Application of Intelligent Well Technology to a SAGD Producer: Firebag Field Trial
Bibliographic record
Abstract
Abstract Even temperature conformance along the length of the horizontal well is key to maximizing Steam Assisted Gravity Drainage (SAGD) production rates. When temperature logs are run in SAGD producers, temperature variations of greater than 50°C between the hottest and coldest spots are commonly observed. We theorize that this temperature distribution is related to an inflow distribution, and that production rates could be improved if this temperature variance was narrowed. It is difficult to influence conformance with traditional SAGD producer well design. Flow areas are large, and liquid velocities are low, resulting in small frictional pressure losses. It is not possible to impose a materially different drawdown on hot and cold spots along the horizontal with typical well completion methods. A field trial is ongoing at the Firebag project in which a production well is equipped with intelligent completion technology. The test well's horizontal liner section is split into four hydraulically isolated zones, with each zone having the ability to provide flow or isolation from the reservoir. The well completion is equipped with optical pressure and temperature (P/T) gauges and distributed temperature sensing (DTS) technology which monitors each segment's performance during operations. The capability to independently and immediately manipulate each segment's production inflow will provide the operator the ability to evaluate the influence of an intelligent completion design on a well's conformance and ultimate oil recovery.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".