Applying Machine Learning to Predict Pipeline Failures

• Author(s): David Dayo Osantola
• Paper ID: 1708456
• Page: 2382-2388
• Published Date: 03-07-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 11 May-2025

Abstract

Pipelines are vital for transporting oil and gas, but leaks can have serious consequences such as fires, injuries, pollution, and property damage. Therefore, preserving pipeline integrity is crucial for a safe and sustainable energy supply. The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to develop predictive models that can effectively tackle these challenges. This article explores the significant challenges in managing pipeline infrastructure, including the safety and reliability of its pipeline network. The aging infrastructure, coupled with varying environmental conditions and operational stresses, increases the risk of leaks, which can lead to safety hazards, environmental damage, and financial losses. Traditional leak detection methods are reactive, identifying issues only after significant damage has occurred. The advent of machine learning (ML) presents a significant opportunity to enhance gas pipeline operations through improved efficiency, predicting potential leaks, and prioritizing maintenance. This paper delves into the implementation of machine learning in gas pipelines, focusing on three major areas: Enhanced Safety, Operational Efficiency, and Regulatory Compliance. This paper presents a comprehensive approach to build a machine learning model to predict potential pipeline leaks. This model will integrate operational, environmental, and geospatial data to predict potential pipeline leaks. Early detection of leaks not only prevents environmental damage but also safeguards public safety. This paper addresses challenges primarily in data, model, and computational aspects. By implementing ML in pipeline operations, companies can benefit in cost savings, improved safety records, and reduced environmental impact.

Keywords

Machine Learning (ML), Utilities, Geographic Information Systems (GIS), Enhanced Safety, Operational Efficiency, and Regulatory Compliance.

Citations

IRE Journals:
David Dayo Osantola "Applying Machine Learning to Predict Pipeline Failures" Iconic Research And Engineering Journals Volume 8 Issue 11 2025 Page 2382-2388

IEEE:
David Dayo Osantola "Applying Machine Learning to Predict Pipeline Failures" Iconic Research And Engineering Journals, 8(11)