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 co...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.展开更多
河南师范大学化学化工学院远冰冰博士与深圳大学牛青山教授合作在Nature子刊Nat Commun发表了题为"Asymmetric polyamide nanofilms with highly ordered nanovoids for water purification"的研究论文。如何对聚酰胺致密层...河南师范大学化学化工学院远冰冰博士与深圳大学牛青山教授合作在Nature子刊Nat Commun发表了题为"Asymmetric polyamide nanofilms with highly ordered nanovoids for water purification"的研究论文。如何对聚酰胺致密层进行结构设计,以提高渗透性和选择性,突破"Trade-Off"曲线,是分离膜研究领域的关键问题。目前有两种主流方法,一是降低致密层的厚度,降低水的传输阻力;二是在致密层中引入纳米孔结构,增加比表面积和水的传输通路。若能把这两种方法结合起来,不仅可提高渗透性,还能同时调控选择性,鱼和熊掌可兼得。展开更多
基金National Key Science & Technology Special Projects(Grant No.2008ZX05000-004)CNPC Projects(Grant No.2008E-0610-10).
文摘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.
文摘河南师范大学化学化工学院远冰冰博士与深圳大学牛青山教授合作在Nature子刊Nat Commun发表了题为"Asymmetric polyamide nanofilms with highly ordered nanovoids for water purification"的研究论文。如何对聚酰胺致密层进行结构设计,以提高渗透性和选择性,突破"Trade-Off"曲线,是分离膜研究领域的关键问题。目前有两种主流方法,一是降低致密层的厚度,降低水的传输阻力;二是在致密层中引入纳米孔结构,增加比表面积和水的传输通路。若能把这两种方法结合起来,不仅可提高渗透性,还能同时调控选择性,鱼和熊掌可兼得。
