A New Approach Utilizing Liquid Catalyst for Improving Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Chemical-assisted enhanced oil recovery (EOR) has recently received a great deal of attention as a means of improving the efficiency of oil recovery processes. Producing heavy oil is technically difficult due to its high viscosity and high asphaltene content; therefore, novel recovery techniques are frequently tested and developed. This study contributes to general progress in this area by synthesizing an acidic Ni-Mo-based liquid catalyst (LC) and employing it to improve heavy oil recovery from sand-pack columns for the first time. To understand the mechanisms responsible for improved recovery, the effect of the LC on oil viscosity, density, interfacial tension (IFT), and saturates, aromatics, resin, and asphaltenes (SARA) were assessed. The results show that heavy oil treated with an acidic Ni-Mo-based LC has reduced viscosity and density and that the IFT of oil–water decreased by 7.69 mN/m, from 24.80 mN/m to 17.11 mN/m. These results are specific to the LC employed. The results also indicate that the presence of the LC partially upgrades the structure and group composition of the heavy oil, and sand-pack flooding results show that the LC increased the heavy oil recovery factor by 60.50% of the original oil in place (OOIP). Together, these findings demonstrate that acidic Ni-Mo-based LCs are an effective form of chemical-enhanced EOR and should be considered for wider testing and/or commercial use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".