Development and Evaluation of an Integrated AI-Based Technology for Pipelines Integrity Assessment

Abstract

This research examines the monitoring of the integrity of pipeline infrastructure in the Oil and Gas Industry. It emphasizes the importance of proactive monitoring, robust predictive maintenance practices, and cutting-edge innovative technologies to ensure the safety and reliability of both piggable and non-piggable pipelines. The study examines factors such as pressure fluctuations to develop an applicable strategy for implementation of AI-Driven Digital Twin Framework for Pipeline Maintenance, ROW Surveillance, and Corrosion Control Optimization, identifying vulnerabilities in pipeline systems. It stresses the significance of maintenance practices in the right-of-way conditions and addresses the risks posed by poor asset integrity among major Oil and Gas Producers in meeting global energy demands as varying levels of infrastructure integrity can threaten supply stability to OPEC quotas, the nation economic drives and environmental health. The 300 pipeline segments studied resulted in a classification of 157 pipeline segments as Low Risk, 137 segments as Medium Risk, and 6 pipeline segments as High Risk indicating that most of the evaluated pipelines still retain acceptable levels of structural integrity. This is also supported by an average Digital Twin Integrity Index of 69.12 demonstrating that the integrity state of the pipelines was stable, and an average Remaining Useful Life of 22.11 years was indicative of moderate long-term sustainable operational capacity of the pipelines. However, the evaluation provided identification of 6 critical condition pipelines that require immediate maintenance, and 24 anomalous pipelines were identified using an anomaly detection system. The research advocates for improved transparency, better oil and gas infrastructure management, and increased investment in pipeline safety measures to secure the future of global energy supply. Given the extensive pipeline network, it's crucial to have effective and predictive maintenance systems to identify leaks quickly. The report found that the use of AI- powered digital twin systems, predictive analytics, fiber optic surveillance, anomaly detection models, anomaly detection algorithms, predictive AI-models, AI-based corrosion prediction models to cushion pipeline cathodic protection systems, smart PIG inspections and robotic crawler ultrasonic testing can greatly enhance pipeline reliability, predictive maintenance planning, structural integrity monitoring and environmental sustainability in complex oil and gas operating environments.

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