Nanomodel visualization of fluid injections in tight formations
Bibliographic record
Abstract
The transport and phase change of a complex fluid mixture under nanoconfinement is of fundamental importance in nanoscience, and limits the recovery efficiency from tight oil reservoirs (<10%). Herein, through experiments and supporting theory we characterize the transport and phase change of a nanoconfined complex fluid mixture. Our nanofluidic platform, nanomodel, replicates shale reservoirs in terms of mean pore size (∼100 nm), permeability (∼μD) and porosity (∼10%). We screen conditions for the most promising shale EOR strategies, directly quantifying their pore-scale efficiency and underlying mechanisms. We find that immiscible gas (N2) flooding presents a prohibitively large capillary pressure threshold (∼2 MPa). Miscible (CO2) gas flooding eliminates this threshold leading to film-wise stable oil displacement with high recovery efficiency. Strong capillary forces present barriers as well as opportunities for recovery strategies unique to nanoporous reservoirs by transitioning from a miscible to an immiscible condition locally within the reservoir. These results quantify the fundamental transport and phase change mechanisms applicable to nanoconfined complex fluids, with direct implications in unconventional oil as well as nanoporous media more broadly.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".