Abnormal identification of dynamic liquid level measurement data in oil wells based on normalized RBFNN
In order to solve the lack of accuracy of data feature extraction caused by missing values,nonlinear and non-stationary characteristics in the measurement data of oil well dynamic liquid level,and the problem that the accurate measurement of oil well dynamic liquid level position cannot be achieved,an abnormal identification method of oil well dynamic liquid level measurement data based on normalized RBF neural network is proposed.Through the sensor installed on the oil well to collect data in real time,the multi-source oil normalization processing technology based on expert database is used to complete the data verification and integration.Empirical mode decomposition(EMD)is used to decompose the data into trend and fluctuation terms.After removing the fluctuation terms,the trend data is used as the input of normalized RBF neural network.The experimental results show that this method can effectively complete incomplete data,accurately identify abnormal data through the trend term and provide reasonable alternative values,and the obtained dynamic liquid level position curve is basically consistent with the actual dynamic liquid level position curve,with the maximum error of less than 2 m,which can realize the accurate estimation of the dynamic liquid level position of oil wells.The abnormal identification method of oil well dynamic liquid level measurement data based on normalized RBF neural network solves the challenges brought by data missing,nonlinearity and non stationarity,realizes the accurate estimation of oil well dynamic liquid level position,and provides technical support for real-time monitoring and data analysis of oil well dynamic liquid level.
normalizationRBF neural networkoil well dynamic liquid levelmeasurement dataabnormal identification
万方数据
维普
