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A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes.

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Title: A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes.
Authors: Gholgheysari Gorjaei, Reza1, Songolzadeh, Reza1, Torkaman, Mohammad1 m.torkaman@put.ac.ir, Safari, Mohsen2, Zargar, Ghassem3
Source: Journal of Natural Gas Science & Engineering. May2015, Vol. 24, p228-237. 10p.
Subjects: Support vector machines, Two-phase flow, Wellheads, Empirical research, Flocculation
Abstract: Two-phase flow through chokes is common in oil industry. Wellhead chokes regulate and stabilize flow rate to prevent reservoir pressure declining, water coning and protecting downstream facilities against production flocculation. Choke liquid rate prediction is a basic requirement in production scheme and choke design. In this study, for the first time a least square support vector machine (LSSVM) model is developed for predicting liquid flow rate in two-phase flow through wellhead chokes. Particle swarm optimization (PSO) is applied to optimize tuning parameters of LSSVM model. Model inputs include choke upstream pressure, gas liquid ratio (GLR) and choke size which are surface measurable variables. Calculated flow rates from PSO-LSSVM model are excellently consistent with actual measured rates. Moreover, comparison between this model and related empirical correlations show accuracy and superiority of the model. Results of this work indicate PSO-LSSVM model is a powerful technique for predicting liquid rate of chokes in oil industry. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Natural Gas Science & Engineering is the property of Elsevier B.V. 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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DbLabel: Engineering Source
An: 103202668
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  Data: A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes.
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  Data: <searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Two-phase+flow%22">Two-phase flow</searchLink><br /><searchLink fieldCode="DE" term="%22Wellheads%22">Wellheads</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Flocculation%22">Flocculation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Two-phase flow through chokes is common in oil industry. Wellhead chokes regulate and stabilize flow rate to prevent reservoir pressure declining, water coning and protecting downstream facilities against production flocculation. Choke liquid rate prediction is a basic requirement in production scheme and choke design. In this study, for the first time a least square support vector machine (LSSVM) model is developed for predicting liquid flow rate in two-phase flow through wellhead chokes. Particle swarm optimization (PSO) is applied to optimize tuning parameters of LSSVM model. Model inputs include choke upstream pressure, gas liquid ratio (GLR) and choke size which are surface measurable variables. Calculated flow rates from PSO-LSSVM model are excellently consistent with actual measured rates. Moreover, comparison between this model and related empirical correlations show accuracy and superiority of the model. Results of this work indicate PSO-LSSVM model is a powerful technique for predicting liquid rate of chokes in oil industry. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Natural Gas Science & Engineering is the property of Elsevier B.V. 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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      – Type: doi
        Value: 10.1016/j.jngse.2015.03.013
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 10
        StartPage: 228
    Subjects:
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Two-phase flow
        Type: general
      – SubjectFull: Wellheads
        Type: general
      – SubjectFull: Empirical research
        Type: general
      – SubjectFull: Flocculation
        Type: general
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      – TitleFull: A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes.
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            NameFull: Gholgheysari Gorjaei, Reza
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            NameFull: Songolzadeh, Reza
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            NameFull: Torkaman, Mohammad
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            NameFull: Safari, Mohsen
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            NameFull: Zargar, Ghassem
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              M: 05
              Text: May2015
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              Y: 2015
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              Value: 24
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