油田产量预报的多维时间序列神经网络模型

对注水开发油田，提出一种新的油田动态研究模型，即油田产油量、产水量多维时间序列神经网络预测器。同时考虑油田增产措施和油田开发过程时变性，对各种随机干扰因素具有自适应性。从信息论角度出发，利用神经网络非线性时间序列预测模型，构造了油田产油量、产水量的多维时间序列神经网络预测器。结果表明，该预到器具有较高的预测精度，适合于油田各个阶段的产油量、产水量的动态预报。完善了油田产油量、产水量动态预报理论。最后给出了一个动态预报的实例。

Neural Network Model of Dynamic Research for Injection Water Oil Fields

A new dynamic research model for injection water oil fields was presented. The model contains increase production measures and it is adaptive to various random factors together in the process of oil field exploitation. In this paper, using neuron network models of nonlinear multidimensional time series prediction, neuron network predictors for the oil production and water production of oil fields were constructed. Results illustrated that the predictors were up to higher prediction precision. But this method improved the theory of the dynamic prediction of oil production and water production .Finally, an example of the dynamic prediction were given.