A Deep-To-Shallow Seasonal Energy Storage Strategy to Improve Geothermal Longevity in Regina, Saskatchewan
Bibliographic record
Abstract
Abstract Sedimentary basins, both deep and shallow, offer valuable low-carbon energy solutions by serving as significant geothermal energy sources. In cold climates like the Canadian prairie provinces, extracting geothermal energy from low-enthalpy subsurface systems provides a practical way to meet seasonal heat demands. This study focuses on optimizing geothermal energy utilization through deep-to-shallow thermal energy storage strategies, enhancing system sustainability, and supporting long-term energy security. In this study, we built a numerical model of three aquifers, namely Winnipeg, Deadwood, and Mannville. These formations are key geothermal resources and have provided high permeability and suitable temperatures for direct heat use since 1978. The shallower Mannville Aquifer, at around 850 m, consists of interbedded sandstone and shale with regional groundwater flow. Two doublet systems are incorporated to evaluate deep-to-shallow aquifer thermal energy transfer. Seasonal energy demand is evaluated using data from a University of Regina residential facility. The methodology involves simulating fluid flow, heat transfer, and aquifer interactions to optimize extraction rates while minimizing thermal breakthroughs. Numerical simulations indicate that integrating a deep-to-shallow thermal energy storage approach significantly enhances geothermal system efficiency. The highest cumulative production rate from both aquifers was set at 2400 m³/day. During low-energy demand periods, deep geothermal well production could be reduced to a constant rate of 1000 m³/day, with excess energy stored by injecting it into the shallower geothermal reservoir. Storing excess energy from deep aquifers in the shallower Mannville aquifer during low-demand periods slows thermal depletion of the deep reservoir, preserving higher temperatures for extended use. Observations show that seasonal energy storage improves temperature stability, reduces thermal breakthroughs, and optimizes extraction rates. Additionally, findings emphasize the importance of managing production patterns to align with seasonal heat demand, ensuring long-term sustainability. The study concludes that implementing Aquifer Thermal Energy Storage (ATES) enhances geothermal system longevity, increases energy efficiency, and provides a sustainable solution for regions like Saskatchewan. Overall, the deep-to-shallow thermal exchange method offers an effective strategy for optimizing geothermal energy utilization while improving thermal stability in subsurface reservoirs. Our modeling approach assesses the novel deep-to-shallow geothermal storage strategy to optimize energy extraction and seasonal heat management. The results provide insights into enhancing energy efficiency, increasing geothermal system sustainability, and improving thermal storage in the shallower aquifer during low-demand months.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".