SAGD-Pair Performance Optimization: A Field Case Study of Recovery Enhancement
Bibliographic record
Abstract
Summary Performance of fields under thermal recovery processes has been improved considerably by experimenting with new techniques and innovative operational solutions in heavy-oil and extra heavy oil fields (Butler 1994; Singhal et al. 1998). By far, steam-assisted-gravity-drainage (SAGD) and cyclic-steam-stimulation (CSS) methods have been the most compelling and have shown a successful history in these oil fields (Butler et al. 2000; Butler 2001). At the Celtic heavy-oil field, Husky Energy experienced three phases of optimization over the course of 10 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 downtime caused by sand production. Following new geological realization and reservoir studies, the injector wells were converted to production wells performing a 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 confirmed on one side of the pattern, and it has been extending gradually to the other side as it was investigated in other work (Edmunds 1991; Al-Salhi et al. 1997). Recently, acquired field data suggest that the process is exceeding the expected oil profile and its steam consumption is less than anticipated. The geological review, history of the F Pad East pairs, results of numerical-simulation study, and recommendations are presented in this paper.
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 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 | 0.002 | 0.001 |
| 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".