文章提出基于双向长短期记忆(bidirectional long short-term memory,Bi-LSTM)神经网络,考虑测井曲线相关性的测井曲线预测新方法。同一口井往往可以得到反映地层与井筒属性多种测井曲线,通过分析测井曲线之间存在的相关性,根据曲线之...文章提出基于双向长短期记忆(bidirectional long short-term memory,Bi-LSTM)神经网络,考虑测井曲线相关性的测井曲线预测新方法。同一口井往往可以得到反映地层与井筒属性多种测井曲线,通过分析测井曲线之间存在的相关性,根据曲线之间的相关性大小选择合适的训练样本,利用Bi-LSTM进行测井曲线预测。同时,测井曲线前后关联性强,Bi-LSTM可以考虑数据间的前后关联,从而提高测井曲线预测精度。实验结果表明,考虑曲线相关性的Bi-LSTM模型能减少样本数据,明显提高预测精度,均方误差相比单向长短期记忆神经网络方法能减小50%以上,具有很好的应用前景。展开更多
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 by actual field data. In this paper,...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 by 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.展开更多
文摘文章提出基于双向长短期记忆(bidirectional long short-term memory,Bi-LSTM)神经网络,考虑测井曲线相关性的测井曲线预测新方法。同一口井往往可以得到反映地层与井筒属性多种测井曲线,通过分析测井曲线之间存在的相关性,根据曲线之间的相关性大小选择合适的训练样本,利用Bi-LSTM进行测井曲线预测。同时,测井曲线前后关联性强,Bi-LSTM可以考虑数据间的前后关联,从而提高测井曲线预测精度。实验结果表明,考虑曲线相关性的Bi-LSTM模型能减少样本数据,明显提高预测精度,均方误差相比单向长短期记忆神经网络方法能减小50%以上,具有很好的应用前景。
基金Supported by the National Science and Technology Major Project(2017ZX05009005-002)
文摘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 by 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.
