Cushion gas replacement on underground gas storage in a naturally fracture aquifer: Cushion gas strategies for matrix-independent storage
Bibliographic record
Abstract
Energy supply has become a critical global concern. Due to its low cost and high heating value (HV), natural gas is a leading energy source. However, its consumption fluctuates throughout the year due to varying heating demands in different weather conditions, with significantly more usage during colder seasons. Underground natural gas storage (UNGS) addresses this variability. UNGS requires cushion gas to maintain reservoir pressure, enabling gas production. Cushion gas usually constitutes 15–75% of total stored gas, making it costly. Replacing cushion gas is a promising approach to reduce operational costs. This study numerically simulates underground gas storage in a naturally fractured aquifer. It investigates carbon dioxide and nitrogen as potential cushion gas replacements. Various flow scenarios were modeled, and performance was evaluated based on gas recovery, unwanted water production, and produced gas quality. The study focuses on natural gas storage in the Yourtsha aquifer and explores the technical and economic feasibility of using inert gases as substitutes. Results show that carbon dioxide outperforms nitrogen as a cushion gas without accounting for gas dissolution in water. However, when nitrogen dissolution is considered, nitrogen shows higher potential. Including gas dissolution effects also indicates that inert gases perform better than natural gas in displacing fluids and maintaining pressure during storage operations. Furthermore, comparing cumulative injection and production volumes in scenarios with and without gas mixing revealed that mixing negatively impacts storage operation quality. Water production increased in the base scenario due to mixing, adversely affecting seasonal gas injection and production. In scenarios using inert cushion gases, the presence of multiple gas components intensified the mixing phenomenon, resulting in higher water production from gas wells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".