Non-Equilibrium Solvent Injection Simulation via Pore Scale Heat Conduction and Dispersion
Bibliographic record
Abstract
Abstract In the mathematical treatment and numerical simulation of hybrid recovery processes, specifically SAGD (Steam-Assisted Gravity Drainage) with solvents, instantaneous phase equilibrium is assumed. Previous studies by the authors, and others, show that this is not the case. Experimental work and the lack of success in the field corroborate this view. In the present work, a non-equilibrium model is developed involving the solution of mass and heat transfer equations. The state of equilibrium is based on diffusion and dispersion at the pore scale. First, a new model is developed for oil mobilization by solvent and heat based on diffusion and dispersion mechanisms for different solvents. The concentration distribution is used to calculate the partition coefficients in a gridblock based on temperature, pressure, composition, and apparent time. The latter is different from simulation time and is an indicator of gridblock interaction with the solvent. By defining new parameters viz. apparent time, a gridblock has a memory of how long it was in contact with the solvent. The analytical model developed in the first phase of this work confirmed the need for a non-equilibrium model for reservoir simulation. The non-equilibrium partition coefficient developed for vapor solvents of methane, propane, and butane using compressible assumption for the vapor phase. The oil production appears to improve using a solvent if instantaneous equilibrium is assumed. In this study, the nonequilibrium approach developed for heated solvent showed the cumulative oil production decreased as a result of the inability of the solvent to reduce viscosity. The present study is the first of its type in the development of a non-equilibrium pore-scale simulator. The outcome of this study is considered to be novel in reservoir simulation that can improve predictions significantly.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".