Cracked Naphtha Coinjection in Steam-Assisted Gravity Drainage
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) is a thermal in situ recovery method for heavy oil and oil sands that has been employed to exploit the vast petroleum deposits in the Athabasca region of northern Alberta that are not amenable to surface mining. Nevertheless, in spite of its success in recovering highly viscous bitumen, SAGD remains a costly technology that requires large energy input in the form of steam for each barrel of produced oil. This requires large quantities of water and natural gas, resulting in sizable greenhouse gas (GHG) emissions and extensive postproduction water treatment. There are ongoing efforts to make SAGD more energy-efficient and environmentally sustainable by reducing steam consumption while maintaining favorable oil production rates and ultimate oil recovery. Such efforts include the coinjection of steam and solvent in a process called expanding solvent SAGD (ES-SAGD) wherein bitumen that is essentially immobile at initial reservoir conditions is made mobile by heating and mixing solvent into the oil. In this work, experiments are reported in which cracked naphtha is applied as an additive to steam for the production of Long Lake bitumen. The results conclusively demonstrate that coinjecting cracked naphtha with steam, under specific conditions, significantly increases oil production rates and reduces overall steam requirements.
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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".