【摘要】 利用双孔隙度预测技术 ,进行井信息约束条件下的波阻抗反演 ,采用神经网络非线性最优化技术进行油藏多参数模式识别 ,指出储层有利区。模式识别的结果包括孔隙度、含流体性质及其饱和度等。以濮城油田沙一下l、濮 1 2 8块沙三上6 油藏为例 ,论述了如何从录井、测井和地震资料中提取指示油气藏分布的信息 ,并对这些信息进行交互校正和分类 ,然后采用双孔隙度法进行油藏描述 ,使油藏特征定量化描述得以实现更多还原

【Abstract】 In the dual porosity method, we will describe how to extract some useful information from core, well logging and seismic data, calculate the acoustic porosity and the density porosity, build the geologic model, perform impedance inversion with the well constraint, apply those data to multi-parameter reservoir description through nonlinear neural network pattern recognition. Finally, we obtain the rock porosity and the oil saturation.In this paper we take lower S 1 and upper S 3 of Pu128 block of Pucheng oilfield for example. We describe how to get distributing information from geologic and seismic data after information’s correlation and classification. We make use of dual porosity method to realize the application to the characteristic parameter’s quantitative description.更多还原