Foam-Assisted Enhanced Oil Recovery: Bridging the Gap between Theory and Practice
Bibliographic record
Abstract
The utilization of foam in enhanced oil recovery (EOR) applications has been the subject of extensive scientific investigations in last two decades. This is due to its ability to regulate the mobility of residual oil and improve the interfacial properties simultaneously. As a result, foam declines the gas relative permeability and improves overall sweep efficiency. The effectiveness of the foam in EOR application is a function of porosity, permeability, the flow rate of gaseous and liquid phases, capillary pressure, temperature, and salinity. Despite the various benefits associated with foam-assisted EOR, significant challenges have been identified in its implementation. In reservoir-like conditions, foam is susceptible to drying out because the lamella cannot withstand the prevalent capillary pressure resulting from the insufficient liquid. This results in the coalescence and drying of foam. The effectiveness of foam diminishes when it dries out, leaving only the liquid phase, thereby causing the relative permeability to resemble that of water injection. The present study suggests a best practise workflow to address the aforementioned issues in foam-assisted EOR (theoretical, experimental, and simulation perspectives). The workflow consists of characterizing the foam independently before conducting an optimized coreflood. We purport that more thought into the form of the semi-empirical local equilibrium (LE) model may be warranted. Furthermore, the study provides a workflow to gain valuable insights to improve the effectiveness of foam-assisted EOR application.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".