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Applicability of LAMDA as classification model in the oil production

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Abstract

This work analyzes the utilization of classification models in the context of the oil industry and presents examples of application. Particularly, we analyze three case studies, two to explain the behavior of oil wells that produce via artificial methods (the classification as a descriptive model), and another to predict the oil prices (the classification as a predictive model). The classification technique used in this work is LAMDA-HAD, which is an improvement to the well-known technique called learning algorithm multivariable and data analysis (LAMDA), that has been used in diagnostic tasks. Finally, the results with the descriptive and predictive models are discussed, in order to analyze the importance of the classification in the context of the oil business.

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References

  • Aceros E, Camargo E, Lozada H, Aguilar J (2016) Implantación de un sensor virtual en procesos petroleros. Cuadernos de Mecánica Computacional 14(1):68–77

    Google Scholar 

  • Aguilar J (1998) Definition of an energy function for the random neural to solve optimization problems. Neural Netw 11(4):731–737

    Article  Google Scholar 

  • Aguilar-Martín J, López De Mantaras N (1982) The process of classification and learning the meaning of linguistic descriptors of concepts. North-Holland Publishing Company, Amsterdam

    Google Scholar 

  • Ali J (1994) Neural networks: A new tool for the petroleum industry? In: European petroleum computer conference, pp 217–231

  • Alizadeh A, Mafinezhad K (2010) Monthly Brent oil price forecasting using artificial neural networks and a crisis index. In: Proceedings of the international conference on electronics and information engineering, pp 465–468

  • Barrera V, Carrillo G, Ordóñez G, Mora J (2005) Una Aplicación de la Técnica LAMDA a los Índices de Continuidad del Suministro de Energía Eléctrica. In: Proc. Simposio Internacional sobre la Calidad de la Energía Eléctrica

  • Botía J, Isaza C, Kempowsky T, Le Lann M, Aguilar-Martín J (2013) Automaton based on fuzzy clustering methods for monitoring industrial processes. Eng Appl Artif Intell 26(4):1211–1220

    Article  Google Scholar 

  • Camargo E, Aguilar J (2015) Advanced supervision of oil wells based on soft computing techniques. J Artif Intell Soft Comput Res 4(3):215–225

    Article  Google Scholar 

  • Camargo E, Aguilar J, Ríos A, Rivas F, Aguilar-Martin J (2008) Nodal analysis- based design for improving gas lift wells production. WSEAS Trans Inf Sci Appl 5(5):706–715

    Google Scholar 

  • Camargo E, Aguilar J, Ríos A, Rivas F, Aguilar-Martin J (2009) A neo-fuzzy approach for bottom parameters estimation in oil wells. WSEAS Trans Syst Control 4(9):445–454

    Google Scholar 

  • Hernandez H, Camas L, Medina A, Perez M, Le Lann M (2014) Fault diagnosis by LAMDA methodology applied to drinking water plant. IEEE Lat Am Trans 12(6):985–990

    Article  Google Scholar 

  • Kisswani K, Nusair S (2013) Non-linearities in the dynamics of oil prices. Energy Econ 36:341–353

    Article  Google Scholar 

  • Krivanek V (2011) Application LAMDA algorithm for fault detection and isolation. In: 14th International symposium on MECHATRONIKA, pp 46–51

  • Lamrini B, Le Lann M, Benhammou A, Lakhal E (2005) Detection of functional states by the “LAMDA” classification technique: application to a coagulation process in drinking water treatment. C R Phys 6(10):1161–1168

    Article  Google Scholar 

  • Li H, Guo H, Guo H, Meng Z (2008) Data mining techniques for complex formation evaluation in petroleum exploration and production: a comparison of feature selection and classification methods. In: IEEE Pacific-Asia workshop on computational intelligence and industrial application, pp 37–43

  • Lu S, Huang W, Chen F, Li J, Wang M, Xue H, Wang W, Cai X (2012) Classification and evaluation criteria of shale oil and gas resources: discussion and application. Pet Explor Dev 39(2):268–276

    Article  Google Scholar 

  • Mora-Florez J, Barrera-Nunez V, Carrillo-Caicedo G (2007) Fault location in power distribution systems using a learning algorithm for multivariable data analysis. IEEE Trans Power Deliv 22(3):1715–1721

    Article  Google Scholar 

  • Morales L, Aguilar J, Chavez D, Izasa C (2018) LAMDA-HAD, an extension to the Lamda classifier in the context of supervised learning. Publ Int J Inf Technol Decis Mak 1–26

  • Ronning R (2011) Automatic start-up and control of artificially lifted wells. Technical report, Norwegian University of Science and Technology, Department of Engineering Cybernetics

  • Ruiz F, Isaza C, Agudelo A, Agudelo J (2017) A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines. Eng Appl Artif Intell 60:117–127

    Article  Google Scholar 

  • Sanchez-Pi N, Martí L, Garcia A (2014) Text classification techniques in oil industry applications. In: Herrero Á et al (eds) International joint conference SOCO’13-CISIS’13-ICEUTE’13. Advances in intelligent systems and computing, vol 239

  • Sharma A, Srinivasan S, Lake LW (2010) Classification of oil and gas reservoirs based on recovery factor: a data-mining approach. Society of Petroleum Engineers, London

    Google Scholar 

  • Waissman J, Sarrate R, Escobet T, Aguilar J, Dahhou B (2000) Wastewater treatment process supervision by means of a fuzzy automaton model. In: IEEE international symposium on intelligent control, pp 163–168

  • Zamani M (2004) An econometric forecasting model of short term oil spot price. In: 6th IAEE European energy conference, pp 1–7

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Authors and Affiliations

  1. Departamento de Automatización y Control Industrial, Escuela Politécnica Nacional, Quito, Ecuador

    L. Morales

  2. PDVSA, Línea de Servicio AIT, Maracaibo, Venezuela

    H. Lozada & E. Camargo

  3. CEMISID, Facultad de Ingenieria, Universidad de Los Andes, Mérida, 5101, Venezuela

    H. Lozada & J. Aguilar

Authors
  1. L. Morales
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  2. H. Lozada
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  3. J. Aguilar
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  4. E. Camargo
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Correspondence to J. Aguilar.

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Morales, L., Lozada, H., Aguilar, J. et al. Applicability of LAMDA as classification model in the oil production. Artif Intell Rev 53, 2207–2236 (2020). https://doi.org/10.1007/s10462-019-09731-6

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  • DOI: https://doi.org/10.1007/s10462-019-09731-6

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