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<p>The increasing complexity of oil and gas production systems, coupled with growing demands for energy efficiency and environmental sustainability, has accelerated the adoption of artificial intelligence (AI) in petroleum production engineering. AI-based approaches, including machine learning, deep learning, and hybrid physics-informed models, have demonstrated significant improvements in prediction accuracy, operational efficiency, and decision-making compared to conventional methods. For example, recent studies report that data-driven models such as artificial neural networks and long short-term memory (LSTM) networks can substantially enhance production forecasting accuracy and equipment performance prediction, particularly in dynamic and data-rich production environments. This paper presents a comprehensive and application-oriented review of AI techniques in petroleum production engineering, focusing on key domains such as production forecasting, artificial lift optimization, predictive maintenance, surface facility optimization, and reservoir–production system integration. The review also examines the role of AI in improving energy efficiency, reducing greenhouse gas emissions, and enabling sustainable production practices through intelligent optimization and monitoring systems. In addition, this study provides a critical analysis of current challenges and research gaps, including issues related to data quality and availability, model interpretability, generalization across fields, and real-time deployment in operational environments. Despite the promising performance of AI models, their practical implementation remains limited by data-related constraints and integration challenges with existing production systems. The findings of this review highlight the potential of AI as a key enabler of digital transformation in petroleum production engineering and provide insights into future research directions, including explainable AI, hybrid modeling approaches, digital twins, and autonomous production systems.</p>

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Artificial intelligence in petroleum production engineering: applications, optimization, and sustainability

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月17日

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  • Yasin Khalili,
  • Saeed Abbasi

摘要

The increasing complexity of oil and gas production systems, coupled with growing demands for energy efficiency and environmental sustainability, has accelerated the adoption of artificial intelligence (AI) in petroleum production engineering. AI-based approaches, including machine learning, deep learning, and hybrid physics-informed models, have demonstrated significant improvements in prediction accuracy, operational efficiency, and decision-making compared to conventional methods. For example, recent studies report that data-driven models such as artificial neural networks and long short-term memory (LSTM) networks can substantially enhance production forecasting accuracy and equipment performance prediction, particularly in dynamic and data-rich production environments. This paper presents a comprehensive and application-oriented review of AI techniques in petroleum production engineering, focusing on key domains such as production forecasting, artificial lift optimization, predictive maintenance, surface facility optimization, and reservoir–production system integration. The review also examines the role of AI in improving energy efficiency, reducing greenhouse gas emissions, and enabling sustainable production practices through intelligent optimization and monitoring systems. In addition, this study provides a critical analysis of current challenges and research gaps, including issues related to data quality and availability, model interpretability, generalization across fields, and real-time deployment in operational environments. Despite the promising performance of AI models, their practical implementation remains limited by data-related constraints and integration challenges with existing production systems. The findings of this review highlight the potential of AI as a key enabler of digital transformation in petroleum production engineering and provide insights into future research directions, including explainable AI, hybrid modeling approaches, digital twins, and autonomous production systems.