Identification of high-pressure two-phase flow regime transition using image processing and deep learning

Abstract

The commercial production process of methane gas from methane hydrate has been developed by the MH21-S R&D consortium (MH21-S) in recent years. In order to establish the gas production method, an accurate gas-liquid two-phase flow regime identification under high-pressure conditions is indispensable. In the present study, a 28 m long test section with an inner diameter of 52.7 mm was newly constructed, and a nitrogen-water two-phase flow experiment was conducted at 3.0 MPa G. Two-phase flow images were successfully acquired using the high-speed camera from the visualization port. The sequential image of upward gas-liquid two-phase flow was merged into a single image by the time strip method. Then, flow regime identification using the convolutional neural network (CNN) trained under sequential images was carried out. The proposed method has shown an excellent capability to identify flow regime images obtained at the current high-pressure conditions. In addition, for the slug flow regime, it was found that the Taylor bubble length highly influences the identification performance, and the model proposed by Street and Tek showed good agreement for the present dataset. Furthermore, the bubbly-slug transition region was successfully evaluated and quantified through the present model. The current methods have shown a good correlation with the existing model. These results indicate that the gas void fraction at the transition region to be larger than the theoretical value for the elevated pressure system.