Improvement of Displacement Efficiency in Heavy Oil Reservoirs With Enzyme
Bibliographic record
Abstract
Abstract In this study, a systematical technique has been developed to experimentally and numerically evaluate the displacement efficiency in heavy oil reservoirs with enzyme under different conditions. First, dynamic interfacial tensions (IFTs) between enzyme solution and heavy oil are measured with a pendant-drop tensiometer, while effects of pressure, temperature, enzyme concentration, and contact time of enzyme and heavy oil on equilibrium IFT were systematically examined and analyzed. After waterflooding, enzyme flooding was carried out in sandpacks to evaluate its potential to enhance heavy oil recovery at high water-cut stage. Numerical simulation was then performed to identify the underlying mechanisms accounting for the enzyme flooding performance. Subsequently, a total of 18 scenarios were designed to simulate and examine effects of the injection modes and temperature on oil recovery. Except for pressure, temperature, enzyme concentration, and contact time are found to impose a great impact on the equilibrium IFTs, i.e., a high temperature, a high enzyme concentration, and a long contact time reduce the equilibrium IFTs. All three enzyme flooding tests with different enzyme concentrations show the superior recovery performance in comparison to that of pure waterflooding. In addition to the IFT reduction, modification of relative permeability curves is found to be the main reason responsible for further mobilizing the residual heavy oil. A large slug size of enzyme solution usually leads to a high recovery factor, although its incremental oil production is gradually decreased. In addition, temperature is found to have a great effect on the recovery factor of enzyme flooding likely owing to reduction of both oil viscosity and IFT.
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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) | 0.000 | 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".