Enhanced Heavy-Oil Recovery by Alkali-Surfactant Flooding
Bibliographic record
Abstract
Abstract This study presents the results of laboratory core studies investigating the recovery mechanisms of alkali-surfactant flooding in heavy oil reservoirs. Specifically, mixtures of water and alkali-surfactant systems have been injected into cores containing heavy oil (11 000 mPa·s and 15 000 mPa·s). Salinity is varied in order to generate oil-in-water vs. water-in-oil emulsion systems, and the effects of generating different emulsions are compared. The application of this work is for the many heavy oil reservoirs in countries such as Canada and Venezuela containing viscous oil that still has some limited mobility under reservoir conditions. Alkali-surfactant (AS) flooding has considerable potential for non-thermal oil recovery after primary production. Experiments were performed on cores with varying permeability, at different AS injection rates. All tests were performed on gas-free oil systems. The response from direct injection of AS systems is compared to AS injection after waterflooding. Pressure and oil recovery information is obtained from core floods, and these results are interpreted based on a semi-theoretical framework obtained from phase behavior and bulk liquid studies. It is demonstrated that both oil-in-water and water-in-oil emulsions can lead to the recovery of additional oil. Alkali-surfactant flooding is already an established technique in conventional oil reservoirs, whereby enhanced oil recovery is a result of reduced trapping of oil due to the lowered oil/water interfacial tension. In addition, the injection of these chemicals may lead to the formation of emulsions, as has been documented by previous researchers. In our work, we demonstrate that in heavy oil systems, emulsion formation is a necessary requirement for the production of heavy oil. When these emulsions form, AS injection can lead to considerable improvements in the flooding response, even without the addition of polymers to stabilize the flood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".