Advanced supervision of oil wells based on soft computing techniques

• Autorzy: Camargo E. , Aguilar J.

Identyfikatory

DOI

10.1515/jaiscr-2015-0010

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

intelligent model of supervision; evolutionary computation; fuzzy system; oil industry; operational diagnosis; petroleum wells; gas lift method; multilayer fuzzy system; genetic algorithm

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 3
• Strony: 215--225
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Camargo E.

• PDVSA. Edificio El Menito, LSAI Lagunillas, Edo Zulia-Venezuela

autor

Aguilar J.

• Universidad de Los Andes, CEMISID, Mrida, 5101, Venezuela Prometeo Researcher, Universidad Tcnica Particular de Loja, Ecuador

Bibliografia

• [1] Edgar Camargo, Jose Aguilar, Addison Ros, Francklin Rivas, Joseph Aguilar-Martn, “Un modelo 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.
• [2] 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.
• [3] 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.
• [4] Robles O, Romn, R., “Un Modelo de Programacin No Lineal para la Planeacin de la Produccin de Gas y Petrleo”. Paper SPE 112186, 2008 Intelligent Energy Conference, Amsterdam, Netherlands.
• [5] Yang, D., Licheng J., Maoguo G. “Adaptive Multi-Objective Optimization Based on Nondominated Solutions” Computational Intelligence, Volume 25, Number 2, 2009.
• [6] Gong, M., L.C.Jiao., H.F.Du. “Multiobjective innmure algorithm with nondominated nieghbordbased selection” Evolutionary Computation, Volume 16, pp. 225-255, 2008.
• [7] Shahab, D,M., ”Recent Development in Application of Artificial Intelligence in Petroleum Engineering”. paper SPE 89033. Society of Petroleum Engineers. 2005.
• [8] Popa A., Ramos R., Cover A., “Integration of Artificial Intelligence and Lean Sigma for Large-Field Production Optimization: Application to Kern River Field”, Paper SPE 97247, pp. 34-45, 2005.
• [9] Cordero, S., Moreno, F. “Una Herramienta de Induccin de Sistemas Difuso Jerrquicos”. XV Congreso Espaol sobre Tecnologas y Lgica Difusa. Huelva (Spain), pp. 249-254. Feb. 2010.
• [10] 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.