SAGD Pair Performance Optimization: A Field Case of Recovery Enhancement
Bibliographic record
Abstract
Abstract Performance of fields under thermal recovery processes has been considerably improved by experimenting with new techniques and innovative operational solutions in heavy-oil and extra heavy-oil fields. By far, steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) methods have been the most compelling and have proven a successful history in these oil fields. At the Celtic heavy-oil field, Husky Energy experienced three phases of optimization over the course of ten years of recovery. The Celtic field consists of 28 SAGD pairs located in the Lloydminster heavy-oil region that have been in service since 2001. Among these pairs, there are three SAGD pairs (F-pad east) with early productivity challenges in addition to down-time caused by sand production. Following new geological realization and reservoir studies, the injector wells were converted to production wells performing horizontal CSS process. In 2008, and in an effort to maintain production, a detailed numerical simulation study was conducted to compare different steam support configurations and the result was justified for field execution. The outcome resulted in four directional steam-injection wells (G-Pad) permanently started to support the current horizontal producers. In 2009, the new configuration commenced and is still in operation today. Communication with horizontal wells has been completed on one side of the pattern and it has been gradually extending to other side. Recently acquired field data suggest that the process is exceeding the expected oil profile and its steam consumption is less than that was anticipated. The geological review, history of the F-Pad East pairs, results of numerical simulation study, and recommendations are presented in this paper.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".