Development of a Microfluidic Method to Study Enhanced Oil Recovery by Low Salinity Water Flooding
Bibliographic record
Abstract
Microfluidics is an appealing method to study processes at rock pore scale such as oil recovery because of the similar size range. It also offers several advantages over the conventional core flooding methodology, for example, easy cleaning and reuse of the same porous network chips or the option to visually track the process. In this study, the effects of injection rate, flood volume, micromodel structure, initial brine saturation, aging, oil type, brine concentration, and composition are systematically investigated. The recovery process is evaluated based on a series of images taken during the experiment. The remaining crude oil saturation reaches a steady state after injection of a few pore volumes of the brine flood. The higher the injection rate, the higher the emulsification and agitation, leading to unstable displacement. Low salinity brine recovered more oil than the high salinity brine. Aging, initial brine saturation, and the presence of divalent ions in the flood led to a decrease in the oil recovery. Most of the tests in this study showed viscous fingering. The analysis of the experimental parameters allowed to develop a reliable and repeatable procedure for microfluidic water flooding. With the method in place, the enhanced oil recovery test developed based on different variables showed an increase of up to 2% of the original oil in place at the tertiary stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".