<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0122-5383</journal-id>
<journal-title><![CDATA[CT&F - Ciencia, Tecnología y Futuro]]></journal-title>
<abbrev-journal-title><![CDATA[C.T.F Cienc. Tecnol. Futuro]]></abbrev-journal-title>
<issn>0122-5383</issn>
<publisher>
<publisher-name><![CDATA[Instituto Colombiano del Petróleo (ICP) - ECOPETROL S.A.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0122-53832019000100027</article-id>
<article-id pub-id-type="doi">10.29047/01225383.149</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[COMBINED ARTIFICIAL INTELLIGENCE MODELING FOR PRODUCTION FORECAST IN AN OIL FIELD]]></article-title>
<article-title xml:lang="es"><![CDATA[MODELAMIENTO COMBINADO EMPLEANDO INTELIGENCIA ARTIFICIAL PARA EL PRONÓSTICO DE PRODUCCIÓN EN UN CAMPO DE PETRÓLEO]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marco]]></surname>
<given-names><![CDATA[Ruiz,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guillermo]]></surname>
<given-names><![CDATA[Alzate-Espinosa,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Andrés]]></surname>
<given-names><![CDATA[Obando,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernán]]></surname>
<given-names><![CDATA[Alvarez,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas Grupo de Investigación de Yacimientos de hidrocarburos]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas Grupo de Investigación de Geomecánica Aplicada- GIGA]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas Grupo de Investigación en Procesos Dinámicos - KALMAN]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>9</volume>
<numero>1</numero>
<fpage>27</fpage>
<lpage>35</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0122-53832019000100027&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0122-53832019000100027&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0122-53832019000100027&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT 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.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Este artículo presenta los resultados en el uso de una metodología que combina dos modelos de Inteligencia Artificial (IA) para predecir la producción de crudo, agua y gas en un campo petrolero colombiano. Al combinar lógica borrosa (LB) y las redes neuronales artificiales (RNA), se implementa un nuevo procedimiento de minería de datos, que incluye una estrategia de imputación de datos. La herramienta de LB determina las variables o parámetros más útiles para incluir en cada modelo de producción de pozo. Mientras que después los modelos predictivos de RNA y sistemas de inferencia borrosa (SIB) desarrollan la minería de datos. Los modelos SID son capaces de predecir comportamientos específicos, mientras que los modelos RNA son capaces de predecir el comportamiento promedio. El uso combinado de ambas herramientas con pocos pasos iterativos, permite una mejor previsión del comportamiento del pozo hasta alcanzar un nivel de precisión específico. El procedimiento de imputación de datos propuesto es el elemento clave para corregir elementos falsos o para completar posiciones vacías en los datos operacionales empleados para identificar modelos para un campo de producción de petróleo característico. Finalmente se obtuvieron dos modelos para cada producto de pozo, conformando una herramienta interesante dada la mejor predicción precisa de la producción en fase fluida.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Forecasting]]></kwd>
<kwd lng="en"><![CDATA[Oil production modeling]]></kwd>
<kwd lng="en"><![CDATA[Data mining]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[Previsión]]></kwd>
<kwd lng="es"><![CDATA[Producción de crudo]]></kwd>
<kwd lng="es"><![CDATA[Modelamiento]]></kwd>
<kwd lng="es"><![CDATA[Minería de datos]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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