Syed Jehanzeb Adeel Haider, Enbridge Energy Inc., USA

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the pipeline sector of the oil and gas industry has demonstrated considerable potential, especially in overcoming the difficulties associated with incomplete data. This paper explores the application of AI in supplementing missing data for risk evaluations, particularly in scenarios where safety is a critical concern. Potential pitfalls and risks associated with relying solely on AI-generated data are analytically discussed and illustrated in this paper. Through a detailed process flow, this paper also suggests strategies to balance AI reliance with real data acquisition, emphasizing the importance of consequence analysis, cost-benefit considerations, and a hybrid approach to ensure the safety and reliability of operations across the pipeline and broader oil and gas industry in an efficient way.

Artificial Intelligence (AI), Machine Learning (ML), Risk Assessment, Pipeline Safety, ALARP