关键词: 聚合物驱, 影响因素, 含水率, 预测, 神经网络模型

Abstract: The varying characteristics of composite water cut in the area of polymer flooding and multi factors affecting the results of polymer flooding were analyzed.Taking these factors as inputs and the varying characteristics of the composite water cut as outputs,and taking the known inputs and the outputs of the early production areas as learning samples,a modified model for the three-layer cumulative back propagation (CBP) neural network was established.Some variables of the output layers may be unknowable in the learning samples in this model.Therefore,the all areas with the different production histories can be put into the learning samples,and the unknown points can be predicted.This method can avoid the deviation of the composite water cut in the industrial areas predicted by mode chart method and human correction and used to quantitatively analyze the various factors influencing the dynamic performances of polymer flooding.This model has been successively applied to predict composite water cut,liquid production and oil production of some subsequent production areas.The prediction result may be the good bases for making oilfield development plan.

Key words: polymer flooding, influencing factors, water cut, prediction, neural network model