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

Author

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  • Maria Krechowicz

    (Faculty of Management and Computer Modelling, Kielce University of Technology, Al. 1000-lecia Państwa Polskiego 7, 25-314 Kielce, Poland)

  • Adam Krechowicz

    (Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. 1000-lecia Państwa Polskiego 7, 25-314 Kielce, Poland)

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.

Suggested Citation

  • Maria Krechowicz & Adam Krechowicz, 2021. "Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning," Energies, MDPI, vol. 14(2), pages 1-28, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:289-:d:476176
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    References listed on IDEAS

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    1. Ahmad Al-AbdulJabbar & Salaheldin Elkatatny & Ahmed Abdulhamid Mahmoud & Tamer Moussa & Dhafer Al-Shehri & Mahmoud Abughaban & Abdullah Al-Yami, 2020. "Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    2. Rafał Wiśniowski & Paweł Łopata & Grzegorz Orłowicz, 2020. "Numerical Methods for Optimization of the Horizontal Directional Drilling (HDD) Well Path Trajectory," Energies, MDPI, vol. 13(15), pages 1-15, July.
    3. Rafał Wiśniowski & Krzysztof Skrzypaszek & Paweł Łopata & Grzegorz Orłowicz, 2020. "The Catenary Method as an Alternative to the Horizontal Directional Drilling Trajectory Design in 2D Space," Energies, MDPI, vol. 13(5), pages 1-14, March.
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    Citations

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    Cited by:

    1. Maria Krechowicz & Katarzyna Kiliańska, 2021. "Risk and Opportunity Assessment Model for CSR Initiatives in the Face of Coronavirus," Sustainability, MDPI, vol. 13(11), pages 1-22, May.
    2. Maria Krechowicz & Adam Krechowicz & Lech Lichołai & Artur Pawelec & Jerzy Zbigniew Piotrowski & Anna Stępień, 2022. "Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning," Energies, MDPI, vol. 15(11), pages 1-21, May.
    3. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    4. Maria Krechowicz, 2022. "Towards Sustainable Project Management: Evaluation of Relationship-Specific Risks and Risk Determinants Threatening to Achieve the Intended Benefit of Interorganizational Cooperation in Engineering Pr," Sustainability, MDPI, vol. 14(5), pages 1-24, March.

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