摘要
提出一种基于卷积神经网络的径向复合油藏自动试井解释方法,并利用现场实测数据验证其有效性和准确性。采用对数函数进行数据变换,采用均方误差作为损失函数,利用“dropout”方法避免过拟合,通过不断减小损失函数进行网络优化,得到最优的卷积神经网络。训练好的最优网络可直接用于解释径向复合油藏中井的压力恢复或压力降落数据,将给定的实测压力变化及其导数数据的双对数图输入到网络中,即可输出对应的油藏参数(流度比、储容比、无因次复合半径以及表征井储和表皮效应的无因次组),从而实现了试井参数解释的自动初拟合。利用大庆油田现场实测数据对该方法进行了验证,研究表明,该方法具有很高的解释精度,且优于解析法和最小二乘法。
An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network(CNN)is proposed,and its effectiveness and accuracy are verified using actual field data.In this paper,Based on the data transformed by logarithm function and the loss function of mean square error(MSE),the optimal CNN is obtained by reducing the loss function to optimize the network with'dropout'method to avoid over fitting.The trained optimal network can be directly used to interpret the buildup or drawdown pressure data of the well in the radial composite reservoir,that is,the log-log plot of the given measured pressure variation and its derivative data are input into the network,the outputs are corresponding reservoir parameters(mobility ratio,storativity ratio,dimensionless composite radius,and dimensionless group characterizing well storage and skin effects),which realizes the automatic initial fitting of well test interpretation parameters.The method is verified with field measured data of Daqing Oilfield.The research shows that the method has high interpretation accuracy,and it is superior to the analytical method and the least square method.
作者
李道伦
刘旭亮
查文舒
杨景海
卢德唐
LI Daolun;LIU Xuliang;ZHA Wenshu;YANG Jinghai;LU Detang(Hefei University of Technology,Hefei 230009,China;Daqing Well Logging Technology Service Company,Daqing 163453,China;University of Science and Technology of China,Hefei 230026,China)
出处
《石油勘探与开发》
SCIE
EI
CAS
CSCD
北大核心
2020年第3期583-591,共9页
Petroleum Exploration and Development
基金
国家科技重大专项(2017ZX05009005-002)。
关键词
径向复合油藏
试井解释
卷积神经网络
自动解释
人工智能
radial composite reservoir
well testing interpretation
convolutional neural network
automatic interpretation
artificial intelligence
