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 injection wells leads to a virtually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. 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, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO₂ sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, 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.

The rapid expansion of hydrogen production from low-carbon sources requires large-scale, flexible and reliable storage solutions to balance supply and demand and support the integration of hydrogen into future energy systems. Underground hydrogen storage, and particularly storage in salt caverns, offers significant potential due to the favorable characteristics of salt formations and the possibility of storing and recovering large quantities of hydrogen. However, identifying and comparing prospective storage sites requires the simultaneous consideration of geological suitability, uncertainty in the available data, operating characteristics, and project-specific requirements. Existing screening approaches generally address only subsets of these aspects, limiting their applicability to comprehensive site comparison and ranking. This study develops a holistic Decision-Making Tool (DMT) for the preliminary screening and comparative ranking of potential hydrogen storage sites in salt formations. The methodology integrates four complementary information categories: Static, Static-Probabilistic, Dynamic, and Operator’s data. Deterministic suitability assessment, Monte Carlo-based uncertainty propagation, engineering feasibility calculations of hydrogen storage performance, and operator-defined requirements are sequentially combined into a scenario-dependent final ranking. The methodology has been implemented in an Excel-based, transparent tool, allowing multiple candidate sites and alternative storage scenarios to be evaluated without changing the underlying calculation framework. Application to three prospective evaporite areas in western Greece demonstrates the ability of the tool to distinguish between sites not only according to geological suitability but also according to data confidence, storage performance and proximity to hydrogen production and consumption hubs. The resulting framework provides a practical bridge between early-stage geological screening and engineering-oriented site selection, while retaining the flexibility to adapt the ranking to different project objectives and levels of decision-making.

The rapid expansion of hydrogen production from low-carbon sources requires large-scale, flexible and reliable storage solutions to balance supply and demand and support the integration of hydrogen into future energy systems. Underground hydrogen storage, and particularly storage in salt caverns, offers significant potential due to the favourable characteristics of salt formations and the possibility of storing and recovering large quantities of hydrogen. However, identifying and comparing prospective storage sites requires the simultaneous consideration of geological suitability, uncertainty in the available data, operating characteristics, and project-specific requirements. Existing screening approaches generally address only subsets of these aspects, limiting their applicability to comprehensive site comparison and ranking. This study develops a holistic Decision-Making Tool (DMT) for the preliminary screening and comparative ranking of potential hydrogen storage sites in salt formations. The methodology integrates four complementary information categories: Static, Static-probabilistic, Dynamic, and Operator’s data. Deterministic suitability assessment, Monte Carlo-based uncertainty propagation, engineering feasibility calculations of hydrogen storage performance, and operator-defined requirements are sequentially combined into a scenario-dependent final ranking. The methodology has been implemented in an Excel-based, transparent tool, allowing multiple candidate sites and alternative storage scenarios to be evaluated without changing the underlying calculation framework. Application to three prospective evaporite areas in western Greece demonstrates the ability of the tool to distinguish between sites not only according to geological suitability, but also according to data confidence, storage performance and proximity to hydrogen production and consumption hubs. The resulting framework provides a practical bridge between early-stage geological screening and engineering-oriented site selection, while retaining the flexibility to adapt the ranking to different project objectives and levels of decision-making.

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 injection wells leads to a vir-tually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. For project developers, this computational burden can stall critical Final Investment Decisions (FID). The proposed approach here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO2 sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, 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.