Deep Geothermal Heat Storage under Oilsands - Can We Use it to Help Oilsands Industry? New EGS Concept Proposed
Bibliographic record
Abstract
The research into a geothermal energy option for a deeper crystalline basement heat source in the Northern Alberta basin as a potential artificially fractured subsurface heat exchanger to deliver heat for oilsands processing and/or deep geothermal energy for heating to offset CO2 emission is currently underway as part of the University of Alberta Helmholtz-Alberta Initiative (HAI) geothermal energy project. Temperature logging into old Precambrian granites beneath 0.5 km thin sedimentary column in the 2.35 km deep Hunt well near Fort McMurray shows that there is a rather limited amount of heat in granites. It would require drilling some 4-5 km to get to 80-100 oC in Fort McMurray area and 120-150 oC in Peace River area, respectively. This temperature is not sufficient to generate steam for the in-situ recovery of heavy-oil and bitumen but the demand for hot water for surface processing is limited to only 40-70 oC. Our current effort is to generate hot-water through engineered gothermal systems (EGS) for surface processing of bitumen in the Fort McMurray area as an alternative to burning natural gas for this purpose. At the same time, relatively high temperature gradient areas can serve as a heat source for communities. In this paper, we propose a new concept for greening oilsand energy through the new EGS system. This system is planned to deliver heat for the processing of mined oilsands or pre-heating using inclined to horizontal drillholes used to create artificial heat exchange space and use deep underground heat beneath oilsand. In addition to this, tapping into naturally existing hot aquifers in the hotter Foreland Alberta basin to produce heat/electricity for communities in order to offset the CO2 emisions from oilsand operations is proposed as another option.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".