Application of machine learning in on-line calibration of flow measurement errors

Abstract

With the vigorous construction of an intelligent pipe network, an automatic flowmeter was the key to realize online flow measurement of oil and gas. Ensuring the accuracy of flow measurement was very important to achieve real-time feedback on oil–gas production status for adjusting the oil recovery technology. However, due to the changing flow state of the oil–gas in the pipe and the error of the flow-meter itself, there were some errors during flow measurement. Therefore, based on the machine learning method, this paper corrected the errors caused by the pulsating flow and the flowmeter itself in data drift. The results showed that the toy–LSTM model can measure the pulsating flow accurately. When the number of toy–LSTM model neurons was ranged between 10 and 50, both RMSE and R2 showed good performance. The average of multiple predicted results presented higher accuracy than that of single predicted results, and furthermore, the drop rate can significantly increase the robustness of the toy–LSTM model. It should be noted that the optimal case proposed in this paper was based on a specific condition in the field. In the actual application, it was necessary to comprehensively consider the number of branch pipes, the change in oil well production, the characteristics of the oil–gas–water mixture, and other factors to determine the specific values of the number of neurons, the drop rate, and other parameters. Aiming at the distortion caused by the data drift of the flowmeter itself, the program was designed and data were collected from the software platform, information collector, and base station. The data acquisition of temperature–pressure integrated sensor and flowmeter was carried out in the field. In 20 groups of experiments, the online corrected program based on the Mexican Hat wavelet transform can realize accurate identification and automatic corrected responses, and the corrected time was within 5 min.