Analysis of the Performance of Various Well Liner Completion Strategies in Surmont
Bibliographic record
Abstract
Abstract ConocoPhillips operates Surmont, which is the first Steam-Assisted Gravity Drainage (SAGD) project to implement Flow Control Devices (FCDs) in producer wells. This study was conducted to evaluate the production performance of different liner completion strategies. The analysis compared well pairs completed with slotted liners (SL) to producers completed with FCDs, both liner deployed (LD-FCD) and tubing deployed (TD-FCD), and investigated the impact of FCDs in injectors. An extensive analysis was conducted using available production and temperature data along the wells. The wells were completed using various fixed-resistance FCD settings, while some wells were completed using variable setting designs. As time went on, several of the slotted liner producer wells were retrofitted with tubing-deployed FCD completions. One of the key objectives of the study was to determine the success rate of tubing-deployed FCDs and their performance relative to liner-deployed FCD wells. Another objective was to evaluate the impact of retrofitting slotted liner SAGD injectors with tubing-deployed FCD completions. In this study, a grading system was established based on the reservoir quality along the well for both injector and producer. For similar graded well pairs, LD-FCDs had better production performance than TD-FCDs. Considering similar graded reservoir quality, FCDs consistently performed better than slotted liners, in both conformance and production acceleration. The production analysis showed that the FCD flow restriction was a major controller of the conformance, but considering the self-choking phenomenon of the reservoir, most FCDs can perform positively in different circumstances. In this study, the self-choking effect of the liquid pool is discussed and explained for different reservoirs and variable subcool. Generally, if erosion is not a factor, FCDs can create a more controlling system than liquid-pool dominant systems. In these cases, both conformance and production acceleration is enhanced if operators yield lower subcools and greater draw-down pressures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".