Rise of the Digital Petroleum Engineer Reshapes the Oil and Gas Industry

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 226792, “Reshaping the Oil and Gas Industry: The Rise of the Digital Petroleum Engineer,” by Babak Moradi, SPE, Lasse Hermansson, and Tor Ellingsen, THREE60 Energy, et al. The paper has not been peer-reviewed. _ This paper explores the evolving role of the digital petroleum engineer (DPE), examines the core technologies they use, assesses the challenges they face, and projects future industry trends. Emphasis is placed on the synergy between traditional domain knowledge and digitalization as a key driver of success. The study focuses on the expanding role of the DPE, including the application of artificial intelligence (AI)-driven analytics, cloud-based platforms, and advanced automation. Advantages of Embracing the Digital Shift: Case Study in Accelerating Reservoir Simulation The complete paper details a list of benefits accrued in embracing the shift toward digitization, including talent attraction, sustainability, and heightened operational efficiency (Fig. 1). As an example, the complete paper provides the following case study in the discipline of reservoir simulation. In modern petroleum engineering, the ability to simulate reservoir behavior quickly and accurately has become critical for efficient field development and real-time decision-making. Thanks to advances in computational power, parallel processing, and AI, DPEs can perform studies that would have been computationally infeasible just a decade ago. The evolution of computational capability has allowed engineers to build and simulate models with unprecedented resolution. In 1977, it was estimated that black-oil reservoir models could reach 100 million grid cells by 2017. This threshold was far surpassed, however, when engineers successfully ran a trillion-cell simulation in 2016. The transition from traditional central-processing-unit (CPU)-based calculations to parallel CPU and graphics-processing-unit processing also has revolutionized simulation workflows. CPU parallelization distributes tasks across multiple cores and processors, dramatically reducing run times. In parallel, data-driven modeling techniques have emerged as complementary tools to physics-based simulators. These methods use machine learning (ML) and statistical algorithms to model complex reservoir behavior based on historical field data. Some techniques allow engineers to evaluate multiple development scenarios quickly, even in mature or data-sparse fields. Combining physics-based models with data-driven methods offers the best of both approaches. The Role of the DPE Oil and gas operations now generate enormous volumes of data. Making timely and actionable use of this data requires engineers with skills in advanced analytics, ML, and data interpretation. Real-time decision-making also is becoming standard in field operations. A DPE also must integrate geoscience, reservoir engineering, economics, and operational strategy with digital tools and platforms. This convergence of disciplines requires a new kind of versatility in technical roles. Remote operations are another driving force. This capability proved essential during the pandemic. Additionally, the industry faces a generational shift as a large portion of the experienced workforce approaches retirement. Preserving this institutional knowledge by embedding it into AI systems and training a new generation of digitally fluent engineers is vital for long-term resilience and continuity.