Influence of solvent injection temperature in steam-solvent assisted gravity drainage process for heavy oil reservoirs of Brazilian Northeast
Bibliographic record
Abstract
The solvent–steam assisted gravity drainage is an oil recovery process that combines the advantages of thermal and miscible effects, and it has been successfully tested, especially in Canada, where many heavy oil reservoirs are located. This method uses two parallel horizontal wells drilled one above the other, the upper injects steam and solvent and the lower produces oil. This process has not been applied yet in Brazil, where there are heavy oil reservoirs, especially in northeast region. Based on this context, this research aimed to study the application of the ES-SAGD process in a semisynthetic reservoir, with characteristics similar to those found in the Brazilian Northeast, specifically to analyze the influence of the solvent injection scheme on ES-SAGD. Numerical simulations were performed using commercial software from CMG. It was analyzed several steam-solvent injection rates, using two injected temperatures for solvent, (at reservoir or at steam temperature´s), in order to minimize explosion risks, that can be due to high temperatures. Results showed that solvent injection temperature has great influence on oil recovery, it is better for oil production when it is inject hot solvent, because cold solvent generates a cooling of the steam. However, it is possible to injected cold solvent (at reservoir temperature), and increase oil production, changing some steam properties, avoiding explosions risks.
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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 | 0.001 |
| 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 | 0.001 | 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".