神经网络构建稠油掺稀黏度预测模型研究

本文利用BP神经网络能够较好地在实验数据基础上建立稠油掺稀黏度预测模型，以对新疆塔河油田稠油掺入四种稀油的黏度预测为例，通过BP神经网络建立预测模型，并与四种传统基于线性回归的建模方法及进行改进的方法进行对比，结果表明：利用神经网络建立模型的最大误差为4.1%，黏度与温度、稀稠比的非线性关系能够较好拟合，对比基于线性回归方法的建模方法及其改进算法有着更高的拟合精度。

Research on the Prediction Model the Viscosity of Viscous Oil Mixtures Based on BP-Neural Network

A research was carried out on the prediction model of the viscosity of viscous oil mixture based on neural network .The research proved that the model of viscosity of the viscous oil mixtures established by the use of the BP-neural network method is reliable. A series of prediction models of the viscosity of viscous oil mixtures of an oil field in Sinkiang was established by BP- neural network as an example. The maximum error of the model was 4.1%. The correlation between viscosity, temperature and the rate of viscous oil and thin oil matched preferable, and proved a better fit compared with linear regression and Cragoe method.