Spatiotemporal Hierarchical Optimization of CO2 Injection Schedules: An Industry-Ready Framework

A critical requirement in geological CO2 storage is the optimization of injection strategy. Various constraints such as pressure buildup without compromising wellbore integrity, continuous intake to satisfy commercial obligations, minimization of costly recycling and brine handling and adherence to strict regulatory requirements for plume containment, create a multi-dimensional optimization problem. Attempting to navigate this complexity through manual, heuristic scheduling is not only inefficient but increasingly impractical as CCS projects scale in size and ambition. To address this challenge, we present a fully automated hierarchical optimization framework for CO2 injection scheduling which integrates full-physics reservoir simulation with embedded constraints, enabling efficient convergence toward industry-ready solutions. The optimizer is firstly run on a simplified case where all injectors share same rate. The solution initializes the subsequent model where the injectors and the simulation time scale are alternatively refined, in a spatiotemporal refinement fashion, until a fully varying optimal injection schedule has been reached. When applied to realistic heterogeneous models, the fully automated hierarchical optimization framework delivers site-specific CO2 injection strategies that maximize storage efficiency and economic returns while ensuring operational safety. By systematically refining schedules in space and time, it provides a replicable, industry-ready pathway to scale safe and cost-effective CCS deployment.