<p>This paper explores the adoption of digital twin technology in the oil and gas industry. With advancements in computational power, cloud computing, artificial intelligence (AI), and machine learning (ML), traditional reservoir simulation methods are becoming outdated. The energy transition relies on the experience gained from previous reservoir simulation studies. Thus, this paper sheds light on the evolving trends and future directions in reservoir simulation and modeling activities. The components of digital twin technology, their roles in reservoir management, data acquisition, real-time monitoring, data quality, and the infrastructure for AI/ML are highlighted. The six benefits of digital twins for reservoir simulation include (1) improved operational efficiency, (2) cost savings, (3) enhanced decision-making, (4) increased sustainability, (5) innovation, and (6) collaboration. For each benefit, an example of reservoir engineering applications is provided. By linking to an oil price dataset that dynamically reports oil price fluctuations, a digital twin can assess the economic performance of a field. This feature is particularly valuable during times of political conflict, such as wars or pandemics, when oil prices experience significant volatility. The role of cloud computing in accelerating the adoption of digital twins in reservoir simulation is also discussed. Furthermore, the paper considers issues such as regulatory compliance and cybersecurity. Lastly, it addresses the need to update educational materials and textbooks to equip the next generation of geoscientists with the necessary digital skills. This study provides guidelines for policymakers, regulators, academia, and industry experts interested in digital twins, with a focus on the energy transition.</p>