Achieving Unprecedented Precision in Multiphase Flow Meter Monitoring with Deep Learning-Driven Abnormality Detection

Abstract

Multiphase flow meters are commonly used in the oil and gas industry to measure oil, gas, and water flow rates simultaneously. However, the accuracy of these measurements can be significantly affected by various abnormalities. This study proposed a deep learning-based abnormality detection method using recurrent neural network (RNN) to identify abnormal conditions in the multiphase flow meter and help with maintenance and repair. The RNN model was trained and tested using historical data from the multiphase flow meter. The results showed that the RNN model could accurately identify abnormal conditions with an accuracy of 99.48%, F1-score of 99.48%, and a recall of 99.48% after troubleshooting. This improved the monitoring of the multiphase flow meter, allowing for faster identification and repair of any abnormalities. Additionally, the study demonstrated the potential of deep learning-based methods for improving the accuracy and reliability of multiphase flow meter measurements. In conclusion, the deep learning-based abnormality detection method using RNN proved to be an effective tool for identifying abnormalities in the multiphase flow meter, which can help to prevent costly downtime and repairs in the oil and gas industry. This study highlights the potential of using advanced machine learning techniques to improve the accuracy and reliability of multiphase flow meter measurements.