Characterization of Gas Injection Flow Patterns Subject to Gravity and Viscous Forces
Bibliographic record
Abstract
Core Ideas Air was injected into saturated 0.7‐mm sand at 0.1, 10, 100, 250, and 498 mL min −1 . Optical density and gas pressure were used to classify the gas flow pattern. The gravity drive of highly buoyant gas quickly dominates over viscous forces. Bo/Ca was used to predict the transition of flow from continuous to discontinuous. Gas movement in otherwise water‐saturated porous media is complex and is important for a variety of applications. The interplay of gravity, capillary, and viscous forces influences the movement and resulting pattern of gas. To develop a better understanding of this competition among these forces, air injection experiments were performed in an intermediate‐scale, two‐dimensional flow cell at injection rates of 0.1, 10, 100, 250, and 498 mL min −1 . The resulting gas patterns were characterized using gas pressure measurements and optical density measurements based on digital images to classify gas flow as continuous, transitional, or discontinuous, and near‐pore‐scale observations of transient gas flow were made to gain insight concerning the influence of gravity, viscous, and capillary forces. These observations highlighted the importance of gravity and viscous forces, along with capillary forces, for gas flow in water‐wet media. Based on these observations, a simplified dimensionless number (the ratio of the Bond and Capillary numbers) was proposed to quantify the interplay of gravity to viscous forces, and its validity for the prediction of the type of gas flow was assessed. This study provides a high spatiotemporal dataset of transient gas movement in homogeneous sand, which can be used to provide insight for multiphase flow modeling efforts and future understanding of gas movement coupled to mass transfer.
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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 | 0.001 | 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) | 0.001 | 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".