Effects of Concentration-Dependent Diffusion on Mass Transfer and Frontal Instability in Solvent-Based Processes
Bibliographic record
Abstract
Abstract Mass transfer plays a key role in affecting the efficiency of solvent-based processes in enhanced oil recovery. The mass transfer is usually very slow because of the pure molecular diffusion. However, it can be greatly enhanced by frontal instability when solvent is injected to displace oil. Under unfavorable mobility contrast, the interface between two fluids may become very convoluted as displacements continue. This therefore increases the contact area and leads to more efficient mixing. It is necessary to accurately simulate the detailed frontal instability and its propagation with time in order to investigate its effect on the mass transfer. Most of previous numerical simulations assumed a constant diffusion coefficient (CDC) in solvent-based processes. However, experimental studies on the mixing between two miscible fluids indicated that the diffusion coefficients actually vary with concentration or viscosity. Moreover, some numerical simulations with commercial simulators also showed that using the CDC may result in very high and unrealistic values when matching the oil production rate. In present study, a concentration-dependent diffusion coefficient (CDDC) is considered in which the diffusion coefficient is exponentially proportional to concentration. Highly accurate nonlinear numerical simulations are conducted to simulate the frontal instability under unfavorable mobility ratio between solvent and oil. The effect of injection rate and mobility contrast on frontal instability and mass transfer is examined, and the breakthrough time for the CDDC case is discussed. For better comparisons, the CDC case is presented to more clearly show the effects of CDDC.
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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 | 0.000 | 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".