Simulation of O/W Emulsion Flow in Alkaline/Surfactant Flood for Heavy Oil Recovery
Bibliographic record
Abstract
Summary The formation and flow of emulsions during alkaline flooding process plays an important role for improving heavy oil recovery. In this study alkaline/surfactant (A/S) flood tests were performed in sandpacks to demonstrate the effectiveness of sweep efficiency improvement by the in-situ generated oil-in-water (O/W) emulsion. High tertiary oil recoveries were obtained in sandpack flood tests. Experimental results were history matched by including the mechanisms of in-situ generation and flow of O/W emulsion, as well as the chemical adsorption and the reduction of interfacial tension involved in the chemical flooding process. The decrease in local water phase permeability caused by the entrapment of emulsion droplets was modelled using the filtration theory. Both the pressure response and the oil recovery improvement were fairly matched. Field-scale simulations were conducted to investigate the potential of A/S flooding for heavy oil reservoirs. Simulations showed promising results of chemical flooding for heavy oils. It was indicated that a certain length of waterflooding time would benefit the final oil recovery, and there existed an optimum chemical slug size. These laboratory results and the simulation technique are helpful in the simulation and design of field-scale projects of chemical flooding for enhanced heavy oil recovery.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".