Sensor Fault Diagnosis Of Oilfield Monitoring System Based On Multivariate Statistical Analysis

This thesis is based on the research background of the on-site production process of the oil field,with the main purpose is to ensure the reliable operation of testing instruments and equipment during the production process of the oil field,and focus on the fault diagnosis of sensors in the oil field monitoring system.The improved method based on the SLKPCA in the multivariate statistical analysis is used to solve the problems faced by the sensor fault diagnosis of the oilfield monitoring system,so as to improve the accuracy of the oilfield sensor equipment fault diagnosis.The main research content of the thesis is:In view of that SLKPCA is unable to distinguish between abnormal production parameters of oil wells and changes in production status and sensor failures in the oil field production process when performing fault diagnosis on the sensors of the oilfield monitoring system,an improved SLKPCA oil field monitoring sensor fault diagnosis method is proposed.The experimental results show that the improved SLKPCA method can effectively distinguish abnormal production parameters of oil wells,changes in production status and sensor failures during oil field production,improving the accuracy of fault diagnosis.In the fault location,the method based on the mutual information contribution graph is used to judge the correlation between each variable and the monitoring statistics,the fault variable can be accurately located,and the failure of multiple sensors in the oilfield monitoring system can still be effectively located.According to the characteristics of the data in the production process of the oilfield,in view of the weak generalization ability of the sensor fault diagnosis model when the production status of the oilfield changes and the working conditions change,a dynamic update model method based on the two-dimensional correlation coefficient method is proposed.The two-dimensional correlation coefficient method is combined with the improved SLKPCA method to realize the dynamic update of the sensor fault diagnosis model,using the actual production data of electric pump wells in the oil field to perform fault diagnosis experiments on the proposed method.The experimental results show that when the oilfield production status changes,the updated sensor fault diagnosis model can adapt to the current oilfield operating status and effectively improve the generalization ability of the model.Applying the above-mentioned method to sensor fault diagnosis of oilfield monitoring system can ensure efficient and smooth production,reduce production costs,and provide safety guarantees for field workers,which is of great significance for improving oilfield production efficiency.