Modeling of Heat Transfer Coupled with Fluid Flow for Temperature Transient Analysis during SAGD Process
Bibliographic record
Abstract
Abstract The steam-assisted gravity drainage (SAGD) process is the most successful in-situ recovery method for heavy oil and bitumen. It is commonly suggested that heat conduction is the dominant mechanism of heat transfer near the edge of steam chamber. Heat convection is neglected in classical models. In this study, three novel heat tranfer models have been developed to describe the transient heat transfer coupled with steady flow in different injection situations during SAGD process. Both heat conduction and heat convection were taken into account in the three models. Model #1 represents a continuous fluid injection at constant temperature. Model #2 represents a continuous fluid injection with exponentially decreasing temperature. Model #3 represents a periodic fluid injection, in which high temperature fluid is injected at the beginning and then lower temperature fluid is injected instead after a period of time. In the models, reservoir and fluid properties were integrated into two parameters, i.e., thermal diffusivity of reservoir and fluid system, and thermal convection velocity of injection fluid. The two parameters are constant under steady flow condition. The analytical solutions to the three heat tranfer models were derived and validated. The effects of thermal diffusivity and thermal convection velocity were examined. It is found that heat convection and heat conduction occur simultaneously in SAGD process, fluid flow motivates convective heat transfer and increases the overall rate of heat transfer. It is also found that the temperature curves predicted by the analytical solutions in this study show excellent agreements with those predicted by COMSOL. In the reservoir and fluid system with larger thermal diffusivity, the heating area is larger, and the temperature increasing rate is smaller at the same observation location. When the steam is injected at a higher thermal convection velocity, heat can be transported to further distance, and the temperature increasing rate is larger at the same observation location. The newly proposed heat transfer models and newly developed analytical solutions are simple and efficient to quickly obtain the temperature profiles in heavy oil reservoirs during SAGD process.
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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) | 0.001 | 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".