Chance-Constrained Model Predictive Control for SAGD Process Using Robust Optimization Approximation
Why this work is in the frame
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Bibliographic record
Abstract
Control of a steam-assisted gravity drainage (SAGD) process is a challenging task, because of the presence of various uncertainties, such as geological uncertainty and steam quality uncertainty. They often lead to constraint violations and performance degradation. In this work, a chance-constrained model predictive control (CCMPC) method is presented to generate a safe and optimal control strategy, considering the presence of uncertainties. A novel robust optimization method is applied to solve the chance-constrained optimization problem under general distribution of uncertainties. Two case studies are presented to demonstrate the proposed approach. Furthermore, the modeling of SAGD process is discussed, and the proposed robust optimization-based CCMPC is tested using a reservoir simulator (Petroleum Experts) of the SAGD process. The proposed approach reduces constraint violations that are due to uncertainties and achieves satisfactory performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it