Multiphase flowrate measurement with time series sensing data and sequential model

Accurate multiphase flowrate measurement is challenging but vital in the energy industry to monitor the production process. Machine learning has recently emerged as a promising method for estimating multiphase flowrates based on different conventional flow meters. In this paper, we propose a Convolutional Neural Network (CNN)-Long-Short Term Memory (LSTM) model and a Temporal Convolutional Network (TCN) model to estimate the volumetric liquid flowrate of oil/gas/water three-phase flow based on the Venturi tube. The volumetric flowrates of the liquid and gas phase vary from 0.1-10 m(3)/h and 7.6137-86.7506 m(3)/h, respectively. We collected time series sensing data from a Venturi tube installed in a pilot-scale multiphase flow facility and utilized single-phase flowmeters to acquire reference data before mixing. Experimental results suggest that the proposed CNN-LSTM and TCN models can effectively deal with the time series sensing data from the Venturi tube and achieve a good accuracy of multiphase flowrate estimation under different flow conditions. TCN achieves a better accuracy for both liquid and phase flowrate estimation than CNN-LSTM. The results indicate the possibility of leveraging conventional flow meters for multiphase flowrate estimation under various flow conditions.

Automated summary

Accurate multiphase flowrate measurement is challenging but vital in the energy industry, and machine learning has emerged as a promising method for estimating such flowrates. The proposed CNN-LSTM and TCN models effectively deal with time series sensing data from a Venturi tube and achieve good accuracy of multiphase flowrate estimation under different flow conditions, with TCN performing better than CNN-LSTM for both liquid and gas phase flowrate estimation. Leveraging conventional flow meters for multiphase flowrate estimation under various flow conditions is deemed possible.