Alkaline‐ionic liquid slug injection for improved heavy oil recovery
Bibliographic record
Abstract
Abstract Chemical flooding has a great potential for improving the recovery factor of heavy oil, especially for reservoirs in which thermal techniques are not applicable. In this novel study, chemical flooding experiments of alkali and alkali + ionic liquid (AIL) mixtures were performed using unconsolidated sand packs. The aim was to investigate the effect on the recovery factor (RF) of combining an alkali, Na2CO3, and four different imidazolium‐based ionic liquids (1‐ethyl‐3‐methylimidazolium acetate [EMIM][Ac], 1‐ethyl‐3‐methyl imidazolium chloride [EMIM][Cl], 1‐butyl‐3‐methylimidazolium bis(trifluoromethylsulphonyl)imide [BMIM][Tf2N], and 1‐dodecyl‐3‐methylimidazolium chloride [DMIM][Cl]). The obtained data show that as the ionic liquid concentration and slug size increase, the recovery factor (RF) was found to increase. The study also demonstrates that ionic liquids (ILs) are better in improving heavy oil than recovery than a well‐known surfactant, sodium dodecyl sulphate (SDS). IFT measurements were performed for the Pelican oil used with brine and with [DMIM][Cl], and [EMIM][Ac]. The surface tension (SFT) and zeta potential (ZP) were also measured and correlated to the improvement in the RF. The study shows that in the presence of Na2CO3, the ionic liquid type, concentration, and slug size are important parameters in enhancing heavy oil recovery.
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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) | 0.001 | 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".