Graph-based structural controllability and observability of steam assisted gravity drainage pressure chamber: A data driven approach
Bibliographic record
Abstract
Distributed parameter processes are challenging when it comes to modeling and control. Steam assisted gravity drainage (SAGD), used for in-situ extraction and recovery of oil sands bitumen, is a large scale distributed parameter process. The analysis of control relevant properties like controllability and observability enables to address the problem of control of steam chamber growth and sensor placement. We present a data driven and computationally affordable technique to assess the controllability and observability of the SAGD steam chamber dynamics in a structural perspective by exploiting the underlying interaction amongst different regions of the reservoir. A reservoir simulator is used to gather the data, and density-based clustering combined with Granger causality is used to develop a directed graph through which the structural controllability and observability of the SAGD process is characterized. This paper presents a detailed procedure and results for the sensor and actuator locations for partial and full controllability and observability to validate the discussed approach using the data acquired from the CMG-STARS simulator. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".