Quantification of convective and diffusive transport during CO2 dissolution in oil: A numerical and analytical study
Bibliographic record
Abstract
In this study, we use an analytical approach and the interpolation-supplemented lattice Boltzmann method (ISLBM) to quantify convective and diffusive transport during CO2 dissolution. In the first step, we use a turbulence analogy and the ISLBM to determine the relationship between the Rayleigh number (Ra) and the ratio of the pseudo-diffusion coefficient to the molecular diffusion coefficient (D*D). We then use experimental data from two oil samples, condensate and crude oils, to validate the obtained relationship between D*D and Ra. We also use the Sherwood number (Sh) and total mixing and diffusive transport curves to analyze different periods during CO2 dissolution for condensate and crude oils. We focus, in particular, on how Ra affects the characteristics of density-driven fingers and the convection field. Our results show that there is a logarithmic trend between D*D and Ra. Analysis of the total mixing and diffusive curves indicates that the CO2 dissolution process can be divided into three distinct periods, namely, diffusive transport, early convection, and late convection. We find that more than 50% of the ultimate CO2 dissolution occurs in the early convection period. We also show that the analytical results obtained for the critical time and critical depth at the onset of convection is in good agreement with those of the ISLBM. After the onset of convection, the formation of initial fingers leads to enhanced convective transport, with marked implications for the concentration variance and mixing rate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".