Effect of Heterogeneity of Layered Reservoirs on Polymer Flooding: An Experimental Approach Using 5- Spot Glass Micromodel
Bibliographic record
Abstract
Abstract Despite the numerous experimental studies, there is a lack of fundamental understanding about how the local and global heterogeneity control the efficiency of polymer flooding. In this work a series of water and polymer injection processes are performed on five-spot glass micromodels which are initially saturated with the crude oil at varying conditions of flow rate, water salinity, polymer type and concentration. Three different pore structures in combine with different layer orientations are considered for designing of five different micromodel patterns. It has been observed that the oil recovery of water flooding is increasing with the salinity concentration, for the ranges studied here. While, it shows there is an optimum value of concentration in which maximize the oil recovery in polymer flooding. The results confirmed that the highest oil recovery is obtained when the layers are perpendicular to the mean flow direction for both water and polymer flooding. Also, the oil recovery in polymer flooding increases with the increase of layer inclination angle, however it does not increase for waterflooding. In addition, the oil recovery is strongly affected by the local heterogeneity which is near injection zone. This study demonstrates the applicability of micromodel for studying of enhanced oil recovery techniques in locally and globally heterogeneous five-spot models.
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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) | 0.001 | 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".