Digital Transformation in Petroleum Engineering: AI and Simulation-Based Optimization of Production Systems

Abstract

The petroleum industry is undergoing a rapid digital transformation driven by artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and advanced simulation techniques. These technologies are reshaping exploration and production (E&P) operations by enabling real-time monitoring, predictive decision-making, and automated optimization of drilling, reservoir management, and production systems. AI and simulation tools are increasingly applied to improve drilling efficiency, enhance reservoir performance through accurate history matching and production forecasting, optimize fluid transport in pipelines, and enable predictive maintenance that reduces downtime and operational costs. Despite their potential, challenges such as data quality, integration of heterogeneous datasets, cybersecurity, and model interpretability remain significant. Recent advancements in digital twins, hybrid AI-physics modeling, and real-time analytics are helping to address these challenges and improve operational decision-making. Future research is expected to focus on autonomous operations, AI-driven adaptive production strategies, and sustainable digital solutions that enhance efficiency, safety, and environmental compliance in petroleum operations. This review provides a comprehensive perspective on AI and simulation-driven production optimization, offering insights for researchers and industry practitioners aiming to implement smart, efficient, and resilient E&P systems.