Effect of Solvent Co-Injection on Residual Oil Saturation in SAGD Steam Chamber
Bibliographic record
Abstract
Steam Assisted Gravity Drainage (SAGD) process has been applied over wide area of the Province of Alberta, boosting the Canadian oil reserves to the position of third highest in the world. A key performance indicator of SAGD thermal efficiency is the steam-oil-ratio (SOR) that is the volume of water converted to steam and injected into the formation for each unit volume of produced oil. Even though several cost-saving advances have been made in this technology, SAGD remains expensive in terms of both the oil production cost and the environmental cost associated with greenhouse gases (GHG) emissions. Several kinds of additives have been proposed for improving the thermal efficiency of the process and decreasing the SOR while increasing the cumulative oil recovery. Solvent addition in SAGD is one alternative that improves the performance by decreasing the oil viscosity by dilution and thereby by decreasing the required amount of heat per produced oil barrel. In solvent enhanced SAGD, a part of steam volume is replaced by hydrocarbon solvent, in order to take advantage of not just heat but also of dilution for viscosity reduction. At the same time, solvent injection reduces heat losses by reducing the operating temperature. The combination of reservoir characteristics and operational constraints influence the choice of solvent as well as its concentration and timing. No systematic study of residual oil saturation (Sor) in solvent enhanced SAGD has been reported in the literature. This project tested four solvents (Pentane -C5H12, Hexane -C6H14, Cracked Naphtha and Natural Gas Condensate) at different concentrations using linear sand-packs that simulated SAGD gravity drainage to quantify their impact on the recovery performance during the injection process and on the residual oil saturation. The addition of all tested solvents to steam increased the rate of oil drainage and reduced the residual oil saturation. Amongst the single component solvents, 15 vol% hexane gave the fasted recovery and lowest residual oil saturation. However, the multicomponent solvents performed even better. Addition of 15 vol% cracked naphtha gave the lowest residual saturation and fastest oil recovery. The performance of gas condensate was also impressive. At 5 vol% concentration it was able to outperform 10 vol% cracked naphtha and 15 vol% hexane in terms of the rate of oil recovery and residual oil saturation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".