Accelerating Reservoir Simulation to Shorten the Field Development Lifecycle: Innovations in AI, Multiscale Physics, and Scalable Compute

Abstract This paper presents a comprehensive overview of the latest advancements in reservoir simulation technologies that aim to significantly reduce runtimes, enabling faster and more iterative field development planning. With models becoming increasingly large and complex, accelerating simulations is essential to achieving timely decisions. The approach is structured around two key pillars: advanced software and scalable hardware. Software innovations include modern linear solvers, scalable parallelism, Multiscale Sequential Fully Implicit (SFI) methods for black-oil models, and AI/ML-based enhancements such as phase labeling and saturation pressure prediction for compositional models. Additional automation for tuning and model quality checks further optimizes workflows. On the hardware side, the paper explores hardware-agnostic execution across CPUs, full-GPU architecture, and elastic cloud-based environments via on-demand simulation platforms. Real-world use cases across various global assets demonstrate the holistic performance benefits achieved by combining these innovations. Field applications in large, high-resolution reservoirs have shown significant performance gains. Using multiscale SFI methods, simulation runtimes were reduced by up to 4×, enabling full-field models with tens of millions of cells to run in hours rather than days. Full-GPU execution delivered up to up to 2× speedups, while AI/ML-driven enhancements—such as automated phase labeling and saturation pressure prediction—resulted in up to 4× improvements, while automatic tuning and model QC reduced engineering intervention time by more than 50%. These advancements not only improved runtime but also enhanced result stability and enabled more FDP iterations in less time. The outcome is a more agile field development planning process, compressing decision timelines from months to weeks while maintaining technical rigor. This paper uniquely combines physics-based innovation, AI-driven simulation acceleration, and scalable compute infrastructure in practical applications. By bridging traditional reservoir engineering with next-gen digital technologies, it outlines a path toward real-time, simulation-integrated decision-making that is applicable across the industry.