Risk Analysis of Flowlines in the Oil and Gas Sector: A Geographic Information Systems and Machine Learning Approach

Summary In this paper, we present a risk analysis of flowlines in the oil and gas sector using geographic information systems (GIS) and machine learning (ML). Flowlines, vital conduits that transport oil, gas, and water from wellheads to surface facilities, often face underassessment compared with transmission pipelines. In this study, we address this gap using advanced tools to predict and mitigate failures, improving environmental safety and reducing human exposure. To conduct this study, we processed extensive data sets from the Colorado Energy and Carbon Management Commission (ECMC) through spatial matching, feature engineering, and geometric extraction to build robust predictive models, and used various ML algorithms, including logistic regression (LR), support vector machines (SVMs), and gradient boosting decision trees (GBDT), to assess and classify risks. To explore the impact of feature space reduction, we applied two dimensionality reduction methods—principal component analysis (PCA) and canonical correlation analysis (CCA). Finally, we use a thorough data analysis to highlight spatial and operational factors that influence risks, identifying high-risk zones for focused monitoring. Overall, the study demonstrates the transformative potential of integrating GIS and ML in flowline risk management, proposing a data-driven approach that emphasizes the need for accurate data and refined models to improve safety in petroleum extraction.