Study of Ionic Liquids as Effective Solvents for Enhanced Heavy Oil Recovery
Bibliographic record
Abstract
Injecting chemicals in reservoirs is an efficient technique to improve oil recovery. In this study, several types of Ionic Liquids (ILs) were initially screened using the Conductorlike Screening Model for Realistic Solvents (COSMO-RS) model. COSMO-RS, in particular, is capable of providing predictions of the thermodynamics properties of ILs. A comprehensive screening study of 148 probable cations and 46 anions was performed in order to shed some light on the interaction of ILs with hydrocarbons via the calculation of the capacity of the screened ILs to hydrocarbons fractions and water. The most suitable ILs for our purposes were chosen, and employed for improving heavy oil (14o API) recovery from sand pack at room conditions taking the cost, physical and chemical properties of the ILs into consideration. The selected ILs were 1-Dodecyl-3- methylimidazolium chloride [DMIM][Cl], 1-Ethyl-3-methylimidazolium acetate [EMIM][Ac], 1-Ethyl-3-methylimidazolium methyl sulfate [EMIM][MS], 1-Hexyl-3- methylimidazolium tetrafluoroborate [HMIM][Bf4], 1-Butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [BMIM][Tf2N], 1-Methyl-3-octylimidazolium tetrafluoroborate [MOIM][Bf4], 1-Methyl-3-octylimidazolium hexafluorophosphate [MOIM][PF6], 1,3-Dimethylimidazolium tosylate [dMIM][TOS], 1-Benzyl-3- methylimidazolium tosylate [BenzMIM][TOS], N-methylpyridinium tosylate [MPyr][TOS], 1-Ethyl-3-methylimidazolium chloride [EMIM][Cl], 1-Benzyl-3- methylimidazolium chloride [BenzMIM][Cl], and Trihexyltetradecylphosphonium chloride [THTDPh][Cl].
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".