<?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>S0122-53832025000100077</article-id>
<article-id pub-id-type="doi">10.29047/01225383.1756</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[ARTIFICIAL NEURAL NETWORK APPLICATIONS FOR OPTIMIZING WATER ALTERNATING GAS INJECTION: A COMPREHENSIVE REVIEW]]></article-title>
<article-title xml:lang="es"><![CDATA[APLICACIONES DE REDES NEURONALES ARTIFICIALES PARA OPTIMIZAR LA INYECCIÓN DE AGUA ALTERNADA CON GAS: UNA REVISIÓN INTEGRAL]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Saadallah Ogaidi]]></surname>
<given-names><![CDATA[Adil Raad]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Xiao]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Samba]]></surname>
<given-names><![CDATA[Mohammed A]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Southwest Petroleum University School of Petroleum and Natural Gas Engineering ]]></institution>
<addr-line><![CDATA[Chengdu ]]></addr-line>
<country>China</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Southwest Petroleum University School of Petroleum and Natural Gas Engineering ]]></institution>
<addr-line><![CDATA[Chengdu ]]></addr-line>
<country>China</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Sebha University Petroleum Engineering Department ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Libya</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<volume>15</volume>
<numero>1</numero>
<fpage>77</fpage>
<lpage>90</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0122-53832025000100077&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0122-53832025000100077&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0122-53832025000100077&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The Water Alternating Gas (WAG) process is a cyclic method of injecting alternating cycles of gas followed by water, repeating this process over multiple cycles. However, decision-making during the WAG process often involves critical choices that, at times, fall short of providing accurate and effective results. In recent years, the advent of Artificial Neural Networks (ANNs) has opened up promising opportunities to revamp Enhanced Oil Recovery (EOR) processes. This comprehensive review examines ANN-based methodologies for predicting performance of WAG injection projects. It starts with an overview of the WAG injection process, outlining its relevance in enhanced oil recovery. Subsequently, the study explores the architecture and complexity of ANNs, providing foundational insights into their functionality. The application of ANNs in developing predictive models for WAG injection is then analyzed, emphasizing their potential to enhance accuracy in forecasting WAG performance. Key aspects discussed include ANN-based WAG injection across various gas and rock types, the functions used in ANN WAG modeling, and the algorithms employed in ANN-based WAG simulations. Furthermore, the review highlights the challenges associated to implementing ANN-based predictive models in WAG projects. By synthesizing existing research, this study intends to provide valuable insights for petroleum engineers, particularly in understanding and applying ANN models for optimizing WAG injection strategies.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La inyección de Agua Alternada con Gas (WAG, por sus siglas en inglés) es un método cíclico que consiste en inyectar gas y agua de manera alternada, repitiendo este proceso a lo largo de múltiples ciclos. Sin embargo, la toma de decisiones durante el proceso WAG suele implicar elecciones críticas que, en ocasiones, no logran proporcionar resultados precisos y eficaces. En los últimos años, el desarrollo de las Redes Neuronales Artificiales (ANN) ha abierto oportunidades prometedoras para transformar los procesos de Recuperación Mejorada de Petróleo (EOR). Esta revisión integral examina metodologías basadas en ANN para predecir el desempeño de proyectos de inyección WAG. El estudio comienza con una visión general del proceso de inyección WAG, destacando su relevancia en la recuperación mejorada de petróleo. Posteriormente, se explora la arquitectura y complejidad de las ANN, proporcionando una base conceptual sobre su funcionamiento, A continuación, se analiza la aplicación de ANN en el desarrollo de modelos predictivos para la inyección WAG, enfatizando su potencial para mejorar la precisión en el pronóstico del rendimiento del proceso. Los aspectos clave discutidos incluyen la aplicación de ANN en la inyección WAG considerando diferentes tipos de gas y roca, las funciones utilizadas en el modelado ANN de WAG y los algoritmos empleados en las simulaciones basadas en ANN. Además, esta revisión destaca los desafíos asociados con la implementación de modelos predictivos basados en ANN en proyectos WAG. Al sintetizar investigaciones existentes, este estudio tiene como objetivo proporcionar conocimientos valiosos a los ingenieros petroleros, especialmente para comprender y aplicar modelos ANN destinados a optimizar las estrategias de inyección WAG.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[WAG-related terms: Enhanced Oil Recovery]]></kwd>
<kwd lng="en"><![CDATA[Water Alternating Gas]]></kwd>
<kwd lng="en"><![CDATA[WAG Prediction Models]]></kwd>
<kwd lng="en"><![CDATA[ANN-related terms: Artificial Neural Network]]></kwd>
<kwd lng="en"><![CDATA[ANN Applications in WAG]]></kwd>
<kwd lng="es"><![CDATA[Términos relacionados con WAG: Recuperación Mejorada de Petróleo (EOR)]]></kwd>
<kwd lng="es"><![CDATA[Agua Alternada con Gas (WAG)]]></kwd>
<kwd lng="es"><![CDATA[Modelos de Predicción WAG]]></kwd>
<kwd lng="es"><![CDATA[Términos relacionados con ANN: Redes Neuronales Artificiales (ANN)]]></kwd>
<kwd lng="es"><![CDATA[Aplicaciones de ANN en WAG]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Afzali]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Mathematical modeling and simulation of water-alternating-gas (WAG) injection]]></source>
<year>2021</year>
<publisher-name><![CDATA[Memorial University of Newfoundland]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Al-Haboob]]></surname>
<given-names><![CDATA[Z. I. M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Calibrating the Todd and sized slug WAG injection for application and optimisation on a field scale]]></source>
<year>2019</year>
<publisher-name><![CDATA[Heriot-Watt University Edinburgh, UK]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Al-Mudhafar]]></surname>
<given-names><![CDATA[W. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Rao]]></surname>
<given-names><![CDATA[D. N.]]></given-names>
</name>
<name>
<surname><![CDATA[McCreery]]></surname>
<given-names><![CDATA[E. B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Evaluation of immiscible CO2 enhance oil recovery through the CGI, WAG, and GAGD processes in South Rumaila oil field]]></article-title>
<source><![CDATA[79th EAGE Conference and Exhibition]]></source>
<year>2017</year>
<page-range>1-5</page-range><publisher-name><![CDATA[European Association of Geoscientists &amp; Engineers]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Al-Mudhafar]]></surname>
<given-names><![CDATA[W. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Rao]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Lessons learned from the field-scale simulation of the gas-assisted gravity drainage GAGD process in heterogeneous sandstone oil reservoirs]]></article-title>
<source><![CDATA[SPE Western Regional Meeting]]></source>
<year>2017</year>
<publisher-name><![CDATA[Society of Petroleum Engineers]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Amar]]></surname>
<given-names><![CDATA[M. N.]]></given-names>
</name>
<name>
<surname><![CDATA[Jahanbani Ghahfarokhi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ng]]></surname>
<given-names><![CDATA[C. S. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Zeraibi]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Optimization of WAG in real geological field using rigorous soft computing techniques and nature-inspired algorithms]]></article-title>
<source><![CDATA[Journal of Petroleum Science and Engineering]]></source>
<year>2021</year>
<volume>206</volume>
<page-range>109038</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Amar]]></surname>
<given-names><![CDATA[M. N.]]></given-names>
</name>
<name>
<surname><![CDATA[Zeraibi]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Jahanbani Ghahfarokhi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Applying hybrid support vector regression and genetic algorithm to water alternating CO2 gas EOR]]></article-title>
<source><![CDATA[Greenhouse Gases: Science and Technology]]></source>
<year>2020</year>
<volume>10</volume>
<page-range>613-30</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ampomah]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Acheampong]]></surname>
<given-names><![CDATA[S. A.]]></given-names>
</name>
<name>
<surname><![CDATA[McMillan]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Bratton]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[El-Kaseeh]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Development of a machine learning assisted framework in calibrating in-situ stress changes through time-lapse vertical seismic profiling]]></article-title>
<source><![CDATA[Proceedings of the 16th Greenhouse Gas Control Technologies Conference (GHGT-16)]]></source>
<year>2022</year>
<page-range>23-4</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ampomah]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Balch]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Will]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Cather]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Gunda]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Dai]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Co-optimization of CO2-EOR and storage processes under geological uncertainty]]></article-title>
<source><![CDATA[Energy Procedia]]></source>
<year>2017</year>
<volume>114</volume>
<page-range>6928-41</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Amr]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[El-Shafie]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[El Mazoghi]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Shehata]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Taha]]></surname>
<given-names><![CDATA[M. R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt]]></article-title>
<source><![CDATA[International Journal of Physical Sciences]]></source>
<year>2011</year>
<volume>6</volume>
<page-range>1306-16</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Andersen]]></surname>
<given-names><![CDATA[P. O.]]></given-names>
</name>
<name>
<surname><![CDATA[Nygârd]]></surname>
<given-names><![CDATA[J. I.]]></given-names>
</name>
<name>
<surname><![CDATA[Kengessova]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prediction of oil recovery factor in stratified reservoirs after immiscible water-alternating gas injection based on PSO-, GSA-, GWO-, and GA-LSSVM]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2022</year>
<volume>15</volume>
<page-range>656</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Arbib]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[The handbook of brain theory and neural networks]]></source>
<year>2003</year>
<publisher-name><![CDATA[MIT Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Asante]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ampomah]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Tu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Cather]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Data-driven modeling for forecasting oil recovery: A time-series neural network approach for tertiary CO2 WAG EOR]]></article-title>
<source><![CDATA[Geoenergy Science and Engineering]]></source>
<year>2024</year>
<volume>233</volume>
<page-range>212555</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Belazreg]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Mahmood]]></surname>
<given-names><![CDATA[S. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned]]></article-title>
<source><![CDATA[Journal of Petroleum Exploration and Production Technology]]></source>
<year>2020</year>
<volume>10</volume>
<page-range>249-69</page-range></nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Belazreg]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Mahmood]]></surname>
<given-names><![CDATA[S. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Aulia]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fast and cost-effective mathematical models for hydrocarbon-immiscible water alternating gas incremental recovery factor prediction]]></article-title>
<source><![CDATA[ACS Omega]]></source>
<year>2021</year>
<volume>6</volume>
<page-range>17492-500</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Caudle]]></surname>
<given-names><![CDATA[B. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Dyes]]></surname>
<given-names><![CDATA[A. B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Improving miscible displacement by gas-water injection]]></article-title>
<source><![CDATA[Transactions of the AIME]]></source>
<year>1958</year>
<volume>213</volume>
<page-range>281-3</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Application of artificial neural networks in CO2 flooding scenario analysis]]></article-title>
<source><![CDATA[Energy &amp; Fuels]]></source>
<year>2020</year>
<volume>34</volume>
<page-range>625</page-range></nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Christensen]]></surname>
<given-names><![CDATA[J. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Stenby]]></surname>
<given-names><![CDATA[E. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Skauge]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Review of WAG field experience]]></article-title>
<source><![CDATA[SPE Reservoir Evaluation &amp; Engineering]]></source>
<year>2001</year>
<volume>4</volume>
<page-range>97-106</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dharma]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Hassan]]></surname>
<given-names><![CDATA[M. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Ong]]></surname>
<given-names><![CDATA[H. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Sebayang]]></surname>
<given-names><![CDATA[A. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Silitonga]]></surname>
<given-names><![CDATA[A. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Kusumo]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Milano]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Experimental study and prediction of the performance and exhaust emissions of mixed Jatropha curcas-Ceiba pentandra biodiesel blends in diesel engine using artificial neural networks]]></article-title>
<source><![CDATA[Journal of Cleaner Production]]></source>
<year>2017</year>
<volume>164</volume>
<page-range>618-33</page-range></nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Salasakar]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Thakur]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A comprehensive summary of the application of machine learning techniques for CO2-enhanced oil recovery projects]]></article-title>
<source><![CDATA[Machine Learning and Knowledge Extraction]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Elsharafi]]></surname>
<given-names><![CDATA[M. O.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Literature review of water alternation gas injection]]></article-title>
<source><![CDATA[Journal of Earth Energy Engineering]]></source>
<year>2018</year>
<volume>7</volume>
<page-range>33-45</page-range></nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Enab]]></surname>
<given-names><![CDATA[K. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Artificial neural network based design protocol for WAG implementation in CO2 injection using fishbone wells in low permeability oil reservoirs]]></source>
<year>2018</year>
</nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Enab]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Ertekin]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Screening and optimization of CO2-WAG injection and fish-bone well structures in low permeability reservoirs using artificial neural network]]></article-title>
<source><![CDATA[Journal of Petroleum Science and Engineering]]></source>
<year>2021</year>
<volume>200</volume>
<page-range>108268</page-range></nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ertekin]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence applications in reservoir engineering: A status check]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2019</year>
<volume>12</volume>
<page-range>2897</page-range></nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Esene]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Zendehboudi]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Shiri]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Aborig]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deterministic tools to predict recovery performance of carbonated water injection]]></article-title>
<source><![CDATA[Journal of Molecular Liquids]]></source>
<year>2020</year>
<volume>301</volume>
<page-range>111911</page-range></nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Qian]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Yin]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Cao]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine-learning-based approach to optimize CO2-WAG flooding in low permeability oil reservoirs]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2023</year>
<volume>16</volume>
<page-range>6149</page-range></nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hasanzadeh]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Madani]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deterministic tools to predict gas assisted gravity drainage recovery factor]]></article-title>
<source><![CDATA[Energy Geoscience]]></source>
<year>2024</year>
<volume>5</volume>
<page-range>100267</page-range></nlm-citation>
</ref>
<ref id="B27">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hung]]></surname>
<given-names><![CDATA[V. T.]]></given-names>
</name>
</person-group>
<source><![CDATA[Integrated reservoir modelling workflow and optimization framework for CO2 sequestration using water alternating gas process under geological uncertainties]]></source>
<year>2020</year>
<publisher-name><![CDATA[Kyushu University]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B28">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jaturakhanawanit]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Wannakomol]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Water alternating gas injection for enhanced oil recovery in the Phitsanulok Basin]]></article-title>
<source><![CDATA[Suranaree Journal of Science &amp; Technology]]></source>
<year>2011</year>
<volume>18</volume>
<page-range>267</page-range></nlm-citation>
</ref>
<ref id="B29">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kengessova]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Prediction efficiency of immiscible water alternating gas performance by LSSVM-PSO algorithms]]></source>
<year>2020</year>
<publisher-name><![CDATA[University of Stavanger]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B30">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kenig]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ben-David]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Omer]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Sadeh]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Control of properties in injection molding by neural networks]]></article-title>
<source><![CDATA[Engineering Applications of Artificial Intelligence]]></source>
<year>2001</year>
<volume>14</volume>
<page-range>819-23</page-range></nlm-citation>
</ref>
<ref id="B31">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lv]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Tian]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Dong]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Method for potential evaluation and parameter optimization for CO2-WAG in low permeability reservoirs based on machine learning]]></article-title>
<source><![CDATA[IOP Conference Series: Earth and Environmental Science]]></source>
<year>2021</year>
<volume>651</volume>
<page-range>032038</page-range></nlm-citation>
</ref>
<ref id="B32">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Jaehyun]]></given-names>
</name>
</person-group>
<source><![CDATA[Design of an Effective Water-Alternating-Gas (WAG) Injection Process Using Artificial Expert Systems]]></source>
<year>2010</year>
</nlm-citation>
</ref>
<ref id="B33">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Massoud]]></surname>
<given-names><![CDATA[M. A. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Amar]]></surname>
<given-names><![CDATA[I. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Literature review of nanotechnology in the enhanced oil recovery]]></article-title>
<source><![CDATA[Journal of Engineering Research]]></source>
<year>2022</year>
</nlm-citation>
</ref>
<ref id="B34">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nait Amar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Zeraibi]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Redouane]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization]]></article-title>
<source><![CDATA[Arabian Journal for Science and Engineering]]></source>
<year>2018</year>
<volume>43</volume>
<page-range>6399-412</page-range></nlm-citation>
</ref>
<ref id="B35">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Negash]]></surname>
<given-names><![CDATA[B. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Tufa]]></surname>
<given-names><![CDATA[L. D.]]></given-names>
</name>
<name>
<surname><![CDATA[Ramasamy]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[System identification based proxy modeling of a reservoir under IWAG]]></article-title>
<source><![CDATA[IOP Conference Series: Materials Science and Engineering]]></source>
<year>2018</year>
<volume>458</volume>
<page-range>012055</page-range></nlm-citation>
</ref>
<ref id="B36">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ogbeiwi]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Stephen]]></surname>
<given-names><![CDATA[K. D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Optimising the value of a CO2 water-alternating-gas injection project under geological and economic uncertainties using an improved artificial neural network]]></article-title>
<source><![CDATA[Preprint]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B37">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Qin]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Bi]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Xiang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Research on displacement efficiency by injecting CO2 in shale reservoirs based on a genetic neural network model]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B38">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rahmanifard]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Plaksina]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Application of artificial intelligence techniques in the petroleum industry: A review]]></article-title>
<source><![CDATA[Artificial Intelligence Review]]></source>
<year>2019</year>
<volume>52</volume>
<page-range>2295-318</page-range></nlm-citation>
</ref>
<ref id="B39">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Samba]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Syihab]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<source><![CDATA[Development of a predictive model for EOR water alternating gas injection using proxy model derived from artificial neural network model]]></source>
<year>2014</year>
</nlm-citation>
</ref>
<ref id="B40">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Samba]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Aldokali]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Elsharaf]]></surname>
<given-names><![CDATA[M. O.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A new EOR technology: Gas alternating gas injection]]></article-title>
<source><![CDATA[Journal of Earth Energy Engineering]]></source>
<year>2019</year>
<volume>8</volume>
<page-range>27-32</page-range></nlm-citation>
</ref>
<ref id="B41">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Schneider]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A miscible WAG project using horizontal wells in a mature, offshore, carbonate Middle East reservoir]]></article-title>
<source><![CDATA[SPE Middle East Oil and Gas Show and Conference]]></source>
<year>2005</year>
<publisher-name><![CDATA[Society of Petroleum Engineers]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B42">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Smith]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial neural network-based predictive modeling for WAG injection optimization]]></article-title>
<source><![CDATA[Journal of Petroleum Science and Engineering]]></source>
<year>2018</year>
<volume>162</volume>
<page-range>123-31</page-range></nlm-citation>
</ref>
<ref id="B43">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Song]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Sung]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Jang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Jung]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Application of an artificial neural network in predicting the effectiveness of trapping mechanisms on CO2 sequestration in saline aquifers]]></article-title>
<source><![CDATA[International Journal of Greenhouse Gas Control]]></source>
<year>2020</year>
<volume>98</volume>
<page-range>103042</page-range></nlm-citation>
</ref>
<ref id="B44">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sulak]]></surname>
<given-names><![CDATA[R. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Ekofisk Field: The first 20 years]]></article-title>
<source><![CDATA[Journal of Petroleum Technology]]></source>
<year>1991</year>
<volume>43</volume>
<page-range>1265-71</page-range></nlm-citation>
</ref>
<ref id="B45">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Ampomah]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[You]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Cather]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Balch]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Practical CO2-WAG field operational designs using hybrid numerical-machine-learning approaches]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2021</year>
<volume>14</volume>
<page-range>1055</page-range></nlm-citation>
</ref>
<ref id="B46">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[van Assen]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Muscogiuri]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Tessarin]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[De Cecco]]></surname>
<given-names><![CDATA[C. N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence: A century-old story]]></article-title>
<source><![CDATA[Artificial intelligence in cardiothoracic imaging (Contemporary Medical Imaging)]]></source>
<year>2022</year>
<publisher-name><![CDATA[Humana, Cham]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B47">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Van]]></surname>
<given-names><![CDATA[S. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Chon]]></surname>
<given-names><![CDATA[B. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Evaluating the critical performances of a CO2-enhanced oil recovery process using artificial neural network models]]></article-title>
<source><![CDATA[Journal of Petroleum Science and Engineering]]></source>
<year>2017</year>
<volume>157</volume>
<page-range>207-22</page-range></nlm-citation>
</ref>
<ref id="B48">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wei]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Dong]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Experimental study on oil recovery mechanism of CO2 associated enhancing oil recovery methods in low permeability reservoirs]]></article-title>
<source><![CDATA[Journal of Petroleum Science and Engineering]]></source>
<year>2021</year>
<volume>197</volume>
<page-range>108047</page-range></nlm-citation>
</ref>
<ref id="B49">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wojnicki]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Lubas]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Warnecki]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Kusnierczyk]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Szuflita]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Experimental studies of immiscible high-nitrogen natural gas WAG injection efficiency in mixed-wet carbonate reservoir]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2020</year>
<volume>13</volume>
<page-range>2346</page-range></nlm-citation>
</ref>
<ref id="B50">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Yue]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Shao]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Monitoring of flooding characteristics with different methane gas injection methods in low-permeability heterogeneous cores]]></article-title>
<source><![CDATA[Energy &amp; Fuels]]></source>
<year>2021</year>
<volume>35</volume>
<page-range>3208-18</page-range></nlm-citation>
</ref>
<ref id="B51">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yazdani]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Vaziri]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Sedaee Sola]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Performance of water injection and CO2 injection into oil reservoirs based on field data: Using ANNs to predict in the selected scenario]]></article-title>
<source><![CDATA[Journal of Petroleum Science and Technology]]></source>
<year>2022</year>
<volume>12</volume>
<page-range>2-11</page-range></nlm-citation>
</ref>
<ref id="B52">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[You]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ampomah]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework]]></article-title>
<source><![CDATA[Applied Energy]]></source>
<year>2020</year>
<volume>279</volume>
<page-range>115695</page-range></nlm-citation>
</ref>
<ref id="B53">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[You]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ampomah]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Development and application of a machine learning based multi-objective optimization workflow for CO2-EOR projects]]></article-title>
<source><![CDATA[Fuel]]></source>
<year>2020</year>
<volume>264</volume>
<page-range>116758</page-range></nlm-citation>
</ref>
<ref id="B54">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yousef]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Kavousi Ghahfarokhi]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Alnuaimi]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Alatrach]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East]]></article-title>
<source><![CDATA[Petroleum Exploration and Development]]></source>
<year>2020</year>
<volume>47</volume>
<page-range>393-9</page-range></nlm-citation>
</ref>
<ref id="B55">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zarringhalam]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Alizadeh]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Rafiee]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Moshirfarahi]]></surname>
<given-names><![CDATA[M. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Neural network prediction model of three-phase fluids flow in heterogeneous porous media using scaling analysis]]></article-title>
<source><![CDATA[Journal of Petroleum Science and Engineering]]></source>
<year>2016</year>
<volume>138</volume>
<page-range>122-37</page-range></nlm-citation>
</ref>
<ref id="B56">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[G. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Patuwo]]></surname>
<given-names><![CDATA[B. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[M. Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A simulation study of artificial neural networks for nonlinear time-series forecasting]]></article-title>
<source><![CDATA[Computers &amp; Operations Research]]></source>
<year>2001</year>
<volume>28</volume>
<page-range>381-96</page-range></nlm-citation>
</ref>
<ref id="B57">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Feng]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimation of CO2-brine interfacial tension using an artificial neural network]]></article-title>
<source><![CDATA[The Journal of Supercritical Fluids]]></source>
<year>2016</year>
<volume>107</volume>
<page-range>31-7</page-range></nlm-citation>
</ref>
<ref id="B58">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhong]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Carr]]></surname>
<given-names><![CDATA[T. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA]]></article-title>
<source><![CDATA[Geophysics]]></source>
<year>2019</year>
<volume>84</volume>
<page-range>B363-73</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
