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Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR.

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Title: Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR.
Authors: Kardani, Navid1 (AUTHOR), T, Pradeep2 (AUTHOR), Samui, Pijush2 (AUTHOR), Kim, Dookie3 (AUTHOR), Zhou, Annan1 (AUTHOR)
Source: Petroleum Science & Technology. 2021, Vol. 39 Issue 19/20, p804-825. 22p.
Subjects: Asphaltene, GMDH algorithms, Human behavior models
Abstract: Asphaltene precipitation is the reason behind some of the most destructive issues in the oil industry such as wettability alteration, formation plugging, and reduction of the relative permeability. There are different experimental and modeling methods to study the asphaltene phase behavior. However, those approaches are either time-consuming or costly. In addition, the prediction of asphaltene precipitation is challenging due to the nonlinear dependence. Therefore, in this study, three advanced computational algorithms including group method of data handling (GMDH), emotional neural network (ENN), and minimax probability machine regression (MPMR) are developed and applied to the comprehensive dataset to estimate the amount of asphaltene precipitation as the function of temperature, dilution ratio, and the molecular weight of the n-alkanes. Many different performance metrics are used to evaluate the predictive performance of intelligent algorithms. Obtained results of the modeling reveal that intelligent computational algorithms have a great ability to mimic the nonlinear relationships between the target variable and its influential variables. The results indicate MPMR as the best predictive model with the highest R-squared value on both training and testing datasets with values of R2 = 0.992 and R2 = 0.991, respectively. In addition, MPMR is compared and outperformed empirical correlations. [ABSTRACT FROM AUTHOR]
Copyright of Petroleum Science & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR.
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  Data: <searchLink fieldCode="AR" term="%22Kardani%2C+Navid%22">Kardani, Navid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22T%2C+Pradeep%22">T, Pradeep</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Samui%2C+Pijush%22">Samui, Pijush</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Dookie%22">Kim, Dookie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Annan%22">Zhou, Annan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Petroleum+Science+%26+Technology%22">Petroleum Science & Technology</searchLink>. 2021, Vol. 39 Issue 19/20, p804-825. 22p.
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  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Asphaltene%22">Asphaltene</searchLink><br /><searchLink fieldCode="DE" term="%22GMDH+algorithms%22">GMDH algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Human+behavior+models%22">Human behavior models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Asphaltene precipitation is the reason behind some of the most destructive issues in the oil industry such as wettability alteration, formation plugging, and reduction of the relative permeability. There are different experimental and modeling methods to study the asphaltene phase behavior. However, those approaches are either time-consuming or costly. In addition, the prediction of asphaltene precipitation is challenging due to the nonlinear dependence. Therefore, in this study, three advanced computational algorithms including group method of data handling (GMDH), emotional neural network (ENN), and minimax probability machine regression (MPMR) are developed and applied to the comprehensive dataset to estimate the amount of asphaltene precipitation as the function of temperature, dilution ratio, and the molecular weight of the n-alkanes. Many different performance metrics are used to evaluate the predictive performance of intelligent algorithms. Obtained results of the modeling reveal that intelligent computational algorithms have a great ability to mimic the nonlinear relationships between the target variable and its influential variables. The results indicate MPMR as the best predictive model with the highest R-squared value on both training and testing datasets with values of R2 = 0.992 and R2 = 0.991, respectively. In addition, MPMR is compared and outperformed empirical correlations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Petroleum Science & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/10916466.2021.1974882
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 22
        StartPage: 804
    Subjects:
      – SubjectFull: Asphaltene
        Type: general
      – SubjectFull: GMDH algorithms
        Type: general
      – SubjectFull: Human behavior models
        Type: general
    Titles:
      – TitleFull: Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR.
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            NameFull: Kardani, Navid
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            NameFull: T, Pradeep
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            NameFull: Samui, Pijush
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            NameFull: Kim, Dookie
      – PersonEntity:
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            NameFull: Zhou, Annan
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          Dates:
            – D: 01
              M: 10
              Text: 2021
              Type: published
              Y: 2021
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              Value: 10916466
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              Value: 39
            – Type: issue
              Value: 19/20
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            – TitleFull: Petroleum Science & Technology
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