Hydrocarbon Recovery From Oil Sands by Cyclic Surfactant Solubilization in Single-Phase Microemulsions
Bibliographic record
Abstract
Extra heavy crude oil (bitumen) reserves represent a significant part of the energy resources found all over the world. In Canada, the “oil sands” deposits are typically unconsolidated, water-wet media where current methods of recovery, such as open pit mining, steam-assisted gravity drainage (SAGD), vapor extraction, cold heavy oil production with sand, etc., are controversial due to adverse effect on environment. Chemical enhanced oil recovery (cEOR) techniques have been applied as alternatives but have limited success and contradictory results. An alternative method is described in this paper, which relies on the application of single-phase microemulsion to achieve extremely high solubilization. The produced microemulsion will be less viscous than oil, eliminating the need for solvent addition. Produced microemulsion can be separated to recover surfactant for re-injection. The work in this paper discusses phase behavior experiments and a flow experiment to prove the concept that single-phase microemulsions could be used to recover extra-heavy oils. Phase behavior experiments showed that the mixture of alcohol propoxysulfate, sodium dioctyl sulfosuccinate, sodium carbonate, and tri-ethylene glycol monobutyl ether results in single-phase microemulsion with extra-heavy crude. A flow experiment conducted with the same composition produced only single-phase microemulsion leading to 74% recovery of the original oil in place from a synthetic oil sand. Future experiments will be focused on optimizing the formulation and testing with actual oil sands samples.
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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) | 0.000 | 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 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".