Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2018-01-24T12:15:33Z No. of bitstreams: 1 OsmarDeAraujoDouradoJunior_TESE.pdf: 3621125 bytes, checksum: ac4c7dbcd5830c2fa16d1e8be1ae3ee1 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2018-01-26T14:00:19Z (GMT) No. of bitstreams: 1 OsmarDeAraujoDouradoJunior_TESE.pdf: 3621125 bytes, checksum: ac4c7dbcd5830c2fa16d1e8be1ae3ee1 (MD5) / Made available in DSpace on 2018-01-26T14:00:19Z (GMT). No. of bitstreams: 1 OsmarDeAraujoDouradoJunior_TESE.pdf: 3621125 bytes, checksum: ac4c7dbcd5830c2fa16d1e8be1ae3ee1 (MD5) Previous issue date: 2017-06-19 / O objetivo desta Tese ? apresentar um Estimador de Estado para po?os de Plunger Lift com base no algoritmo Extended Kalman Filter (EKF). O estimador de estado ? uma opera??o conjunta da aplica??o para o modelo din?mico de Plunger Lift (PL) na abordagem de Espa?o de Estados e algoritmo EKF. O modelo ? constitu?do por um conjunto de equa??es diferenciais e alg?bricas (DAEs) discretas e modeladas na forma de equa??es no espa?o de estados, levando em conta os sinais de medi??o na presen?a de ru?do. O algoritmo EKF ? aplicado ao modelo de espa?o de estado, resultando num estimador de estado capaz de processar o sinal de medi??o, proporcionando assim estimativas das vari?veis de estado, que neste problema s?o a velocidade da golfada e a press?o no topo do revestimento. A simula??o computacional realizada com dados de um po?o real ? apresentada e os resultados mostraram que o estimador de estados proposto ? capaz de fornecer predi??es para po?os de petr?leo operados por PL. / 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 casinghead 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.