The aim of this Thesis 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 application for 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. EKF algorithm is applied to the state space model, resulting in a state estimator able to process the measurement signal thus providing estimates of the state variables that in this problem are slug velocity and casing head pressure. The computational simulation performed with data from a real well is presented and the results showed that the state estimator proposed is able to provide predictions for oil wells operated by PL.