摘要
Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.
采用编制的计算机程序计算了注采比与水油比法、多元回归法、物质平衡法、GM(1,1)模型和BP神经网络预测法的拟合注采比与实际注采比之间的误差大小,其平均相对误差分别为1.67%、1.08%、19.22%、1.38%和0.88%。对各种预测法产生误差的原因进行了理论分析,得出BP神经网络预测方法的精度最高,而且具有较好的自适应性,能够反映影响注采比的各种因素与注采比的内在关系。因此,BP神经网络方法可用于预测油田注采比。
