An Empirical Correlation to Predict the SAGD Recovery Performance
Bibliographic record
Abstract
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. The prediction of SAGD recovery performance should go back to the theory basis developed by Butler (1978). Afterwards, based on his model, many modified models are proposed. But most of these models are analytical or semi-analytical methods, and the predicting process is much complicated. In particular for the SAGD projects in some irregular thick heavy oil reservoir, it will be a hard work. Thus, a quick and easy method is needed to screen the heavy oil reservoirs for potential SAGD project. In this study, based on the grey system theory, we developed a weighted grey correlation model firstly. Through this 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 were comprehensively evaluated. And a sensitive sequence of each parameter was derived to reflect the sensitive degree. Thus a static multi-parameter nonlinear correlation is proposed to predict the oil recovery, recovery rate 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 is compared against the numerical simulation model. During this process, we also make a survey on the successful SAGD projects around the world and analyze the development features. Through the modification to the proposed correlation above, it is validated. From the simulation results, it is indicated that the SAGD recovery performance is more sensitive to the parameters of reservoir thickness, permeability (including horizontal and vertical), net-to-gross value and steam chamber pressure. Based on the gray related degrees and the sensitivity results, we proposed a six parameters nonlinear correlation to predict the recovery indicators of SAGD process. Thus, using this correlations, the recovery performance of several SAGD projects are predicted, and the correlation results are in good agreement with those obtained from numerical simulation. The prediction error of recovery factor and COSR is controlled within 10%. Furthermore, from the validation results, we found after a modification process, our correlation could be used. Our correlation is a static method to predict the recovery performance of SAGD process in heavy oil reservoir, and it could be used to successfully predict the recovery performance of SAGD projects in heavy oil reservoirs. Through the utilization of this correlation, the SAGD recovery performance in candidate oil reservoirs could be rapidly obtained.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".