New Laboratory Core Flooding Experimental System
Bibliographic record
Abstract
This paper focuses on finding ways to improve the traditional core flooding experimental setup that has been used by the reservoir engineers over the past decades. The new proposed setup can be used in contemporary studies related to enhanced oil recovery. This setup has a possibility of using different flooding agents, e.g., surfactant, polymer, emulsion, oil and water. It also includes an automated effluent analysis, which has been developed to provide estimates on oil recovery efficiency. For validation purposes, the core flooding setup has been tested with an unconsolidated one-dimensional sand pack as a porous medium. Traditional water flooding experiments with paraffin oil and water were conducted at first. Also, two types of emulsion flooding techniques were tested for the sand packs: the direct emulsion flooding and the water flooding followed by the emulsion flooding as an example to exploit the capability of the new setup to successfully perform enhanced oil recovery techniques. Hence, this setup provides a valuable tool for the reservoir engineers to test the different flooding strategies in a laboratory scale experiment, before committing to huge resources in terms of man-power and cost in actual drilling operations in oil reservoirs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".