Investigation on different scenarios of two-phase flow measurement using Orifice and Coriolis flow meters: Experimental and modeling approaches

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

“…Today, new machine learning techniques are efficient tools for optimization and sophisticated computing that reduce operating costs and improve system performance. Extensive research has been conducted in recent years on the application of intelligent machine learning methods in various sectors of the upstream oil and gas industry, such as desalting system analysis [37], hydrocarbon phase behavior prediction [38][39][40][41][42], determination of oil and gas flow through orifice [43][44][45][46] and determination of flow rate through wellhead choke [18,[47][48][49][50][51][52][53]. Predicting multiphase flow rate from wellhead chokes is the subject of other studies on machine learning application in flow measurement concepts.…”

Flow measurement for CCUS applications

“…Coriolis flowmeters (Fig. 6) are based on the principle of measuring the phase difference of mechanical oscillations of U-shaped tubes through which the medium is moving 27,28 and allow measurements to be made with an accuracy of less than ±0.1%. 29 The phase shift is proportional to the mass flow rate.…”