Accelerated Mass Transfer of CO<sub>2</sub> in Reservoir Brine Due to Density-Driven Natural Convection at High Pressures and Elevated Temperatures
Bibliographic record
Abstract
In this paper, the mass transfer of CO 2 into a reservoir brine sample is studied experimentally at high pressures and elevated temperatures. The equilibrium concentration of CO 2 in the reservoir brine and the density of CO 2 -saturated brine are measured by saturating the brine with CO 2 . The mass-transfer rate of CO 2 into the brine is determined by monitoring the pressure decay inside a closed, visual, high-pressure PVT cell. It is found that the density of the brine with dissolved CO 2 increases linearly with CO 2 concentration. As CO 2 gradually dissolves into the brine by molecular diffusion, a concentration-induced density gradient is generated near the CO 2 −brine interface. Under the influence of gravity, this concentration-induced density gradient causes natural convection, which accelerates the mass-transfer rate of CO 2 into the brine. The modified diffusion equation with an effective diffusivity is applied to model the mass-transfer process. It is found that the determined effective diffusivities of CO 2 in the reservoir brine are almost two orders of magnitude larger than the molecular diffusivities of CO 2 in water or similar reservoir brines. The detailed experimental results show that the density-driven natural convection greatly accelerates the dissolution process of CO 2 in brine. This means that loss of CO 2 in brine can be significant in an enhanced oil recovery operation using CO 2 flooding in an oil reservoir with a bottom water aquifer. More importantly, the accelerated mass transfer due to the density-driven natural convection significantly increases the geological sequestration rate of CO 2 in deep saline formations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".