摘要
在简单介绍了BP神经网络的基本原理的基础上, 以实例说明了BP神经网络方法在油田注采比预测中的应用, 并与注采比与水油比法、多元回归法和物质平衡法进行比较; 其次, 分析了各种预测法的计算结果, 并对神经网络预测法进行了检验。结果表明, BP神经网络预测方法具有更好的自适应性, 能够较好地反映影响注采比的各种因素与注采比的内在联系, 而且预测精度较高。因此认为,应用BP神经网络方法预测油田注采比是有效、可行的。
Based on the brief introduction of fundamental principles of BP neural network, this paper states the application of the above method in oilfield injection-production ratio prediction by history case and at the same time it is compared with water-oil-ratio method, multiple regression method and material balance method; in addition, it also analyzes the calculated results of each predicting method and checks the first method. The results show that the first method possesses much better self-feasibility and can not only thoroughly respects the inner links between various influencing factors of injection-production ratio and the ratio itself, but also have much higher prediction accuracy. Therefore BP neural network method can effectively and practically predict oilfield injection-production ratio.
出处
《大庆石油地质与开发》
CAS
CSCD
北大核心
2005年第2期53-54,i004-i005,共4页
Petroleum Geology & Oilfield Development in Daqing
关键词
注采比
物质平衡法
多元回归法
神经网络
预测
injection-production ratio
material balance method
multiple regression method
neural network
prediction
