Real‐time feedback control of SAGD wells using model predictive control to optimize steam chamber development under uncertainty
Bibliographic record
Abstract
ABSTRACT The efficiency of steam assisted gravity drainage (SAGD) operation depends on developing a uniform steam chamber by maintaining an optimal subcool temperature along the length of the well pair. Implementing operational parameters obtained from model‐based optimization directly in the field may not lead to the desired subcool temperature. Based on the real‐time measurements from surface and downhole sensors, along with other well and surface constraint information, a real‐time feedback control of SAGD well pairs can be implemented to optimize subcool and steam chamber development. Model predictive control (MPC), which is a multivariable constrained controller, provides a framework for such control. To evaluate the use of MPC for real‐time control of SAGD wells, a case study is performed using a 3D heterogeneous reservoir model. Porosity and permeability realizations are created and ranked based on net present value (NPV). One of the realizations is considered as the ‘true’ reservoir, and two other realizations are selected to represent different cases of uncertain reservoir models. For each model, a reservoir simulator is used to find the optimum rates and subcool temperature, which become the set‐points for MPC to operate the wells. We compare the steam chamber growth and NPV calculated using MPC with the base case where no MPC is used and discuss the implementation advantages. Since MPC led to expedited and accurate tracking of optimized operating targets obtained using reservoir models, ∼18 % improvement in NPV is achieved compared to manual open‐loop application of optimum rates, a practice used commonly in the field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".