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
The carbonate reservoirs in the Tarim Basin are characterized by anisotropy and strong heterogeneity.Combined with an integrated analysis of data from seismic,geology,and drilling results,a series of attributes which ...The carbonate reservoirs in the Tarim Basin are characterized by anisotropy and strong heterogeneity.Combined with an integrated analysis of data from seismic,geology,and drilling results,a series of attributes which are suitable for fractured and caved carbonate reservoir prediction is discussed,including amplitude,coherence analysis,spectra decomposition,seismic absorption attenuation analysis and impedance inversion.Moreover,3-D optimization of these attributes is achieved by integration of multivariate discriminant analysis and principle component analysis,where the logging data are taken as training samples.Using the optimized results,the spatial distribution and configuration features of the caved reservoirs can be characterized in detail.This technique not only improves the understanding of the spatial distribution of current reservoirs but also provides a significant basis for the discovery and production of carbonate reservoirs in the Tarim Basin.展开更多
A transparent 3-mercaptopropyl trimethoxysilane(MPTMS)/Ag/MoO3 composite anode is introduced to fabricate green organic light-emitting diodes(OLEDs). Effects of the composite anode on brightness and operating voltage ...A transparent 3-mercaptopropyl trimethoxysilane(MPTMS)/Ag/MoO3 composite anode is introduced to fabricate green organic light-emitting diodes(OLEDs). Effects of the composite anode on brightness and operating voltage of OLEDs are researched. By optimizing the thickness of each layer of the MPTMS/Ag/MoO3 structure, the transmittance of MPTMS/Ag(8 nm)/Mo O3(30 nm) reaches over 75% at about 520 nm. The sheet resistance is 3.78 ?/□, corresponding to this MPTMS/Ag(8 nm)/MoO3(30 nm) structure. For the OLEDs with the optimized anode, the maximum electroluminescence(EL) current efficiency reaches 4.5 cd/A, and the maximum brightness is 37 036 cd/m2. Moreover, the OLEDs with the optimized anode exhibit a very low operating voltage(2.6 V) for obtaining brightness of 100 cd/m2. We consider that the improved device performance is mainly attributed to the enhanced hole injection resulting from the reduced hole injection barrier height. Our results indicate that employing the MPTMS/Ag/MoO3 as a composite anode can be a simple and promising technique in the fabrication of low-operating voltage and high-brightness OLEDs.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.
基金co-supported by the National Basic Resarch Program of China (Grant No.2011CB201103)the National Scince and Technology Major Project (Grant No.2011ZX05004003)
文摘The carbonate reservoirs in the Tarim Basin are characterized by anisotropy and strong heterogeneity.Combined with an integrated analysis of data from seismic,geology,and drilling results,a series of attributes which are suitable for fractured and caved carbonate reservoir prediction is discussed,including amplitude,coherence analysis,spectra decomposition,seismic absorption attenuation analysis and impedance inversion.Moreover,3-D optimization of these attributes is achieved by integration of multivariate discriminant analysis and principle component analysis,where the logging data are taken as training samples.Using the optimized results,the spatial distribution and configuration features of the caved reservoirs can be characterized in detail.This technique not only improves the understanding of the spatial distribution of current reservoirs but also provides a significant basis for the discovery and production of carbonate reservoirs in the Tarim Basin.
基金supported by the National Natural Science Foundation of China(No.21174036)the National High Technology Research and Development Program of China(863 Program)(No.2012AA011901)the National Basic Research Program of China(973 Program)(No.2012CB723406)
文摘A transparent 3-mercaptopropyl trimethoxysilane(MPTMS)/Ag/MoO3 composite anode is introduced to fabricate green organic light-emitting diodes(OLEDs). Effects of the composite anode on brightness and operating voltage of OLEDs are researched. By optimizing the thickness of each layer of the MPTMS/Ag/MoO3 structure, the transmittance of MPTMS/Ag(8 nm)/Mo O3(30 nm) reaches over 75% at about 520 nm. The sheet resistance is 3.78 ?/□, corresponding to this MPTMS/Ag(8 nm)/MoO3(30 nm) structure. For the OLEDs with the optimized anode, the maximum electroluminescence(EL) current efficiency reaches 4.5 cd/A, and the maximum brightness is 37 036 cd/m2. Moreover, the OLEDs with the optimized anode exhibit a very low operating voltage(2.6 V) for obtaining brightness of 100 cd/m2. We consider that the improved device performance is mainly attributed to the enhanced hole injection resulting from the reduced hole injection barrier height. Our results indicate that employing the MPTMS/Ag/MoO3 as a composite anode can be a simple and promising technique in the fabrication of low-operating voltage and high-brightness OLEDs.
