A Laboratory Protocol for Evaluating Microemulsions for Enhanced Oil Recovery while Fracturing
Bibliographic record
Abstract
Abstract In this paper, we developed a laboratory protocol to evaluate the performance of three microemulsion (ME) additives in fracturing water for enhanced oil recovery. We applied the protocol on oil, brine, and core samples from two wells completed in the Montney (MT) Formation in the following steps: 1) Performing bulk-phase tests to evaluate fluid properties, particle size and stability of nanodispersions (ND) generated by mixing the ME additives with water, 2) Characterizing natural wettability of the core plugs by spontaneous imbibition and contact angle tests, and 3) Evaluating surfactant-assisted imbibition oil recovery during the shut-in time by conducting systematic contact-angle and counter-current imbibition tests under different conditions of brine salinity. Results of fluid-fluid tests, showed that one of the MEs, gives the smallest particle size (36.54 nm), the lowest IFT (0.1753 mN/m), the closest oil-solubility to Winsor III, and the best stability compared with other MEs. In rock-fluid experiments, performed by the candidate ME from fluid-fluid tests, we observed higher and faster imbibition oil recovery by mixture of ME with tap water (52% oil-recovery) compared with mixture of ME with brine (16.6% oil-recovery). In addition, adding the ME additives in brine does not improve imbibition oil recovery compared with the brine without ME additive (7.5%).
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".