Resumen

MARCO, Ruiz,; GUILLERMO, Alzate-Espinosa,; ANDRES, Obando,  y  HERNAN, Alvarez,. COMBINED ARTIFICIAL INTELLIGENCE MODELING FOR PRODUCTION FORECAST IN AN OIL FIELD. C.T.F Cienc. Tecnol. Futuro [online]. 2019, vol.9, n.1, pp.27-35. ISSN 0122-5383.  https://doi.org/10.29047/01225383.149.

This paper presents the results about the use of a methodology that combines two artificial intelligence (AI) models to predict oil, water and gas production in a Colombian oil field. By combining fuzzy logic (FL) and artificial neural networks (ANN), a novelty data mining procedure is implemented, including a data imputation strategy. The FL tool determines the most useful variables or parameters to include in each well production model. ANN and FIS (fuzzy inference systems) predictive models identification is developed after the data mining process. The FIS models are able to predict specific behaviors, while ANN models are able to forecast average behavior. The combined use of both tools with few iterative steps, allows for improved forecasting of well behavior until reaching a specified accuracy level. The proposed data imputation procedure is the key element to correct false items or to complete void positions in the operational data used to identify models for a typical oil production field. At the end, two models are obtained for each well product, conforming an interesting tool given the best accurate prediction of fluid phase production.

Palabras clave : Artificial Intelligence; Forecasting; Oil production modeling; Data mining.

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