Modified cyclic SCCO2 injection for energy recovery and thermal breakthrough mitigation in Saskatchewan geothermal reservoirs
Bibliographic record
Abstract
Abstract Geothermal energy is a promising solution to meet the increasing global energy demand while mitigate climate change. In recent years, the utilization of carbon dioxide (CO2), especially supercritical CO2 (SCCO2), for geothermal energy recovery has attracted increasing attention. This study introduces and simulates a modified cyclic SCCO2 injection method for geothermal energy recovery, marking the first exploration of its kind. We analyzed the SCCO2 injection process under various well patterns and injection modes, comparing the cumulative energy recovery performance of cyclic and continuous injection across different models. Our findings revealed that the original reservoir dominates the initial energy production until the SCCO2 breakthrough. After the breakthrough, cyclic injection should be utilized to enhance energy production, with higher heat extraction efficiency and the mitigation of the thermal breakthrough effect. In addition, our findings suggest that an optimal combination of cyclic and continuous injection can leverage the advantages of both strategies. Through further optimization, modified cyclic SCCO2 injection method enhances energy production, achieving up to a 59% improvement in cumulative energy production (4.155E14J) and a 200% increase in NPV ($600,000) compared to baseline scenarios, with higher heat extraction efficiency and mitigation of thermal breakthrough effects.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".