Robust optimization of the production profile of the steam assisted gravity drainage reservoir using a polynomial chaos expansion-based proxy model
Bibliographic record
Abstract
Uncertainty quantification is essential to ensure robustly stable operation in many engineering applications. Steam assisted gravity drainage (SAGD) reservoirs are large scale distributed parameter dynamical systems and pose significant challenges in quantifying the uncertainty. An effective data driven method to develop computationally inexpensive proxy models for the dynamic outputs and their uncertainty would be valuable for optimization and control. In this work, we present a proxy model developed using polynomial chaos expansion (PCE) for the cumulative oil production (COP) of a SAGD reservoir at different steam injection rates. The proxy model is used in robust optimization using statistical metrics of COP calculated over the entire parameter space. An ensemble of realizations of the cumulative oil production is simulated corresponding to the ensemble of the petro-physical properties over which the PCE model coefficients are estimated using collocation points over the orthogonal polynomial basis under an inner product relationship. Robust optimization is performed as a trade-off between maximum nominal performance and robustness and used to find the optimum steam injection rate for maximum oil production with minimum variability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".