A Fully Automated Design of Experiments-based Method for Rapidly Achieving Near-Optimal CO2 Injection Strategies

Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of wells creates a virtually infinite set of injection scenarios, while traditional optimization techniques demand thousands of reservoir simulations, often resulting in prohibitive costs and delays. For project developers, this computational burden can stall critical Final Investment Decisions (FID).

The approach proposed here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling to identify near-optimal solutions with minimal simulations. Instead of pursuing computationally expensive global optima, this method efficiently maps the relationship between injection rates and storage performance, enabling rapid identification of high-capacity configurations. In testing, storage efficiencies up to 60% were achieved with as few as 12 simulations, demonstrating a step-change reduction in time and cost.

From a business standpoint, this means CCS operators can de-risk projects earlier, accelerate FID timelines, and evaluate multiple site configurations in parallel while minimizing computational overhead. Rather than waiting weeks or months for exhaustive optimization, decision-makers can gain timely, reliable insights that directly support capacity commitments, regulatory submissions, and ultimately revenue realization.