Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

Abstract

Accurate multiphase flow measurement is vital in monitoring and optimizing various production processes. Deep learning has as of late arose as a promising approach for assessing multiphase flowrate dependent on various customary flow meters. In this paper, we propose a multi-modal sensor and Temporal Convolution Network (TCN) based method to predict the volumetric flowrate of oil/gas two-phase flows. The volumetric flowrates of the liquid and gas phase vary from 0.96 - 6.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{{3}}$ </tex-math></inline-formula> /h and 5.5 - 121.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{{3}}$ </tex-math></inline-formula> /h, respectively. The multi-modal sequential sensing data are simultaneously collected from a Venturi tube and a dual-plane Electrical Capacitance Tomography (ECT) sensor in a pilot-scale multiphase phase flow facility. The reference data are derived from the single-phase flowmeters. Z-score and First-Difference (FD) data pre-processing methods are employed to manipulate the collected instantaneous time series multi-modal sensing data. The pre-processed data are utilized for training the TCN model. Experimental results reveal that the TCN model can effectively predict the multiphase flowrate based on the multi-modal sensing data. The results provide guidance on data pre-processing methods for multiphase flowrate estimation and demonstrate the effectiveness of combining multi-modal sensors and TCN for multiphase flowrate prediction under complex flow conditions.