<?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>0016-7169</journal-id>
<journal-title><![CDATA[Geofísica internacional]]></journal-title>
<abbrev-journal-title><![CDATA[Geofís. Intl]]></abbrev-journal-title>
<issn>0016-7169</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Geofísica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0016-71692025000301677</article-id>
<article-id pub-id-type="doi">10.22201/igeof.2954436xe.2025.64.3.1830</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Prediction of permeability and effective porosity values using ANN in Maleh field]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nassani]]></surname>
<given-names><![CDATA[Mohammed Essa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alaji]]></surname>
<given-names><![CDATA[Ali]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Damascus University Faculty of Science Department of Geology]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Syrian Arab Republic</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Damascus University Faculty of Science Department of Geology]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Syrian Arab Republic</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<volume>64</volume>
<numero>3</numero>
<fpage>1677</fpage>
<lpage>1689</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0016-71692025000301677&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0016-71692025000301677&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0016-71692025000301677&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This study presents the development of an intelligent system designed to predict permeability and effective porosity in wells where core samples are unavailable. An artificial neural network (ANN) was constructed with three hidden layers&#8212;comprising 15, 10, and 4 neurons, respectively&#8212;utilizing well logging parameters (CAL, VCL, NPHI, RHOB, DT) as inputs. The ANN outputs predicted permeability and effective porosity values with remarkable accuracy. The network was optimized with a learning rate of 0.05, a momentum coefficient of 0.95, and the LOGSIG activation function, applied across layers. Input values were normalized to the range of 0 to 1, and training was performed using the sequential forward backpropagation algorithm (newcf). The training phase achieved a minimum mean square error of 0.00001 within 58 seconds over 12,000 cycles, delivering a 100% recognition rate for the training data. The ANN was tested on independent data and demonstrated exceptional performance, achieving 96% accuracy for effective porosity and 98% for permeability predictions in sandstone formations. This efficient algorithm eliminates the need for core sample analysis, reducing costs and time while improving prediction reliability, making it a valuable tool for subsurface characterization and resource exploration.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este estudio presenta el desarrollo de un sistema inteligente diseñado para predecir la permeabilidad y la porosidad efectiva en pozos donde no se dispone de muestras de núcleo. Se construyó una red neuronal artificial (ANN) con tres capas ocultas, que consisten en 15, 10 y 4 neuronas, respectivamente, utilizando parámetros de registro de pozos (CAL, VCL, NPHI, RHOB, DT) como entradas. La ANN genera valores predichos de permeabilidad y porosidad efectiva con una precisión notable. La red se optimizó con una tasa de aprendizaje de 0.05, un coeficiente de momento de 0.95 y la función de activación LOGSIG aplicada en las capas. Los valores de entrada se normalizaron en un rango de 0 a 1, y el entrenamiento se realizó utilizando el algoritmo de retropropagación secuencial (newcf). La fase de entrenamiento logró un error cuadrático medio mínimo de 0.00001 en 58 segundos durante 12,000 ciclos, alcanzando una tasa de reconocimiento del 100% en los datos de entrenamiento. La ANN se probó con datos independientes y mostró un rendimiento excepcional, logrando una precisión del 96% para las predicciones de porosidad efectiva y del 98% para las estimaciones de permeabilidad en formaciones de arenisca. Este algoritmo eficiente elimina la necesidad de análisis de muestras de núcleo, reduce los costos y el tiempo, y mejora la fiabilidad de las predicciones, convirtiéndose en una herramienta valiosa para la caracterización del subsuelo y la exploración de recursos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Maleh Field]]></kwd>
<kwd lng="en"><![CDATA[well logging]]></kwd>
<kwd lng="en"><![CDATA[Permeability]]></kwd>
<kwd lng="en"><![CDATA[effective porosity]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Cores]]></kwd>
<kwd lng="en"><![CDATA[Artificial Neural Network (ANN)]]></kwd>
<kwd lng="es"><![CDATA[Campo Maleh]]></kwd>
<kwd lng="es"><![CDATA[registro de pozos]]></kwd>
<kwd lng="es"><![CDATA[permeabilidad]]></kwd>
<kwd lng="es"><![CDATA[porosidad]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[núcleos]]></kwd>
<kwd lng="es"><![CDATA[red neuronal artificial (ANN)]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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