Effect of Aromatic Ring, Cation, and Anion Types of Ionic Liquids on Heavy Oil Recovery
Bibliographic record
Abstract
Surfactant/alkali flooding is one of the best chemical flooding methods to enhance the oil Recovery Factor (RF). In this research, Ionic Liquid/Alkali (ILA) mixtures were chosen to represent a form of chemical flooding experiments. The selected Ionic Liquids (ILs), {[EMIM][Cl], [THTDPH][Cl], [EMIM][Ac], [BzMIM][Cl], [DMIM][Cl], [BzMIM][TOS], [dMIM][TOS] and [MPyr][TOS]}, were introduced to investigate their efficiency in improving heavy oil (14o API) RF from the sand packs. Besides, the use of mixtures of the same ionic liquids and brine (3.37 wt. % salts) with an alkali (Sodium Bicarbonate [NaHCO3]) were also investigated. In this experimental study, the flooding process started with injecting about 3.2 Pore Volumes (PVs) of only brine, followed by one PV of the chemical composites, and flushed with two PVs of formation brine. The study discussed the influence of cation type, anion type, the structure of the ILs, and the effect of combining ILs/alkali on the RF. The results revealed that the proposed chemical mixtures are effective in enhancing the recovery factor. ILs with shorter alkyl chain and more aromatic rings are noticeably more efficient in enhancing the RF. Finding the optimum composition of ([DMIM][Cl] + NaHCO3) the chemical slug increased the additional RF up to 31.55 (% OOIP). Also, increasing the slug size to two PVs improved the RF to 42.13 (% OOIP). The recovery factor mechanism was explained and supported by measuring the effect of IL types on the viscosity, Surface Tension (SFT), and Zeta Potential (ZP) of the mixture.
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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 | 0.001 |
| 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".