In this work is presented a hybrid intelligent model of supervision based on Evolutionary Computation and Fuzzy Systems to improve the performance of the Oil Industry, which is used for Operational Diagnosis in petroleum wells based on the gas lift (GL) method. The model is composed by two parts: a Multilayer Fuzzy System to identify the operational scenarios in an oil well and a genetic algorithm to maximize the production of oil and minimize the flow of gas injection, based on the restrictions of the process and the operational cost of production.
Additionally, the first layers of the Multilayer Fuzzy System have specific tasks: the detection of operational failures, and the identification of the rate of gas that the well requires for production. In this way, our hybrid intelligent model implements supervision and control tasks.
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 Edgar Camargo, Jose Aguilar, Addison Ros, Francklin Rivas, Joseph Aguilar-Martn, “Un mod elo de Produccin de Pozos por Levantamiento Artificial utilizando Anlisis Nodal”. Revista Ciencia e Ingeniera. Universidad de los Andes. Vol.30, pp. 23-28.N 1. 2009.
 Edgar Camargo, Jose Aguilar, Addison Ros, Francklin Rivas, Joseph Aguilar-Martn, “Nodal Analysis-based Design for Improving Gas Lift Wells Production”. WSEAS Transactions on Informations Science & Applications. Vol.5, No. 5, pp. 706-715. 2008.
 Edgar Camargo, Jose Aguilar, Addison Ros, Francklin Rivas, Joseph Aguilar-Martn, “A Neo-Fuzzy Approach for Bottom Parameters Estimation in Oil Wells”, WSEAS Transactions on Systems and Control, Vol.4, No. 9, pp. 445-454, 2009.
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 Edgar Camargo, Jos Aguilar, “Hybrid Intelligent Supervision Model of Oil Wells”, Proceedings of the IEEE World Congress on Computational Intelligence (IEEE WCCI), Beijing, China, 2014.