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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. • Accurate multiphase flowrate is estimated by leveraging single phase flow meters. • Novel deep learning model is developed for multiphase phase flowrate estimation. • Volumetric gas and liquid flowrates are simultaneously estimated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep-learning-based method using a convolutional neural network (CNN) to predict the volume flow rates of individual phases in the oil–gas–water three-phase intermittent flow simultaneously by analyzing the measurement data from multiple sensors, including a temperature sensor, a pressure sensor, a Venturi tube and a microwave sensor. To build datasets, a series of experiments for the oil–gas–water three-phase intermittent flow in a horizontal pipe, in which gas volume fraction and water-in-liquid ratio ranges are 23.77–94.45% and 14.95–86.97%, respectively, and gas flow superficial velocity and liquid flow superficial velocity ranges are 0.66–5.23 and 0.27–2.14 m/s, respectively, have been carried out on a test loop pipeline. The preliminary results indicate that the model can provide relative prediction errors on the testing-1 dataset for the volume flow rates of oil-phase, gas-phase and water-phase within ±10% with 94.49%, 92.56% and 95.71% confidence levels, respectively. Additionally, the prediction results on the testing-2 dataset also demonstrate the generalization ability of the model. The consuming time of a prediction with one sample is 0.43 s on an Intel Xeon CPU E5-2678 v3, and 0.01 s on an NVIDIA GeForce GTX 1080 Ti GPU. Hence, the proposed CNN-based prediction model, which can fulfill the real-time application requirements in the petroleum industry, reveals the potential of using deep learning to obtain accurate results in the multiphase flow measurement field. [ABSTRACT FROM AUTHOR]