Analytical and numerical modeling of the Cyclic ES-SAGD process
Bibliographic record
Abstract
The world is still highly dependable on the energy that comes from oil. The current demand for energy has given importance to oil reservoirs that were normally overlooked in the past due to its properties. One example is found in the Canadian heavy oil sands. The amount of oil that is accumulated in these reservoirs represents the third largest accumulation of oil in the world. In these reservoirs, thermal processes such as Steam Assisted Gravity Drainage (SAGD), are used extensively as a production method. In SAGD, the injection of the steam into the reservoir reduces the viscosity of the oil, which moves downward by the effect of gravity until it reaches a production well. This research presents an alternative way of using SAGD in an efficient and profitable manner. One of the possible variations of SAGD that has shown positive results is the co-injection of a solvent in the injected stream. The idea behind this is to improve the effect of the reduction in the oil viscosity by the diffusion of this solvent in the oil. The amount and type of solvent injected as well as the amount that is recovered are key parameters in the performance of this process, particularly because these solvents are generally more expensive than oil. This work studied the co-injection of the solvent with the steam periodically. This means that in this case the solvent is co-injected through cycles instead of continuously. Some of the aspects that were evaluated are the type of solvent, its concentration and the duration of each of the solvent injection cycles. This study includes the derivation of an analytical model that is able to estimate the oil rate than comes from a cyclic solvent co-injection SAGD process and the use of numerical reservoir simulation to determine the principal recovery mechanisms of the process. One of the principal conclusions is that a similar positive result of the solvent co-injection could be achieved with less amount of solvent usage. This would considerably benefit the profitability of the process and the general performance of SAGD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".