摘要
与传统的测井资料解释和信息处理技术相比较,在对非均质性较强、物性参数级差较大的储集层物性预测中,人工神经网络技术具有极强的自适应和自学习能力,其通过很强的非线性映射,能够精确地建立储集层参数与测井响应之间的非线性模型。在论述神经网络技术基本原理的基础上,对西峰油田延安组和延长组储层的物性参数(孔隙度和渗透率等)进行了预测,取得了较理想的结果。预测结果表明:渗透率参数级差不大(<102)时,预测精度高;渗透率的变化范围较大(>103)时,对具有高渗透率储层的预测精度高,而对具有低渗透率储层的预测值相对误差较大。
To compare with the traditional log data comprehensive explanation and information disposing technique, BP artificial neural network method possesses great ability of adapting itself, learning itself in parameter prediction of reservoirs which have great nonhomogeneity and big progression error of physical parameters. By use of the wonderful nonlinear mapping ability, BP artificial neural network method can build accurately nonlinear model between reservoir parameters and log response. On the basis of expounding essential principle of BP artificial neural network, the prediction of reservoir physical parameters (porosity and permeability) is carried out the method in Yanchang formation and Yan'an formation of Xifeng oil field, and the application result is staisfied. The permeability parameters predictive accuracy is high if the progression error (<10^(2)) of permeability is little. When the change range of permeability is big (>10^(3)), the predictive accuracy of high permeability reservoir is high, and the one of low permeability reservoir has big relative error.
出处
《天然气地球科学》
EI
CAS
CSCD
2004年第3期243-246,共4页
Natural Gas Geoscience
基金
中国科学院资源环境领域知识创新工程重要方向项目(编号:KZCX3-SW-128-04)资助.
关键词
BP神经网络
测井解释
孔隙度
渗透率
西峰油田
BP neural networks
Logging explanation
Porosity
Permeability
Xifeng oilfield.
