Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO$_2$ Storage

Understand

Geological CO2 storage (GCS) involves injecting captured CO2 into deep subsurface formations to support climate goals.

• The effective management of GCS relies on adaptive injection planning to dynamically control injection rates and well pressures to balance both storage safety and efficiency.
• Prior literature, including numerical optimization methods and surrogate-optimization methods, is limited by real-world GCS requirements of smooth state transitions and goal-directed planning within limited time.
• To address these limitations, we propose a Brownian Bridge-augmented framework for surrogate simulation and injection planning in GCS and develop two insights: (i) Brownian bridge as a smooth state regularizer for better surrogate simulation; (ii) Brownian bridge as goal-time-conditioned planning guidance for improved injection planning.