In-situ recovery of heavy oil is a critical challenge due to the high viscosity of heavy crude, which significantly impedes its flow and extraction efficiency. The current state-of-the-art relies heavily on thermal techniques, such as steam injection, which are energy-intensive and less environmentally friendly. Electromagnetic heating presents a promising alternative, offering the potential for more efficient, controlled, and environmentally friendly heavy oil recovery. Unlike conventional steam injection, electromagnetic heating targets the oil directly, minimizing environmental impact.

This project focuses on developing advanced computational models and algorithms that can assist in optimizing electromagnetic heating processes for in-situ heavy oil recovery. Efforts include building high-performance multi-physics models that capture the complex thermal and electromagnetic behavior of subterranean reservoirs and integrating machine learning approaches to adaptively refine and optimize heating strategies. By advancing these modeling and optimization techniques, the project supports the broader goal of promoting more sustainable, efficient, and environmentally conscious methods.