CO2 Foam and CO2 Polymer Enhanced Foam for Heavy Oil Recovery and CO2 Storage
Bibliographic record
Abstract
Enhanced oil recovery (EOR) from heavy oil reservoirs is challenging. High oil viscosity, high mobility ratio, inadequate sweep, and reservoir heterogeneity adds more challenges and severe difficulties during any EOR method. Foam injection showed potential as an EOR method for challenging and heterogeneous reservoirs containing light oil. However, the foams and especially polymer enhanced foams (PEF) for heavy oil recovery have been less studied. This study aims to evaluate the performance of CO2 foam and CO2 PEF for heavy oil recovery and CO2 storage by analyzing flow through porous media pressure profile, oil recovery, and CO2 gas production. Foam bulk stability tests showed higher stability of PEF compared to that of surfactant-based foam both in the absence and presence of heavy crude oil. The addition of polymer to surfactant-based foam significantly improved its dynamic stability during foam flow experiments. CO2 PEF propagated faster with higher apparent viscosity and resulted in more oil recovery compared to that of CO2 foam injection. The visual observation of glass column demonstrated stable frontal displacement and higher sweep efficiency of PEF compared to that of conventional foam. In the fractured rock sample, additional heavy oil recovery was obtained by liquid diversion into the matrix area rather than gas diversion. Aside from oil production, the higher stability of PEF resulted in more gas storage compared to conventional foam. This study shows that CO2 PEF could significantly improve heavy oil recovery and CO2 storage.
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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".