Journal Press India^®

Author Details ( * ) denotes Corresponding author

Concerning the safety, security and integrity of the working conditions of the oil and gas pipeline operators, there are several challenges including identification and prevention of the leakage points, corrosion of pipes, quality check of the materials and other major defects. The oil and gas pipelines form the backbone of the concerned industry as they ensure the safe, reliable and efficient transportation of hydrocarbons from the point of extraction or refining to the point of consumption or end users. The traditional pipeline investigation method required manual intervention and was labour-intensive work. However, with the advancement of technology and advanced computational algorithms, the safety of the pipelines is ensured with the help of data and images captured from sensors, drones and other sources. This paper presents a survey of state-of-the-art machine-learning techniques and algorithms developed for pipeline monitoring. The paper also proposes a framework for incorporating machine learning techniques to ensure oil and gas pipelines' safety, efficiency, and sustainability. This study would help the operators, engineers and potential researchers in this field to identify the way to implement machine learning algorithms to identify potential problems and take corrective actions before they become fatal.

Keywords

Machine learning; Oil and gas pipeline; Sustainability; Artificial Intelligence; Safety

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