高分辨率非线性储层物性参数反演方法和应用
|
摘要
对于陆相沉积环境下的复杂隐蔽岩性储层,由于观测信息不准确,如信息重叠、信息缺失和噪音污染,以及岩石物理关系模糊等原因,储层横向预测存在不惟一性、不稳定性和不确定性.基于线性假定的常规储层横向预测技术已不适用于复杂隐蔽岩性储层的勘探.本文采用一种非线性储层岩性物性褶积模型,建立波阻抗与孔隙度/泥质含量的函数关系;通过多级结构分解和双向边沿子波检测来刻画复杂岩石物理关系;通过Caianiello褶积神经网络实现确定性反演、统计反演和非线性理论三者有机结合;最后联合应用基于逆算子的反演方法和基于正算子的重建算法实现了综合地质、测井和地震波阻抗信息进行高分辨率储层物性参数反演.非线性储层物性参数反演采用多井约束机制和分频反演方式,在陆相和近海油气勘探资料的实际应用中,取得了明显应用效果.
In the prediction of complex reservoir in continental deposition environment, because of inexact data (e.g., information-overlapping, information-incomplete, and noise-contaminated) and ambiguous physical relationship, inversion results suffer from nonuniqueness, instability, and uncertainty. Thus, reservoir prediction technologies based on linear assumption are unsuited for these complex areas. By means of nonlinear rock physical models, the method presented in the paper establishes a relationship between impedance and porosity/clay-content. Through multistage decomposition and bidirectional edge wavelet detection, it can depict more complex rock physical relationship. Moreover, it uses the Caianiello neural network to implement the combination of deterministic inversion, statistical inversion and nonlinear theory. Last, it incorporates geological information, well data and seismic impedance to perform petrophysical parameters inversion by combined applications of model-based and deconvolution-based methods. The joint inversion consists of four steps: (1) multistage vertical edge detection wavelets extraction at the wells and nonlinear factor estimation, (2) initial petrophysical parameters estimation by vertical edge detection wavelets, (3) multistage transverse edge detection wavelets extraction and nonlinear factor estimation, (4) final petrophysical parameters reconstruction by transverse edge detection wavelets. The scheme adopts multi-well constraint and separate-frequency inversion mode and achieves good results in the application on some continental and near-sea exploration data.
In the prediction of complex reservoir in continental deposition environment, because of inexact data (e.g., information-overlapping, information-incomplete, and noise-contaminated) and ambiguous physical relationship, inversion results suffer from nonuniqueness, instability, and uncertainty. Thus, reservoir prediction technologies based on linear assumption are unsuited for these complex areas. By means of nonlinear rock physical models, the method presented in the paper establishes a relationship between impedance and porosity/clay-content. Through multistage decomposition and bidirectional edge wavelet detection, it can depict more complex rock physical relationship. Moreover, it uses the Caianiello neural network to implement the combination of deterministic inversion, statistical inversion and nonlinear theory. Last, it incorporates geological information, well data and seismic impedance to perform petrophysical parameters inversion by combined applications of model-based and deconvolution-based methods. The joint inversion consists of four steps: (1) multistage vertical edge detection wavelets extraction at the wells and nonlinear factor estimation, (2) initial petrophysical parameters estimation by vertical edge detection wavelets, (3) multistage transverse edge detection wavelets extraction and nonlinear factor estimation, (4) final petrophysical parameters reconstruction by transverse edge detection wavelets. The scheme adopts multi-well constraint and separate-frequency inversion mode and achieves good results in the application on some continental and near-sea exploration data.
引文
[1]Brillouin L.Science and Information Theory.New York:Academic,1962
[2]Wyllie MRJ,Gregory A R,Gardner G H F.An experimental investigation of factors affecting elastic wave velocities in porous media.Geophysics,1958,23:459~493
[3]Nur A,Mavko G,Dvorkin J,et al.Critical porosity:a key to relating physical properties to porosity in rocks.The Leading Edge,1998,17:357~362
[4]印兴耀,吴国忱,张洪宙.神经网络在储层横向预测中的应用.石油大学学报(自然科学版),1994,18(5):20~25Yin X Y,Wu GC,Zhang HZ.The application of neural networksin the reservoir prediction.Journal of the University of Petroleum,China(Edition of Natural Science)(in Chinese),1994,18(5):20~25
[5]杨文采.神经网络算法在地球物理反演中的应用.石油物探,1995,34(2):116~120Yang WC.Application of neural network algorithms to geophysical inversion.GPP,1995,34(2):116~120
[6]陆文凯,李衍达,牟永光.误差反传播神经网络法地震反演.地球物理学报,1996,39(增):292~301Lu W K,Li Y D,Mu Y G.Seismic inversion using error-back-propagation neural network.Chinese J.Geophys.(in Chinese),1996,39(Suppl.):292~301
[7]Robinson E A.Predictive decomposition of time series with applicationto seismic exploration.Geophysics,1954,32:418~484
[8]Robinson E A.Predictive decomposition of seismic traces.Geophysics,1957,22:767~778
[9]Robinson E A,Treitel S.Geophysical Signal Analysis.Prentice-Hall,Inc,1980
[10]Saggaf MM,Robinson E A.A unified framework for the deconvolution of traces of nonwhite reflectivity.Geophysics,2000,65:1660~1676
[11]Caianiello E R.Outline of a theory of thought-processes andthinking machines.J.Theoret.Biol.,1961,2:204~235
[12]Fu L Y.Aneuron filtering model and its neural network for space-andtime-varying signal processing.ThirdInternational Conference on Cognitive and Neural Systems,Boston University,Paper Vision B03,1999
[13]Fu L Y.Caianiello neural network method for geophysical inverse problems.In:Poulton M.Computational Neural Networks for Geophysical Data Processing.Amsterdam:Pergamon Press,2001.187~215
[14]Fu L Y.Joint inversions of seismic data for acoustic impedance.Geophysics,2004,69:994~1004
[15]崔炯成,吴媚,李维新等.高分辨率非线性地震波阻抗反演方法和应用.石油物探,2007,46(1):1~12Cui J C,Wu M,Li WX,et al.Nonlinear high-resolutionimpedance inversion method andits application.GPP,2007,46(1):1~12
[16]Fu L Y.Joint lithologic inversion.In:Wong P,Aminzadeh F,Nikravesh Meds.Soft Computing for Reservoir Characterization and Modeling.Springer-Verlag Publishers,2002.511~530
[17]Burge D W,Neff D B.Well-based seismic lithology inversion for porosity and pay-thickness mapping.The Leading Edge,1998,17:166~171
[2]Wyllie MRJ,Gregory A R,Gardner G H F.An experimental investigation of factors affecting elastic wave velocities in porous media.Geophysics,1958,23:459~493
[3]Nur A,Mavko G,Dvorkin J,et al.Critical porosity:a key to relating physical properties to porosity in rocks.The Leading Edge,1998,17:357~362
[4]印兴耀,吴国忱,张洪宙.神经网络在储层横向预测中的应用.石油大学学报(自然科学版),1994,18(5):20~25Yin X Y,Wu GC,Zhang HZ.The application of neural networksin the reservoir prediction.Journal of the University of Petroleum,China(Edition of Natural Science)(in Chinese),1994,18(5):20~25
[5]杨文采.神经网络算法在地球物理反演中的应用.石油物探,1995,34(2):116~120Yang WC.Application of neural network algorithms to geophysical inversion.GPP,1995,34(2):116~120
[6]陆文凯,李衍达,牟永光.误差反传播神经网络法地震反演.地球物理学报,1996,39(增):292~301Lu W K,Li Y D,Mu Y G.Seismic inversion using error-back-propagation neural network.Chinese J.Geophys.(in Chinese),1996,39(Suppl.):292~301
[7]Robinson E A.Predictive decomposition of time series with applicationto seismic exploration.Geophysics,1954,32:418~484
[8]Robinson E A.Predictive decomposition of seismic traces.Geophysics,1957,22:767~778
[9]Robinson E A,Treitel S.Geophysical Signal Analysis.Prentice-Hall,Inc,1980
[10]Saggaf MM,Robinson E A.A unified framework for the deconvolution of traces of nonwhite reflectivity.Geophysics,2000,65:1660~1676
[11]Caianiello E R.Outline of a theory of thought-processes andthinking machines.J.Theoret.Biol.,1961,2:204~235
[12]Fu L Y.Aneuron filtering model and its neural network for space-andtime-varying signal processing.ThirdInternational Conference on Cognitive and Neural Systems,Boston University,Paper Vision B03,1999
[13]Fu L Y.Caianiello neural network method for geophysical inverse problems.In:Poulton M.Computational Neural Networks for Geophysical Data Processing.Amsterdam:Pergamon Press,2001.187~215
[14]Fu L Y.Joint inversions of seismic data for acoustic impedance.Geophysics,2004,69:994~1004
[15]崔炯成,吴媚,李维新等.高分辨率非线性地震波阻抗反演方法和应用.石油物探,2007,46(1):1~12Cui J C,Wu M,Li WX,et al.Nonlinear high-resolutionimpedance inversion method andits application.GPP,2007,46(1):1~12
[16]Fu L Y.Joint lithologic inversion.In:Wong P,Aminzadeh F,Nikravesh Meds.Soft Computing for Reservoir Characterization and Modeling.Springer-Verlag Publishers,2002.511~530
[17]Burge D W,Neff D B.Well-based seismic lithology inversion for porosity and pay-thickness mapping.The Leading Edge,1998,17:166~171
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