期刊名称：Journal of Artificial Intelligence and Soft Computing Research
电子版ISSN：2083-2567
出版年度：2014
卷号：4
期号：3
页码：215-225
DOI：10.1515/jaiscr-2015-0010
出版社：Walter de Gruyter GmbH
摘要：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.
