Microfluidic-Based Optimization of Polymer flooding for Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Polymer flooding has been implemented in Western Canada, specifically in Saskatchewan and Alberta since 2000s. Flooding with polymer has been more effective than with water in heavy oil reservoirs due to a more favorable mobility ratio, resulting in more a stable front and less fingering. Optimization of polymer flood strategies is essential for successful implementation of field-scale operations. Core and sand-pack flood testing have been a reliable method for laboratory evaluation of different injection methods and chemicals as the cores are representative of the reservoir geometries and rock chemistries. However, few drawbacks exist such as long turnaround time when multiple strategies need to be compared and inconsistency in pore geometry. Most importantly, this approach cannot resolve the pore scale displacement mechanisms, falling short to compare injection cases in which the recovery factors are similar, but pore-scale dynamics are different. In this study, a microfluidic-based platform was developed to visually evaluate the performance of different polymer flood cases in heavy oil recovery. Two secondary injection tests were performed with water and polymer. A tertiary polymer injection test was also performed with the waterflood followed by the polymer flood. Recovery factors and pore-scale flow behaviour and dynamics of the displacement processes were quantified. The results reported here demonstrate that the microfluidic system is a unique screening tool that can be potentially implemented in conjunction to the traditional core-flooding method to provide more efficient and reliable information to optimize the polymer flooding parameters and inform the operators.
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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 | 0.000 |
| 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 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".