A New Mathematical Model of Solvent - SAGD Process - Importance of Heat and Mass Transfer
Bibliographic record
Abstract
Abstract The current in situ exploitation of oil sands in Alberta employs steam-based recovery methods, which are energy-intensive. A few companies are adding solvent to steam aiming to reduce steam requirements. The mechanism of oil recovery by steam with added solvent is not clear. Over the years, on several occasions- as at the present time- the oil industry has resorted to the use of solvents with steam in thermal recovery operations. The past trials with solvent were short-lived in view of the cost of solvents as well as the lack of success. Given the controversy regarding the use of solvents with steam, this work is intended to explain whether solvent injection with steam increases oil recovery or not. In this work, a new analytical model is developed for describing the solvent-SAGD performance based on the combination of an overall solvent mass balance, heat balance and volumetric oil displacement and Darcy's oil rate using a mixture viscosity model as a function of temperature and solvent concentration ahead of front which satisfy the equilibrium in the system. The objectives of this work are to predict: vapour-steam chamber growth, oil production rate, solvent production rate, solvent loss (or solvent retention) rate, and the effect of solvent type and concentration on the solvent-SAGD process. The results show that the rate of solvent retention increases over time, while the production rates of solvent and bitumen decrease. The efficiency of this process is evaluated using Cumulative Steam-Oil Ratio (CSOR) and cumulative solvent-oil ratio, which permits a comparison of the efficiency of SAGD and solvent-SAGD processes for different solvents. On the whole, the results of this approach give a better understanding of the mechanism of oil production during the solvent-SAGD process by interconnecting vapour chamber conditions and the conditions of heated and diluted oil ahead of the interface.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".