Soft Sensing for Gas-Lift Wells

Abstract

Abstract This paper considers the use of extended Kalman Filtering as a soft-sensing technique for gas-lift wells. This technique is deployed for the estimation of dynamic variables that are not directly measured. Possible applications are the estimation of flow rates from pressure measurements or the estimation of parameters of a drift-flux model. By means of simulation examples different configurations of sensor systems are analyzed. The estimation of drift-flux model parameters is demonstrated on real data from a laboratory set-up.