Data Processing Method of Cross-correlation Flow Based on K-means Cluster Analysis and Multiple Linear Regression
Cross-correlation flowmeter have been successfully applied in the measurement of oil well production profiles.However,due to the influence of sensors,conditioning circuits,and the noise of the fluid itself,a small amount of abnormal data may appear in the transit time values measured by the cross-correlation flowmeter,resulting in a significant difference between the calculated instantaneous flow rate and the actual value,leading to significant measurement errors in the average flow rate.In this regard,K-means clustering algorithm is pro-posed to cluster the transit time sample data,and a multivariate linear regression prediction model is established according to the clustering results to reasonably predict the transit time value to correct the outlier of the transit time.Comparing the predicted value with the actual val-ue,the accurate cross-correlation flow data was obtained.The established method was validated using experimental data from a multiphase flow loop,and the results show that the method can effectively eliminate anomalies in transit time,optimize flow measurement data,and have certain practical significance for two-phase flow measurement.
cross-correlation flowmetertransit timeK-means clustering algorithmmultiple linear regression
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维普
