High Gas Void Fraction Flow Measurement and Imaging Using a THz-Based Device

Measuring in real-time two-phase flow composition of a mixed fluid having high gas void fraction (GVF) remains a challenging task in oil-gas fields. Such fluid is abundant in gas pipelines where pressure and temperature fluctuations lead to condensate gas. This may also be the case of crude oil produced from CO₂ or steam-based enhanced oil recovery, where the injected gas is mixed with the produced oil. This article presents a new concept of high GVF measurement and flow regime determination using a terahertz-based imaging system. It explores the fact that the gas phase has very low absorption of THz waves, while it yields an absorption factor that is proportional to the amount of liquid. The recent availability of low-cost THz imaging systems that can generate two-dimensional images at more than 100 frames/s makes them well suitable for flow metering applications. Two different artificial intelligence algorithms, namely support vector machine (SVM) and artificial neural network (ANN), were assessed using an in-house multiphase flow loop. The corresponding results reveal that while ANN and SVM yield very accurate results, the SVM technique performed slightly better where a maximal error of 0.46% for GVF in the GVF range from 80% to 100% could be achieved. In addition, it could accurately determine all three type of flow regimes (i.e., annular, stratified, or slug flow). This suggests that the technique can be considered as a good candidate for next-generation flow metering and imaging of multiphase flows.