Static and Dynamic Performance of Wet Foam and Polymer-Enhanced Foam in the Presence of Heavy Oil
Bibliographic record
Abstract
Inadequate sweep efficiency is one of the main concerns in conventional heavy oil recovery processes. Alternative processes are therefore needed to increase heavy oil sweep efficiency. Foam injection has gained interest in conventional oil recovery in recent times as it can control the mobility ratio and improve the sweep efficiency over chemical or gas flooding. However, most of the studies have focused on light crude oil. This study aims to investigate the static and dynamic performances of foam and polymer-enhanced foam (PEF) in the presence of heavy oil. Static and dynamic experiments were conducted to investigate the potential of foam and PEF for heavy oil recovery. Static analysis included foam/PEF stability, decay profile, and image analysis. A linear visual sand pack was used to visualize the performance of CO2 foam and CO2 PEF in porous media (dynamic experiments). Nonionic, anionic, and cationic surfactants were used as the foaming agents. Static stability results showed that the anionic surfactant generated relatively more stable foam, even in the presence of heavy oil. Slower liquid drainage and collapse rates for PEF compared to that of foam were the key observations through foam static analyses. Besides improving heavy oil recovery, the addition of polymer accelerated foam generation and propagation in porous media saturated with heavy oil. Visual analysis demonstrated more stable frontal displacement and higher sweep efficiency of PEF compared to conventional foam flooding. Unlike foam injection, lesser channeling (foam collapse) was observed during PEF injection. The results of this study will open a new insight on the potential of foam, especially polymer-enhanced foam, for oil recovery of those reservoirs with viscous oil.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".