An empirical correlation to predict the SAGD recovery performance
Bibliographic record
Abstract
The prediction of the recovery performance for Steam-Assisted-Gravity-Drainage (SAGD) process is becoming increasingly important as the SAGD projects all over the world continue to increase. Currently most of these models are analytical or semi-analytical methods, and the predicting process is much complicated. In this paper, based on the grey system theory, we developed a weighted grey correlation model. Aiming at a typical thick heavy oil reservoir from Bohai offshore oilfield, China, the influences of reservoir/fluid parameters and operation parameters on SAGD recovery performance are comprehensively evaluated through this model. And a sensitive sequence of each parameter is derived to reflect the sensitive degree. Thus a static multi-parameter correlation is proposed to predict the oil recovery factor and cumulative oil-steam ratio (COSR) of SAGD process. Then, this correlation is used to predict the SAGD recovery performance in some potential thick heavy oil reservoirs of Bohai oilfield and the results are compared against the numerical simulation models. Thereafter, we investigate some successful and operating SAGD projects around the world and use the correlation to estimate the recovery performance of them. This correlation is a static method to predict the recovery performance of SAGD process and it could be used to successfully predict the recovery performance of 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".