Loading…
Wednesday February 26, 2025 12:20pm - 12:45pm CST
Advancing Reservoir Engineering through High-Performance Computing and Neural Operators on the Cloud

Abstract: Contemporary reservoir engineering applications demand extensive high-fidelity simulations that remain computationally intensive despite advances in high-performance computing. This work presents an integration of scientific machine learning with physics-based reservoir simulation through a scalable, cloud-based workflow utilizing Fourier Neural Operators (FNOs) and GPU-accelerated simulators. FNOs learn mappings between function spaces rather than Euclidean spaces, enabling superior generalization capabilities. The framework is validated using two synthetic 2-phase oil-water systems: a homogeneous case and a heterogeneous case with multi-scale property variations. Results demonstrate that our HPC-enabled FNO implementation achieves approximately 1000x speedup compared to traditional approaches while maintaining acceptable accuracy. Future work will address scaling challenges and enhanced applicability in production environments.

Authors: Karthik Mukundakrishnan (Stone Ridge Technology), Vidyasagar Ananthan (Amazon Web
Services), Dan Kahn (Amazon Web Services) and Dmitriy Tishechkin (Amazon Web Services)
Wednesday February 26, 2025 12:20pm - 12:45pm CST
Auditorium

Sign up or log in to save this to your schedule, view media, leave feedback and see who's attending!

Share Modal

Share this link via

Or copy link