<?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>0120-0283</journal-id>
<journal-title><![CDATA[Boletín de Geología]]></journal-title>
<abbrev-journal-title><![CDATA[Bol. geol.]]></abbrev-journal-title>
<issn>0120-0283</issn>
<publisher>
<publisher-name><![CDATA[Universidad Industrial de Santander]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-02832020000300141</article-id>
<article-id pub-id-type="doi">10.18273/revbol.v42n3-2020006</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Porosity prediction from X-ray computed tomography logs (RHOB and PEF) using Artificial Neural Networks (ANN)]]></article-title>
<article-title xml:lang="es"><![CDATA[Predicción de la porosidad a partir de registros de tomografía computarizada de rayos X (RHOB y PEF) usando Redes Neuronales Artificiales (ANN)]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz]]></surname>
<given-names><![CDATA[Andrés Felipe]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernando Herrera]]></surname>
<given-names><![CDATA[Edwar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Santos]]></surname>
<given-names><![CDATA[Nicolás]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Industrial de Santander Escuela de Ingeniería de Petróleos Grupo de Investigación en Tomografía para Caracterización de Yacimientos]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Industrial de Santander Escuela de Ingeniería de Petróleos Grupo de Investigación en Tomografía para Caracterización de Yacimientos]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Industrial de Santander Escuela de Ingeniería de Petróleos Grupo de Investigación en Tomografía para Caracterización de Yacimientos]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>42</volume>
<numero>3</numero>
<fpage>141</fpage>
<lpage>149</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-02832020000300141&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-02832020000300141&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-02832020000300141&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This work presents a method for rock porosity prediction from the X-ray computed tomography (CT) logs obtained using a double energy approach, bulk density (RHOB) and photoelectric factor (PEF). The proposed method seeks to correlate the known porosity from the Routine Core Analysis (RCAL) with RHOB and PEF high-resolution logs, as the response of these two measurements depends on the volumetric quantity of different rock materials and of the volume of its porous space. Artificial Neural Networks (ANNs) are trained so they can predict porosity from CT logs at a high resolution (0.625 mm). The ANNs validation and regression plots show that porosity predictions are good. High-resolution porosity models linked to CT images could contribute to enhancing the petrophysics model as they allow a more refined identification of intervals of interest due to the detailed measurement.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este trabajo presenta un método para la predicción de la porosidad de la roca, a partir de los registros de tomografía computarizada de rayos X (CT) obtenidos mediante un enfoque de doble energía, específicamente, densidad aparente (RHOB) y factor fotoeléctrico (PEF). El método propuesto busca correlacionar la porosidad conocida de los Análisis básicos (RCAL) con los registros de alta resolución RHOB y PEF, ya que la respuesta de estas dos mediciones depende de la cantidad volumétrica de diferentes materiales de roca y del volumen de su espacio poroso. Redes Neuronales Artificiales (ANN) son entrenadas para que puedan predecir la porosidad a partir de estos registros CT a una alta resolución (0,625 mm). Los gráficos de validación y regresión de las ANN muestran que las predicciones de porosidad son buenas. Los modelos de porosidad de alta resolución vinculados a imágenes de CT podrían contribuir a mejorar el modelo de petrofísica del pozo, ya que permiten una identificación más refinada de los intervalos de interés debido a su medición detallada.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[CT]]></kwd>
<kwd lng="en"><![CDATA[Rock images]]></kwd>
<kwd lng="en"><![CDATA[Well logs]]></kwd>
<kwd lng="en"><![CDATA[High-resolution logs]]></kwd>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="es"><![CDATA[CT]]></kwd>
<kwd lng="es"><![CDATA[Imágenes de rocas]]></kwd>
<kwd lng="es"><![CDATA[Registros de pozo]]></kwd>
<kwd lng="es"><![CDATA[Registros de alta resolución]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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