Impact of relative timing of low salinity and polymer flooding on mechanisms by which oil recovery is improved
Bibliographic record
Abstract
A combination of enhanced oil recovery (EOR) methods, specifically polymer flooding and low salinity (LS) brine injection, has been shown to improve oil recovery beyond what is achievable with either method used alone. However, the optimal sequence and timing of these methods remain unclear, affecting their efficiency. This study investigates the impact of injection sequences and timing of LS brine and polymer to optimize oil recovery by understanding the underlying mechanisms. Six injection scenarios were tested: (1) injecting high salinity (HS) water followed by LS brine (tertiary injection), (2) injecting HS water to intermediate saturation followed by LS brine, (3) injecting LS brine directly (secondary injection), and in each case, (4) polymer injected simultaneously with LS brine, (5) polymer injected after the LS brine, or (6) polymer injected before the LS brine. The results showed a positive synergy between LS brine and polymer in both secondary and tertiary injections. This synergy is highly sensitive to injection timing, sequence, and rock/fluid properties. The combined effect of LS brine and polymer shifts the flow regime by altering the balance between capillary and viscous forces, maximizing oil recovery when both mechanisms are active. Conversely, the effectiveness declines when one mechanism dominates. Therefore, the timing and order of polymer and LS brine injection significantly influence displacement efficiency and oil recovery, with different injection sequences producing varying outcomes, even with the same EOR techniques.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".