聚合物驱产量预测的组合优化模型

选择了14个非线性模型建立模型库,并根据这些模型在一段时间内的预测误差分析,选取最优的非线性模型。采用了一阶指数平滑模型、神经网络模型进行聚合物驱产量的预测和比较分析;引入了目标函数的评优算法来确定前述的预测方法的最佳组合策略。结果表明,神经网络模型和组合优化模型的预测结果都能较好地逼近实际曲线,在绝大部分区段优于非线性优化模型和一次指数平滑模型的预测结果。这是因为这两种模型都综合考虑了多种产量影响因素,使预测结果更符合实际聚合物驱开采规律;而组合优化预测模型是建立在最大信息利用的基础上,它集结了单一模型所包含的信息,有着更好的预测效果。

Combining Optimization Model for Prediction of Oil Production by Polymer Flooding Process

Fourteen non-linear models are selected to establish a model base, and according to the prediction error analysis of these models during a period of time, the optimum non-linear model is selected. One-order smooth exponential model and neural network model are applied to predicting, correlating and analyzing oil production by polymer flooding process. Optimization arithmetic of objective function is introduced to determine the optimum combination of these prediction methods. The results show that the prediction value of network model and combining optimized model are well closed to the actual curves and better than non-linear optimized model and oneorder smooth exponential model. The reason is that the two models can comprehensively include many factors influencing oil production so that the prediction values are in more accordance with the actual production performance by polymer flooding. And the combining optimized model is established based on maximizing the information values, which comprises all the information of single model and has better prediction effect.