Bibliographic record
Abstract
Abstract Steam-Assisted Gravity Drainage (SAGD) is a widely used in situ recovery process for heavy oil and bitumen reservoirs. The performance of the SAGD process is tied with growth of the steam chamber which in turn depends on uniform steam delivery along well length and the underlying geology and fluid properties in the near wellbore region. If the reservoir has poor injectivity due to poor underlying geology oil production suffers. This can be avoided in by examining the interwell subcool. The subcool is the temperature difference between the injected steam and produced fluids. In this study, Proportional-Integral-Derivative (PID) feedback control has been employed to control inflow control valves settings to promote subcool to a target value. This control strategy is examined by using a PID algorithm to control SAGD in a detailed three-dimensional heterogeneous reservoir model with properties typical of an Athabasca bitumen reservoir. Specifically, the SAGD injector is divided into six intervals each with its own steam injection pressure. The interwell subcool is calculated and the PID feedback control algorithm is used to direct the subcool to a target value by changing the steam injection pressure in each well interval. The results show that this control method can be used to enhance uniform steam chamber growth and ultimately more oil production with less steam injection. The key benefit of dynamic well control is that the injection strategy is adjusted dynamically to fit the underlying geological and fluid compositional heterogeneity to obtain improved steam conformance along the wellpair. This implies that potentially a priori detailed knowledge of the geological and fluid compositional heterogeneity may not be as critical for well placement for uniform steam conformance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".