Data-driven optimization of underground hydrogen storage: A multi-metric machine learning framework for operational planning and control

Underground hydrogen storage (UHS) must balance injectivity, productivity, hydrogen purity, withdrawal efficiency, and hydrogen-to-water ratio over repeated cycles and across geological conditions. We present a unified optimization framework that uses simulation-trained machine-learning (ML) proxies to co-optimize these five metrics with respect to injection/production rates, half-cycle duration, cushion-fill time, and cushion-gas composition. The proxies were trained on 500 high-fidelity compositional simulations generated from a synthetic aquifer reservoir model and achieved held-out test accuracies of R 2 = 0.905–0.953 across the five metrics. These cycle-resolved models were embedded in a global differential-evolution search using a normalized multi-metric area-under-curve objective. Unlike most prior studies, the framework simultaneously evaluates pure, binary, and ternary CO₂–CH₄–N₂ cushion gases while treating cycle timing as a decision variable. Results show that operational controls dominate performance: the best composite outcomes occur at high throughput with relatively short half-cycles (1–3 months) and cushion-fill times typically in the 2–10 month range. Methane (CH₄) is the most reliable baseline cushion gas across most cases, while a CO₂–CH₄–N₂ ternary blend improves injectivity in fast-cycling regimes. Across reservoir-quality cases, the optimizer consistently retains CH₄ and a 2-month half-cycle, while reducing cushion-fill time from 12 months (lower-quality case) to 4 months (medium/high-quality cases). The workflow transfers to a heterogeneous 3D geomodel for rapid site-specific screening.