Nonlinear Behaviour of Mass Transfer Mechanisms in Solvent Oil Recovery
Bibliographic record
Abstract
Literature has indicated that, experimentally, solvent fronts in hybrid thermal solvent recovery processes progress more rapidly than what can be predicted using current approximations and more rapidly than thermal processes alone [1]. The equations that govern thermal multiphase flow through porous media are extremely complex and it is very difficult to decouple the contribution of the mass transfer mechanisms from the thermal effects. This paper explores the behavior of the mass transfer mechanisms in these processes through an examination of the nonlinear one-dimensional advection diffusion/dispersion (ADD) equation using finite difference methods. Earlier work [2] indicated that the linear ADD equation, using physically estimated parameters for diffusion and dispersion coefficients obtained from the literature, could not account for the solvent front progression rate predicted by Edmunds [3]. The results in this preliminary study indicate that the nonlinear effects are important in predicting the progression of a solvent front using the one dimensional ADD equation. The shapes and rate of propagation of the concentration profiles are influenced by both velocity and diffusion functionality. These results are more consistent with the solvent front propagation rate predicted by Edmunds [3]. These results also suggest that including nonlinear effects in traditional reservoir simulation software may be necessary in the modeling of solvent processes. Further work is needed to explore and understand the influence of the velocity and diffusion functionality necessary to mimic the behaviour observed in thermal solvent recovery processes and to further increase the understanding of their impact on solvent front propagation.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".