Reservoir-on-a-Chip (ROC): A new paradigm in reservoir engineering
Bibliographic record
Abstract
In this study, we design a microfluidic chip, which represents the pore structure of a naturally occurring oil-bearing reservoir rock. The pore-network has been etched in a silicon substrate and bonded with a glass covering layer to make a complete microfluidic chip, which is termed as 'Reservoir-on-a-chip' (ROC). Here we report, for the first time, the ability to perform traditional waterflooding experiments in a ROC. Oil is kept as the resident phase in the ROC, and waterflooding is performed to displace the oil phase from the network. The flow visualization provides specific information about the presence of the trapped oil phase and the movement of the oil/water interface/meniscus in the network. The recovery curve is extracted based on the measured volume of oil at the outlet of the ROC. We also provide the first indication that this oil-recovery trend realized at chip-level can be correlated to the flooding experiments related to actual reservoir cores. Hence, we have successfully demonstrated that the conceptualized 'Reservoir-on-a-Chip' has the features of a realistic pore-network and in principle is able to perform the necessary flooding experiments that are routinely done in reservoir engineering.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".