Experimental Investigation of the Effect of Solvent Type and Its Concentration on the Performance of ES-SAGD
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) is a widely used thermal enhanced oil recovery (EOR) technique in North America, particularly in high-permeability oil sand reservoirs. While effective, its economic viability has declined due to low oil prices and high greenhouse gas (GHG) emissions from the steam generation. To improve cost-effectiveness and reduce emissions, solvent-assisted SAGD techniques have been explored. Expanding Solvent-SAGD (ES-SAGD) involves co-injecting light hydrocarbons like propane or butane with steam to enhance oil viscosity reduction. This approach lowers the steam–oil ratio by combining solvent dissolution effects with thermal effects. However, the high cost of solvents, particularly butane, challenges its commercial feasibility. Propane is cheaper but less effective, while butane improves performance but remains expensive. This research aims to optimize ES-SAGD by using a propane–butane mixture to achieve efficient performance at a lower cost than pure butane. A linear sand pack is used to evaluate different propane/butane compositions, maintaining constant operational conditions and a solvent concentration of 15 vol.%. Temperature monitoring provides insights into steam chamber growth. Results show that solvent injection significantly enhances ES-SAGD performance compared to conventional SAGD. Performance improves with increasing butane concentration, up to 80% butane in the C3–C4 mixture at the test pressure and ambient temperature. Propane alone results in the lowest system temperature, while conventional SAGD reaches the highest temperature. These findings highlight the potential of optimized solvent mixtures to improve ES-SAGD efficiency while reducing costs and GHG emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".