Real‐time multivariable model predictive control for steam‐assisted gravity drainage
Bibliographic record
Abstract
Thermal recovery techniques, such as steam‐assisted gravity drainage (SAGD), are used to recover the majority of the crude bitumen, in Western Canada. However, suboptimal production techniques have led to a large carbon footprint and a subsequent search for more efficient extraction techniques, than open loop manual control. This article summarizes research on the comparison of performance of a novel multi‐input multioutput (MIMO) model predictive controller (MPC) with steam trap and oil rate controls with a multi‐input single output (MISO) MPC with only steam trap control. An appropriate system identification technique was also used for periodic model update in compliance with changing system behavior. The real‐time control study was made possible by establishing a bidirectional communication between computer modeling group STARS TM (virtual reservoir) and MATLAB (onsite controller) software. The results show a 171% improvement in oil recovery for the novel MIMO MPC over the MISO MPC. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3034–3041, 2018
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".