An Experimental Study of Non-Equilibrium CO2-Oil Interactions
Bibliographic record
Abstract
Abstract In this study, we use a custom-designed visual cell to investigate non-equilibrium CO2/oil interactions under high-pressure and high-temperature conditions. We visualize the CO2/oil interface and measure visual cell's pressure over time. We perform 5 sets of visualization test. The first three tests aim at investigating interactions of gaseous (g), liquid (l), and supercritical (sc) CO2 with the Montney oil. In the fourth test, in order to visualize the interactions at the bulk oil phase, we replace the opaque Montney oil with a translucent Duvernay light oil (LO). Finally, we conduct N2(sc)-Oil test to compare the results with those of CO2-Oil tests. Results of the first three tests show that oil immediately expands upon injection of CO2 into the visual cell. CO2(sc) leads to the maximum oil expansion followed by CO2l and CO2(g). Furthermore, the rate of oil expansion in CO2(sc)-Oil test is higher than that in CO2(l)-Oil and CO2(g)-Oil tests. We also observe extracting and condensing flows at the CO2(l)-Oil and CO2(sc)-Oil interfaces. Moreover, we observe density-driven fingers inside the LO phase due to the local increase in the density of LO. We do not observe neither extracting/condensing flows nor density-driven mixing for N2(sc)-Oil and CO2(g)-Oil tests, explaining low expansion of oil in these tests. The results suggest that the combination of density-driven natural mixing and extracting/condensing flows enhance CO2(sc) dissolution into the oil phase, leading to fast oil expansion after CO2(sc) injection into the visual cell.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".