Evaluating the Efficacy of Artificial Intelligence Techniques for Proactive Risk Assessment in Oil and Gas: A Focus on Predictive Accuracy and Real Time Decision Support

Abstract

The oil and gas industry operates within a landscape of complex, high-stakes risks that span operational, environmental, and safety domains. Traditional risk assessment methodologies, while foundational, are constrained by their static nature and limited capacity to process dynamic, large-scale data. This dissertation investigates the application of artificial intelligence (AI) methodologies—specifically fuzzy logic and machine learning—to enhance risk assessment frameworks in the oil and gas sector. By systematically evaluating key performance criteria, including predictive accuracy, data processing capabilities, and user interactivity, this research establishes a comprehensive framework for integrating AI-driven approaches into risk management systems. The findings demonstrate that AI-based models significantly enhance the ability to anticipate and mitigate risks through real-time decision support and advanced predictive analytics. This work further introduces a scalable decision-making model leveraging fuzzy inference to handle uncertainty and improve the robustness of risk assessments. The proposed framework offers a pathway for transitioning from reactive to proactive safety management strategies, ensuring resilience and sustainability in increasingly complex industrial environments.