基于循环神经网络的缝洞型油藏油井产量预测  

Forecast of Oil Production in Fractured-Vuggy Reservoir by Using Recurrent Neural Networks

作　　者：周于皓 刘慧卿[1] 祁鹏 赵萌[1] 陈宇[1] ZHOU Yuhao;LIU Huiqing;QI Peng;ZHAO Meng;CHEN Yu(Petroleum Engineering Institute,China University of Petroleum (Beijing),Beijing102249,China)

机构地区：[1]中国石油大学(北京)石油工程学院

出　　处：《计算物理》2018年第6期668-674,共7页Chinese Journal of Computational Physics

摘　　要：利用神经网络的强大非线性映射和拟合能力,构建神经网络产量预测模型,并针对油田生产数据的高误差、易缺省等特性和曲线拟合预测不易收敛的情况,提出了训练数据集扩充方法和改良的均方误差损失函数.在拟合油井产量方面取得了显著的效果.With powerful nonlinear mapping and fitting ability of neural network,a production predicting neural network model is constructed.In view of high error,easy to default and other characteristics of oil field production data or data fitting and prediction is not easy to converge,a method of extended training data set and improved mean square error loss function are presented to get remarkable results in oil production fitting.

关 键 词：缝洞型油藏 产量预测 循环神经网络 反向传播算法 

分 类 号：TE344[石油与天然气工程—油气田开发工程]