An Experimental Study of Nonequilibrium Carbon Dioxide/Oil Interactions
Bibliographic record
Abstract
Summary In this study, we use a custom-designed visual cell to investigate nonequilibrium carbon dioxide (CO2)/oil interactions under high-pressure/high-temperature conditions. We visualize the CO2/oil interface and measure the visual-cell pressure over time. We perform five sets of visualization tests. The first three tests aim at investigating interactions of gaseous (g), liquid (l), and supercritical (sc) CO2 with a Montney (MTN) oil sample. In the fourth test, to visualize the interactions in the bulk oil phase, we replace the opaque MTN oil with a translucent Duvernay (DUV) light oil (LO). Finally, we conduct an N2(sc)/oil test to compare the results with those of CO2(sc)/oil test. We also compare the results of nonequilibrium CO2/oil interactions with those obtained from conventional pressure/volume/temperature (PVT) 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 CO2(l) and CO2(g). Furthermore, the rate of oil expansion in the 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 because of the local increase in the density of LO. The results of PVT tests show that the density of the CO2/oil mixture is higher than that of the CO2-free oil, explaining the density-driven natural convection during CO2(sc) injection into the visual cell. We do not observe either extracting/condensing flows or density-driven mixing for the N2(sc)/oil test, explaining the low expansion of oil in this test. The results suggest that the combination of density-driven natural convection and extracting/condensing flows enhances CO2(sc) dissolution into the oil phase, leading to fast oil expansion after CO2(sc) injection into the visual cell.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".