Optimization of a Vertical-Horizontal Heavy Oil Thermal Operation by using Automated Field-scale Control
Bibliographic record
Abstract
Abstract Given its water consumption and greenhouse gas emissions to the atmosphere, it is critical to optimize the steam injection strategy and steam conformance in steam-based oil sands recovery processes such as cyclic steam stimulation and steam- assisted gravity drainage to minimize the steam-to-oil ratio and maximize the cumulative oil volume produced. Given the heterogeneity of oil sands reservoirs, robust adaptive oil sands processes which respond to the system to continuously shift the operation towards desired operational objectives are desired. This kind of adaptive control can be achieved by proportional-integral-derivative (PID) control. PID automated control is a relatively simple method to control well operations in thermal processes by using observed data already measured in existing steam-based recovery processes. Here, a vertical-horizontal hybrid well configuration within an oil sands reservoir, with reservoir properties previously tuned by a history-match to field data, is operated under PID control to demonstrate that automated control can yield improvements of recovery process performance. Here, the well configuration consists of multiple vertical injectors and a few horizontal producers – early in the process, cyclic steam stimulation is initially done in the vertical wells to establish thermal communication within the reservoir. After sufficient communication is accomplished, the steam is injected into the vertical wells and fluids are produced through the horizontal wells in a steam-assisted gravity drainage mode. Automated control must accomplish thermal communication between the vertical and horizontal wells and thereafter the steam must be controlled to minimize the steam-to-oil ratio and maximize the oil rate. The results demonstrate that PID control can be used to improve the cSOR and volume of oil produced.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".