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

Maria Krechowicz and Adam Krechowicz
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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

Energies, 2021, vol. 14, issue 2, 1-28

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.

Keywords: risk assessment; pipeline installation; Horizontal Directional Drilling; energy infrastructure; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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