Technical Review of Tubing Deployed Flow Control Devices at MacKay River
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage (SAGD) is a complex process and often requires more control relative to conventional applications during production operations. Flow Control Devices (FCDs) have been identified as a technology that offers improved efficiency of the process while simplifying the operations. The first FCD completions were installed in SAGD wells in Canada over a decade ago with the intention of improving the steam chamber conformance and reducing the steam-oil ratio (SOR). While it is widely understood that FCD completions, for the most part, have helped achieve the desired uplift for SAGD producers, further optimization could be made on future completion designs and operation strategy by looking at actual performance data from previous installations. The objective of the study was to obtain key design parameters and considerations for future FCD completion designs. The majority of FCD completions in MacKay River were tubing deployed, installed in previously producing wellbores (retrofit). This study looks at 11 wells that were completed with a Baker Hughes FCDs. The analysis was broken down into 2 segments: production analysis and modelling. Production strategy implemented for each well was taken into account to eliminate variances. The modelling used a combination of steady state simulation (presented in this paper) and numerical simulation (to be presented in part II). The study showed that TD FCDs improve the performance of SAGD well pairs when implemented in the appropriate candidate wells. An important outcome was the development of a candidate wells’ selection criteria, to ensure the retrofit completion improved performance and did not exacerbate other problems. Furthermore, design consideration were identified to improve the performances of future TD FCD installations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".