State Space Estimator for Plunger Lift

The aim of this paper is to present a State Estimator for Plunger Lift wells based on the Extended Kalman Filter (EKF) algorithm. The state estimator is a joint operation of the Plunger Lift (PL) dynamic model in State Space approach and EKF algorithm. The model is constituted by a set of discrete differential algebraic equations (DAEs) discretized and modeled in the form of equations in state space taking into account the measurement signals in the presence of noise. The state estimator is able to process the measurement signal thus providing estimates of the state variables that in this problem are slug velocity and casinghead pressure. The results of the computational simulation performed with data from a real well are predictions of important well performance items such as production rate, average piston upward velocity, cycle time, and pressures with values very close to those found in the well. The State Estimator for Plunger Lift is therefore a robust prediction system for the variables that influence the performance of a PL well since it considers the noises present in the process and in the measurement. The quality of the prediction is influenced by the correct adjustment of the parameters of the EKF algorithm.