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Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning.

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Title: Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning.
Authors: Krechowicz, Maria1 (AUTHOR) mkrechowicz@tu.kielce.pl, Krechowicz, Adam2 (AUTHOR) a.krechowicz@tu.kielce.pl
Source: Energies (19961073). 1/15/2021, Vol. 14 Issue 2, p289. 1p.
Subject Terms: *Directional drilling, *Machine learning, *Risk assessment, *Artificial neural networks, *Natural gas pipelines, *Random forest algorithms
Geographic Terms: Europe
Abstract: Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. 1/15/2021, Vol. 14 Issue 2, p289. 1p.
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  Data: *<searchLink fieldCode="DE" term="%22Directional+drilling%22">Directional drilling</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Natural+gas+pipelines%22">Natural gas pipelines</searchLink><br />*<searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Europe%22">Europe</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en14020289
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: 289
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      – SubjectFull: Directional drilling
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Natural gas pipelines
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Europe
        Type: general
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      – TitleFull: Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning.
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              Text: 1/15/2021
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              Y: 2021
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              Value: 14
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            – TitleFull: Energies (19961073)
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