基于LLE方法的地震属性特征提取技术及其应用(英文)
|
摘要
从大量的地震属性中提取最能反映地质特征的综合属性是储层预测技术的关键,通常选用降维方法来优选属性。目前应用最为广泛的线性降维方法。但是,由于地震属性与地质特征的关系通常是非线性的,基于线性变换的地震属性降维优化方法不能充分地反映这种非线性关系,降低了储层预测的精度。流形学习是一种新的非线性学习方法,它是通过保持数据局部结构的方式将高维数据投影到低维空间,挖掘和发现隐藏在数据中的内在特征与规律性,开拓了地震属性降维优化研究的新领域。本文首次实现了3D地震数据的层问属性特征提取,讨论了LLE方法及其关键技术,并以奥陶系礁滩相储层实例说明LLE和PCA两种方法降维及聚类的不同效果。理论模型分析和实例应用表明:LLE较好地保持了数据本身的原始结构;提取的综合属性和聚类相图较好地刻画了沉积相带、储层和流体的特征。这说明流形学习具有更好的特征提取性能。
How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis(PCA) is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can't solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding(LLE),realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.
How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis(PCA) is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can't solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding(LLE),realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.
引文
Chen,Q.,and Sidney,S.,1997,Seismic attribute technology for reservoir forecasting and monitoring:The Leading Edge,16(5),445 - 452.
Gong,H.,Zhou,Z.L.,and Ni,Y.,2008,Reduction dimension of seismic attribution parameter based on isomap algorithm:Natural as Industry,28(5),38 - 40.
Horn,R.A.,and Johnson,C.R.,1990,Matrix Analysis: Cambridge University Press,Cambridge,UK.
Liu,C.,Guo,K.,and Luo,D.J.,2007,A non-linear modeling method for seismic data optimization and forecasting physics property parameters:Progress in Geophysics,22(6),1880 - 1883.
Ni,Y.,2008,Nonlinear dimensionality reduction of isomap in the analysis of seismic attribute parameter data: Journal of Southwest University for Nationalities(Natural Science Edition),34(2),397 - 400.
Roweis,S.T.,and Saul,L.K.,2000,Nonlinear dimensionality reduction by locally linear embedding: Science,290,2323 - 2326.
Singh,Y.,and Carigalli,P.,2007,Lithofacies detection through simultaneous inversion and principle component attributes:The Leading Edge,26(17),1568 - 1575.
Sun,L.P.,Zheng,X.D.,Shou,H.,Li,J.S.,and Li,Y.D., 2010,Quantitative prediction of channel sand bodies based on seismic peak attributes in the frequency domain and its application:Applied Geophysics,7(1), 10- 17.
Tenenbaum,J.B.,de Silva,V.,and Langford,J.C.,2000,A global geometric framework for nonlinear dimensionality reduction:Science,290,2319- 2322.
Teuvo,K.,1982,Self-organized formation of topologically correct feature maps:Biological Cybernetics,43,59 - 69.
Wallet,B.C.,and Marfurt,K.J.,2008,A grand tour of multispectral components:A tutorial:The Leading Edge, 27(3),334 - 341.
Wallet,B.C.,de Matos,M.C.,Kwiatkowski,J.T.,and Suarez,Y.,2009,Latent space modeling of seismic data:An overview:The Leading Edge,28(12),1454 - 1459.
Yin,X.Y.,Kong,G.Y.,and Zhang,G.Z.,2008,Seismic attributes optimization based on kernel principal component analysis(KPCA) and application:Oil Geophysical Prospecting,43(2),179 - 183.
Zhao,J.F.,and Chen,X.H.,2005,Dual optimization of seismic attributes based on principle component analysis and K-L transform:Geophysical & Geochemical Exploration,29(6),253 - 256.
Zheng,X.D.,Li,Y.D.,Li,J.S.,and Yu,X.W.,2007, Reef and shoal reservoir characterization using paleogeomorpology constrained seismic attribute analysis:77th Ann.Internat.Mtg.,Soc.Expl.Geophys., Expanded Abstracts,1382 - 1386.
Zhou,J.G.,Deng,H.Y.,Fan,G.Z.,and Gong,Q.S.,2008, Geological-geophysical model and prediction application of Upper Ordovician Lianglitage reef-shoal reservoir in Tazhong Area,Tarim Basin:Marine Origin Petroleum Geology,13(3),17 - 23.
Gong,H.,Zhou,Z.L.,and Ni,Y.,2008,Reduction dimension of seismic attribution parameter based on isomap algorithm:Natural as Industry,28(5),38 - 40.
Horn,R.A.,and Johnson,C.R.,1990,Matrix Analysis: Cambridge University Press,Cambridge,UK.
Liu,C.,Guo,K.,and Luo,D.J.,2007,A non-linear modeling method for seismic data optimization and forecasting physics property parameters:Progress in Geophysics,22(6),1880 - 1883.
Ni,Y.,2008,Nonlinear dimensionality reduction of isomap in the analysis of seismic attribute parameter data: Journal of Southwest University for Nationalities(Natural Science Edition),34(2),397 - 400.
Roweis,S.T.,and Saul,L.K.,2000,Nonlinear dimensionality reduction by locally linear embedding: Science,290,2323 - 2326.
Singh,Y.,and Carigalli,P.,2007,Lithofacies detection through simultaneous inversion and principle component attributes:The Leading Edge,26(17),1568 - 1575.
Sun,L.P.,Zheng,X.D.,Shou,H.,Li,J.S.,and Li,Y.D., 2010,Quantitative prediction of channel sand bodies based on seismic peak attributes in the frequency domain and its application:Applied Geophysics,7(1), 10- 17.
Tenenbaum,J.B.,de Silva,V.,and Langford,J.C.,2000,A global geometric framework for nonlinear dimensionality reduction:Science,290,2319- 2322.
Teuvo,K.,1982,Self-organized formation of topologically correct feature maps:Biological Cybernetics,43,59 - 69.
Wallet,B.C.,and Marfurt,K.J.,2008,A grand tour of multispectral components:A tutorial:The Leading Edge, 27(3),334 - 341.
Wallet,B.C.,de Matos,M.C.,Kwiatkowski,J.T.,and Suarez,Y.,2009,Latent space modeling of seismic data:An overview:The Leading Edge,28(12),1454 - 1459.
Yin,X.Y.,Kong,G.Y.,and Zhang,G.Z.,2008,Seismic attributes optimization based on kernel principal component analysis(KPCA) and application:Oil Geophysical Prospecting,43(2),179 - 183.
Zhao,J.F.,and Chen,X.H.,2005,Dual optimization of seismic attributes based on principle component analysis and K-L transform:Geophysical & Geochemical Exploration,29(6),253 - 256.
Zheng,X.D.,Li,Y.D.,Li,J.S.,and Yu,X.W.,2007, Reef and shoal reservoir characterization using paleogeomorpology constrained seismic attribute analysis:77th Ann.Internat.Mtg.,Soc.Expl.Geophys., Expanded Abstracts,1382 - 1386.
Zhou,J.G.,Deng,H.Y.,Fan,G.Z.,and Gong,Q.S.,2008, Geological-geophysical model and prediction application of Upper Ordovician Lianglitage reef-shoal reservoir in Tazhong Area,Tarim Basin:Marine Origin Petroleum Geology,13(3),17 - 23.
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