Feasibility and economic analysis of hydrogen seasonal storage in depleted gas reservoirs: A case study in Alberta
Bibliographic record
Abstract
Underground hydrogen storage (UHS) in depleted gas reservoirs is a promising solution to address seasonal energy imbalances in regions like Alberta, where surplus renewable electricity in summer contrasts sharply with high winter demand. At present, most existing studies are based on synthetic reservoir models and do not adequately account for geological heterogeneity, operational constraints observed in field conditions, or the integrated optimization of technical performance and economic viability. This study investigates the feasibility and economic potential of UHS in a near-depleted gas reservoir within the Edson Formation, Alberta. Multiple hydrogen-related mechanisms, including permeability hysteresis, structural trapping, diffusivity, solubility, and the effect of cushion gas are explicitly simulated, offering a more realistic assessment of storage dynamics. A novel multi-objective Sparrow Search Algorithm (MOSSA) combined with Pareto non-dominated ranking is developed to simultaneously maximize hydrogen recovery factor and Net Present Value (NPV), incorporating both integer and continuous operational variables such as well count, location, injection rates, and conversion timing. Results show that optimal well configurations improve hydrogen recovery by up to 10 % over the base case, with economic scenarios achieving projected revenues exceeding CAD 23.78 million in the tenth year. Sensitivity analysis reveals that pressure and well scheduling significantly affect performance, while storage mechanisms exert minimal impact. This research provides a technically and economically robust framework for large-scale UHS deployment in real field settings, addressing key knowledge gaps and supporting Alberta's transition to a low-carbon energy system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".