New insights into reservoir on chip: Numerical investigation and experimental validation
Bibliographic record
Abstract
The present study reports a numerical investigation of oil extraction from a pore-scale perspective using water as the injection fluid. The pore network is constructed from the statistical realization of the pore space of the reservoir rock. Conceptually, the pore network model used in the study miniaturizes the porous reservoir containing oil/gas onto a microfluidic platform, capturing the actual pore-level length scale and it complex features. The validity of the numerical model is established through an experimental investigation of single-phase flow. The experimentally calculated absolute permeability based on the Darcy law shows an excellent agreement with the numerically attained value. The two-phase numerical model uses the phase field technique to track the development of the interface between the two immiscible phases, i.e., oil and water. The numerical model shows a piston-like displacement and captures interesting pore-level phenomena like snap-off and trapping. Implementing the complete network for simulation reveals the unstable nature of the flooding, which is persistent with invasion percolation. The complete network simulation reveals the discontinuous flood front with a segregated flow configuration. Two-phase experiments conducted on a polydimethylsiloxane test chip with an equivalent pore level network also showed similar flow features, thereby establishing the credibility of our two-phase simulations. The recovery factor obtained from the simulation was found to be 0.78, which is in close agreement with experimental data reported in the literature. The modified Darcy law applied to the numerical model generates relative permeability plots similar to the experimental core flooding plots reported in the literature. The numerical model presented here provides valuable insight into the oil recovery process and its implications at field scale. To the best of our knowledge, this is the first instance involving numerical analysis of the full-scale system of Reservoir on a Chip system detailing the pore-level flow dynamics.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".