Semianalytical Modeling of Steam/Solvent Gravity Drainage of Heavy Oil and Bitumen: Unsteady-State Model With Curved Interface
Bibliographic record
Abstract
Summary Coinjection of solvent with steam in steam-assisted gravity drainage (SAGD) has shown promising results for enhancing oil rates as well as reducing energy and water consumption. Modeling and optimizing hybrid-steam/solvent-recovery processes by use of commercial numerical simulators can be very time-consuming. Semianalytical mathematical models may be used to estimate production rates and thermal efficiency in much less time. In this study, an unsteady-state semianalytical model was developed to predict the oil-flow rate in the steam/solvent-assisted-recovery process. The model assumes a curved interface with transient temperature and solvent distribution in the mobile zone. It also accounts for transverse dispersion and concentration-dependent molecular diffusion for solvent distribution. The oil-flow rate and interface profile are predicted at each time in an iterative fashion. The model is validated against the CMG-STARS thermal simulator as well as experimental results for hexane-aided SAGD physical-model tests. The semianalytical model was able to predict oil-production rates by use of different solvents coinjected with steam, in agreement with reported experimental data. The proposed model accounts for the complex interaction of heat and solvent solubility and diffusion as they affect mobilization and production of viscous oil. This model may be used to estimate the optimal operation parameters for the process over a range of different reservoir qualities and pressures, in a very time-efficient manner. The final outcome may lead to an efficient design of a steam/solvent-recovery process that uses less water and reduces the amount of energy and gas emissions per barrel of oil produced.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".