利用因子分析,从隔夹层数、砂厚、隔夹层厚、层厚、砂渺地比、孔隙度、渗透率、地层系数等17个参数中提取储集因子、流动因子、非均质因子、隔夹层因子、地层厚度因子、饱和度因子6个具有代表性并能反映各参数内部规律的公共因子.通过聚类分析方法和判别分析方法将821个样本分为4类,即4类流动单元,然后采用流动单元约束的随机建模,运用序贯高斯随机模拟方法来进行属性场的预测和模拟.预测结果良好,说明采用该方法可以完成对储层的精细定量描述.%Six representative common factors (storage factor, flow factor, heterogeneity factor, interlayer factor, layer thickness factor and saturation factor) reflecting the inner rules of them were extracted by means of factor analysis from seventeen parameters including interlayer number, sand thickness, interlayer thickness, layer thickness, ratio of sand thickness to layer thickness porosity, permeability, and formation capacity. 821 specimens were classified into four flow units with the aid of cluster analysis and discriminant analysis. Afterwards, stochastic modeling by restraining flow units was applied, and then prediction and simulation of attribute field were done by sequential Gaussian stochastic simulation. The prediction results are perfect, which indicates that this method is capable of describing reservoir finely and quantitively.
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