AI-Powered Solutions for Missing Data in Pipeline Risk Assessments

Syed JehanzebAdeel Haider, Enbridge Energy Inc.,Houston, USA

ABSTRACT

The use of Artificial Intelligence (AI) and Machine Learning (ML) in the oil and gas pipeline industry has shown significant promise, particularly in addressing challenges posed by incomplete datasets. This paper explores the application of AI in filling missing data for risk assessments, with a focus on safety-critical scenarios. Through a detailed process flow, this paper illustrates the potential pitfalls and risks associated with relying solely on AI-generated data. 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.

Keywords

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