Uncertainty Assessment of SAGD Performance Using a Proxy Model Based on Butler's Theory
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage (SAGD) is an efficient method for thermal recovery of bitumen from the vast reserves available worldwide and particularly from the oil sands in western Canada. Flow simulators are available for predicting SAGD performance and are used to support reservoir management decisions; however, the high computational time associated with the use of such complex flow simulation makes it impractical for all locations especially when reservoir uncertainty and variable operational parameters are included in the making decision process. The use of a simpler analytical model as a proxy for the reservoir simulator is shown to be a feasible alternative to flow simulation. A proxy model based on the Butler's SAGD theory is developed to predict the oil flow rate, cumulative oil production and cumulative steam injection profiles during both: the rising and spreading steam chamber periods for a confined SAGD well pair. Modifying factors are used to fit the proxy to flow simulation results to account for conformance and reservoir heterogeneity among other factors. A critical aspect of the proxy model is a realistic parameterization of geological heterogeneity. Monte Carlo Simulation (MCS) and the proxy model permit an efficient transfer of the uncertainty in reservoir and operational parameters through to performance variables such as oil production and steam oil ratio. An example application for a single well pair showed the efficiency of the methodology in terms of computation time. The results permit improved reservoir management of complex SAGD projects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".