Performance Evaluation of Ionic Liquids Using Numerical Simulation
Bibliographic record
Abstract
Given the rise in oil productivity from conventional and unconventional resources in Canada using Enhanced Oil Recovery (EOR), the need to understand and characterize these techniques, for the purpose of recovery optimization, has taken a prominent role in resource management. Chemical flooding has proved to be one of the most efficient EOR techniques. This study investigated the potential of employing Ionic Liquids (ILs) as alternative chemical agents for improving oil recovery. There is very little attention paid to employing this technique as well as few experimental and simulation studies. Consequently, very limited data are available. Since pilot and field studies are relatively expensive and time consuming, a numerical simulation study using CMG-STARS simulator was utilized to explore the efficiency of employing 1-Ethyl-3-Methyl-Imidazolium Acetate ([EMIM][Ac]) and 1-Benzyl-3-meth- limidazolium chloride ([BenzMIM][Cl]) with respect to improving medium oil recovery. Eight different lab-scale sandpack flooding experiments were selected to develop a numerical model to obtain the history matching of the experimental flooding results using CMG-CMOST. We observed that the main challenge was tuning the relative permeability curves to achieve a successful match for the oil recovery factor. Finally, a sensitivity study was performed to examine the effect of the chemical injection rate, the chemical concentration, the slug size, and the initiation time on oil recovery. The results showed a noticeable increase in the oil RF when injecting IL compared to conventional waterflooding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".