Improvement of Sweep Efficiency of Miscible Displacement Processes in Heterogeneous Porous Media
Bibliographic record
Abstract
Abstract The viscous fingering phenomenon in displacement processes develops due to higher mobility of the injected fluid compared to that of the inhabitant fluid. It causes early breakthrough of the injected fluid and results in reduced sweep efficiency of the process. In addition to viscous fingering, heterogeneity of the porous medium can be a source of instability in displacement processes which has significant interactions with the viscous forces. The coupling between viscous fingering and heterogeneity induced channeling has been deemed to enhance the instabilities by increasing the growth rate of fingers formed inside the high permeable channels. This conclusion is questioned by the present work which investigates the effect of the injection velocity, diffusion coefficient, and width of the layers in a layered medium, on the coupling between the two named sources of instability. Numerical simulations and quantitative analysis of the instabilities show that the flow in heterogeneous media goes through four different regimes (initial diffusion, channeling, lateral dispersion, and viscous fingering) each dominated by a different flow mechanism that results in different growth rates of fingers. Based on these characterizations a method is suggested to design the process with an optimum injection rate such that the coupling between viscous fingering and heterogeneity attenuates the instability and results in improved sweep efficiency. Finding the optimum displacement parameters and improving the efficiency of the process helps to reduce the amount of solvent to be injected in the medium and therefore decreases the environmental effects of the recovery process.
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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".