A neo-fuzzy approach for bottom parameters estimation in oil wells

Authors:
Edgar Camargo;Jose Aguilar;Addison Ríos;Francklin Rivas;Joseph Aguilar-Martin
Affiliations:
CEMISID, Facultad de Ingeniería, Universidad de los Andes, Mérida, Venezuela;CEMISID, Facultad de Ingeniería, Universidad de los Andes, Mérida, Venezuela;CEMISID, Facultad de Ingeniería, Universidad de los Andes, Mérida, Venezuela;Laboratorio de Sistemas Inteligentes, Universidad de los Andes, Mérida, Venezuela;Grup SAC, Universitat Politècnica de Catalunya, España
Venue:
WSEAS Transactions on Systems and Control
Year:
2009

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Abstract

In this work, an approach for the bottom parameters estimation in oil wells is presented. It is based on neural networks and fuzzy logic, specifically on the neo-fuzzy-neuron model. We propose a neofuzzy system compose by two neo-fuzzy neurons. For validating the results, the estimation is applied in oil wells based on the artificial gas lift method, using variables of the head of the wells, particularly the gas and production pressures.