Modeling of Foamy-Oil Flow in Solvent-Based Recovery Processes
Bibliographic record
Abstract
Abstract Solvent-based recovery processes, such as cyclic solvent injection and its variants, have shown a great potential to enhance heavy oil recovery after cold heavy oil production with sands. In such processes, pressure is increased and reduced in a cyclic manner to induce foamy oil flow, which is a key production mechanism. Previous conclusions of foamy oil flow in primary production may have limitations for cyclic processes since the fluid properties and operating conditions are fairly different. This study first conducted an experimental study to visualize the foamy oil flow in a scaled physical model under realistic reservoir conditions. Pseudo-bubble points at different stages of the test are recorded. Then a mathematical model is developed to simulate the experimental observations. This model reasonably couples pressure diffusion and mass transfer together, and considers dynamic properties of gas, foamy oil and diluted oil. Experimental observations show that the oil zone remains relatively stable before pressure drops to a certain level. Afterwards, the oil front moves explosively inwards the solvent chamber, indicating a flow of foamy oil. Theoretical modeling results show that the pressure gradient at the oil-gas zone interface increases from zero at the beginning to a considerable value during a drawdown process. The foamy oil flows only when the pressure gradient reaches a certain level, and a larger drawdown rate tends to result in a higher pressure gradient and a higher pseudo-bubble point. In addition, at the same pressure drawdown rate, the foamy oil flow at a later stage is expected to happen more quickly than at an earlier stage.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".