Performance of a SAGD Process with Addition of CO2, C3H8, and C4H10 in a Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract A comprehensive simulation has been conducted to evaluate performance of the conventional SAGD and CO2-solvent(s)-assisted SAGD processes in a real field case. Compared to the steam-only process (i.e., the conventional SAGD process), addition of CO2, C3H8 and C4H10 to the steam stream has been found to slightly reduce the oil recovery proportionally if the total injection rate is maintained constant. As for adding one agent, the C4H10-SAGD and CO2-SAGD processes lead to the smallest and largest reduction in oil recovery, respectively. The optimum C4H10 concentration is 5% volume fraction. As for adding two agents, the C3H8-C4H10-SAGD process results in the lowest reduction in oil recovery. C3H8-C4H10 is the optimum binary solvent mixture with its volume fraction of 5% each in the mixture. As for adding three agents, the CO2-C3H8-C4H10-SAGD process leads to the highest reduction in oil recovery. The optimum concentration of CO2-C3H8-C4H10 ternary solvent mixture is found to be 5% for each solvent by volume. Although CO2 has the least oil recovery, it achieves the highest SAGD thermal efficient among the solvent assisted processes. This means that, in addition to its being stored the most in the formation, CO2 has the most capability to hinder heat transfer and to maintain the most thermal efficiency in a SAGD process. The energy requirements are reduced substantially with addition of the three solvents to the steam stream in the SAGD process, though the oil recovery is slightly reduced.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".