Microfluidic Insights into the Effects of Reservoir and Operational Parameters on Foamy Oil Flow Dynamics during Cyclic Solvent Injection: Reservoir-on-the-Chip Aided Experimental and Numerical Studies
Bibliographic record
Abstract
This study examines the microfluidic characterization of foamy oil flow dynamics in heterogeneous porous media. A total of 12 microfluidic CSI experiments were conducted using reservoir-on-the-chip platforms. In addition, detailed PVT analysis was performed to characterise the heavy oil/solvent systems. Moreover, a numerical model constructed with CMG software package (2021.10) has been validated against the experimental findings in this study. A clear-cut visualization study provided by microfluidic systems revealed that factors including solvent type, pressure depletion rate, and reservoir parameters have a significant impact on foamy oil flow extension. It was found that a solvent containing a higher CO2 content demonstrated more effective performance compared with other solvent compositions, owing to its capability to reduce viscosity, enhance swelling, and offer more gas molecules due to its superior solubility. Additionally, a high pressure-depletion rate amplifies the driving force for bubble nucleation, as well as reducing the amount of time available for bubble coalescence. In addition, lower reservoir porosity impedes bubble movement and delays coalescence, thus extending the foamy oil flow. Furthermore, with the aid of a robust image analysis technique, it was discovered that utilizing 100% CO2 as a solvent resulted in a 17% increase in oil recovery over using 50% CO2 and 50% CH4. Furthermore, a 6% increase in oil recovery was achieved by applying a fast pressure depletion rate as opposed to a slow pressure depletion rate. Moreover, the numerical model constructed was found to be accurate in adjusting heavy oil recovery with an average relative error of 7.7%.
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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 | 0.001 | 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 | 0.000 | 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".