储层岩石表面接触角的不确定性研究

本文指出，储层岩石表面粗糙，非均质性及高的能量，使我们难以用接触角来量度它的润湿性质，由于储层岩石孔隙结构不符合毛管束模型，使得Laplace公式仅在表现接触角趋于2度或180度时可用于根据毛管压力曲线计算储层岩石的孔径分布，好在由于储层岩石表面粗糙使我们常常会遇到这一极端情况。     

Upscaling Uncertain Permeability Using Small Cell Renormalization

Sedimentary rocks have structures on all length scales from the millimeter to the kilometer. These structures are generally associated with variations in rock permeability. These need to be modeled if we are to make predictions about fluid flow through the rock. However, existing computers are not powerful enough for us to be able to represent all scales of heterogeneity explicitly in our fluid flow models—hence, we need to upscale. Small cell renormalization is a fast method for upscaling permeability, derived from an analogue circuit of resistors. However, it assumes that the small scale permeability distribution is known. In practice, this is unlikely. The only information available about small scale properties is either qualitative, derived from the depositional setting of the reservoir, or local to the wells as a result of coring or logging. The influence of small scale uncertainty on large scale properties is usually modelled by the Monte Carlo method. This is time-consuming and inaccurate if not enough realisations are used. This paper describes a new implementation of renormalization, which enables the direct upscaling of uncertain small-scale permeabilities to produce the statistical properties of the equivalent coarse grid. This is achieved by using a perturbation expansion of the resistor-derived equation. The method is verified by comparison with numerical simulations using the Monte Carlo method. The prediction of expected large-scale permeability and its standard deviation are shown to be accurate for small cell standard deviations of up to 40% of the mean cell value, using just the first nonzero term of the perturbation expansion. Inclusion of higher order terms allows larger standard deviations to be modeled accurately. Evaluation of cross-terms allows correlations of actual cell values, over and above the background structure of mean cell values. The perturbation method is significantly faster than conventional Monte Carlo simulation. It needs just two calculations whereas the Monte Carlo method needs many thousands of realisations to be generated and renormalized to converge. This results in significant savings in computer time.     

乾县台地倾斜潮汐测量结果及动态特征

利用Ｖｅｎｅｄｉｋｏｖ调和分析法分析了乾县地震台１９８３－１９９４年的地倾斜固体潮记录，提取了该地区倾斜固体潮汐因子及其动态变化特征，为地球动力学特别是地球内部结构的深入研究提供了固体潮方面的实测参数。     

Implications of εNd—La／Nb,Ba／Nb,Nb／Th Diagrams to Mantle Heterogeneity—Classification of Island—arc Basalts and Decomposition of EMII Component

A group of εNd/Nb,Ba/Nb,Nb/Th diagrams are used to study mantle heterogeneity.Island-arc basalts(IAB) are distributed in a triangle of these diagrams. Three end-member components (the MORB-type depleted mantle, the fluid released from subducted oceanic crust and the sediments from the continental crust) of the source of IAB may be displayed in these diagrams. Two types of IAB are identified .They are of the two-component type (with little continental sediments), such as the basalts from Aletians and New Britain ,and the three-compeonent type, such as those from Sunda, Lesser Antilles and Andes. In addition ,the EMII type mantle-derived rocks may also be divided into two groups. One is exemplified by continental flood basalts and some peridotite xenoliths, similar to IAB, with high La/Nb and Ba/Nb and low Nb/Th ratios, The other includes the Samoa-type oceanic island basalts, with low La/Nb and Ba/Nb and high Nb/Th ratios. The corresponding two sub-components of EMII are EMIIM, which is related to the metasomatism of lithosphere mantle by fluids released from the subducted oceanic crust, and EMIISR, related to the intervention of recycling continental sediments into the convective mantle.     

珠江三角洲东部常平地区景观异质性研究

本文以１９８８年－１９９６年６个时段景观遥感类型图为主要信息源,利用信息熵法和空隙度指数法对珠江三角洲东部常平地区的景观异质性进行了研究。重点探讨了景观组分的空间分布差异和异质性动态变化情况。结果显示,开发区主要呈宏观异质性分布,果园,农田和林地呈微观异质性分布,城镇和水体的异质性分布特征均出现显著变化。     

An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

ABSTRACT

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.