Experimental and Numerical Evaluation of Enzyme-Assisted Hot Waterflooding Performance for Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract In this study, a systematical technique has been developed to experimentally and numerically evaluate enzyme-assisted hot waterflooding performance in a heavy oil reservoir for the first time. Experimentally, an enzyme solution (i.e., a protein-based liquid catalyst) is prepared and used to displace heavy oil in sandpacked experiments at elevated temperatures, during which pressures and fluid productions are continuously monitored and measured. Numerically, reservoir simulation is performed to reproduce the experimental measurements and then extended to evaluate the performance in a targeted heavy oil reservoir. Once history matching on the experimental measurements is completed, such a calibrated model is then employed to optimize enzyme concentration, temperature, and aging time, respectively. It is found from the displacement experiments that temperature imposes a significant impact on heavy oil recovery with its appropriate range of 45–55 °C, and enzyme positively contributes to heavy oil recovery for most scenarios. Compared to the traditional waterflooding mechanisms, the enzyme-assisted hot waterflooding process shows its considerable potential in heavy oil recovery by means of reducing oil viscosity, altering wettability, and reducing interfacial tension.
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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 | 0.001 | 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) | 0.000 | 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".