Predictive Risk Modeling of High-Probability Methane Leak Events in Oil and Gas Networks

Methane emissions from oil and gas infrastructure represent a significant environmental and safety challenge due to their potent greenhouse gas effects and operational hazards. This paper proposes a theoretical framework for predictive risk modeling aimed at identifying and prioritizing high-probability methane leak events within complex pipeline networks. By integrating risk theory, probabilistic modeling techniques, and detailed leak dynamics, the framework systematically quantifies leak likelihood through weighted risk factors derived from infrastructure attributes, operational history, and environmental conditions. The model architecture encompasses data inputs, probabilistic computation, and output metrics to support dynamic temporal and spatial risk mapping, enabling proactive risk governance and resource allocation. Analytical considerations address critical modeling assumptions, risk thresholding strategies, and theoretical performance evaluation metrics. The proposed framework advances both theoretical understanding and practical risk management by facilitating early detection, prioritization, and informed decision-making without reliance on empirical case studies or simulations. Finally, the study outlines future research directions, including multi-risk integration, real-time adaptive modeling, and automated mitigation, highlighting the framework’s potential to enhance methane leak management in pursuit of environmental sustainability and operational safety in oil and gas networks.