Hybrid intelligent supervision model of oil wells

In this work is presented a hybrid intelligent model based on Evolutionary Computation and Fuzzy Systems to improve the performance of the Oil Industry, which is used for Operational Diagnosis in petroleum wells that require gas lift (GL). The model is used for an optimization problem where the objective function is composed by two criteria: maximization of the production of oil and minimization of the flow of gas injection, based on the restrictions of the process and the operational cost of production. We use the genetic algorithms to solve this problem, and the fuzzy logic to identify the operational scenarios in an oil well. In this way, our hybrid intelligent model implements supervision and control tasks.