-ty of Petroleum,1998,22(2):17-20
[4]吴秋波,吴元,王允诚.混合学习法前向网络多属性储层参数预测[J].西南石油大学学报(自然科学版),2011,33(2):68-72Wu Q B,Wu Y,Wang Y C.Reservoir parameterprediction by multi-attribute based on hybrid learn-ing algorithm of feed-forward network[J].Journalof Southwest Petroleum University(Science&Technology Edition),2011,33(2):68-72
[5]宋维琪.应用地震属性与测井数据反演储层参数[J].勘探地球物理进展,2003,26(3):216-219Song W Q.Inversion of reservoir parameters withseismic attributes and logging data[J].2003,26(3):216-219
[6]刘百红,李建华,魏晓东,等.随机反演在储层预测中的应用[J].地球物理学进展,2009,24(2):581-589Liu B H,Li J H,Wei X D,et al.The applicationof stochastic seismic inversion in reservoir prediction[J].Progress in Geophysics,2009,24(2):581-589
[7]Spikes K,Mukerji T,Dvorkin J,et al.Probabilis-tic seismic inversion based on rock-physics models[J].Geophysics,2007,72(5):R87-R97
[8]Mukerji T,Avseth P,Mavko G,et al.Statisticalrock physics:Combining rock physics,informationtheory and geostatistics to reduce uncertainty in seis-mic reservoir characterization[J].The LeadingEdge,2001,20(3):313-319
[9]Gunning J,Michael E G.Detection of reservoirquality using Bayesian seismic inversion[J].Geo-physics,2007,72(3):R37-R49
[10]Ulvmoen M,Omre H.Improved resolution inbayesian lithology/fluid inversion from prestackseismic data and well observations:Part1-Methodol-ogy[J].Geophysics,2010,75(2):R21-R35
[11]胡华锋.基于叠前道集的储层参数反演方法研究[D].青岛:中国石油大学(华东),2011Hu H F.The study of petrophysical-properties in-version based on pre-stack seismic data[D].Qingd-ao:China University of Petroleum(EastChina),2011
[12]Grana D,Rossa E D.Probabilistic petrophysical-properties estimation integrating statistical rockphysics with seismic inversion[J].Geophysics,2010,75(3):O21-O37
[13]秦鑫.一种蒙特卡罗贝叶斯分类的改进方法[D].武汉:华中师范大学,2004Qin X.An improved method of Monte Carlo Bayes-ian classification[D].Wuhan:Central China NormalUniversity,2004
[14]Larsen A L,Ulvmoen M,Omre H,et al.Bayesianlithology/fluid prediction and simulation on the basisof a Markov-chain prior model[J].Geophysics,2006,71(5):R69-R78
[15]Berryman J G.Mixture theories for rock proper-ties:A handbook of physical constants[M].Liver-more:American Geophysical Union,1995:205-228
[16]Hashin Z,Shtrikman S.A variational approach tothe theory of effective magnetic permeability of mul-tiphase materials[J].Journal of Applied Physics,1962,33(10):3125-3131
[17]Gassmann F.Elastic waves through a packing ofspheres[J].Geophysics,1951,16(4):673-685
[18]Mavko G,Mukerji T,Dvorikin J.The rock physicsHandbook:Tools for seismic analysis in porousmedia[M].HeFei:University of Science and Techo-nology of China Press,2008:93-94
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心