The development of techno-economic and life cycle greenhouse gas models for the assessment of toe-to-heel air injection oil sands extraction technology
Bibliographic record
Abstract
Toe-to-Heel Air Injection (THAI) is a novel technology that uses in situ combustion concepts to mobilize oil sands from the reservoir to the processing facility. It eliminates the use of steam, a highly energy-intensive process, but produces gases that contain hydrocarbons, carbon dioxide, etc., which may affect its sustainability. While the THAI developer anticipated higher economic returns than the steam-based techniques, no study has been conducted to confirm the economic viability of THAI technology, particularly with the inclusion of carbon capture (CC) or carbon taxes. In this study, we developed detailed process models of the THAI technology to evaluate its energy, greenhouse gas (GHG) emissions, and economic performances. The effects of carbon tax policy and incorporating a CC were investigated. The results show that energy consumed for producing electrical power ranges from 76.5 to 143.7 MJ/b, and it is mostly driven by the injected air flow rate and discharged pressure. Aside from oil sands combustion in the reservoir, electricity is the main energy source. GHG emission ranges from 64.3 to 174.3 kgCO 2 e/b. The emission is mostly driven by air injection and produced gas flow rate. The supply cost of THAI’s dilbit (a blend of diluent with bitumen emulsion) ranges from C$35.2/b to C$45.1/b, but when taking into account carbon tax and CC, it ranges from C$38.2/b–C$48.9/b to C$42.2/b−C$54.7/b, respectively. Capital cost and carbon tax strongly influence the uncertainty in supply cost. The results also show that the THAI-based dilbit supply costs are economically viable when compared to current and short-term projected crude oil prices. The results of this study hold value for decision-making on policies and investments regarding THAI-based bitumen extraction technology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".