Improving ESP reliability in Direct-To-SAGD completions: A Case Study
Bibliographic record
Abstract
Summary One of the methods to establish continuous emulsion production between producer and injector wells in a steam-assisted gravity drainage (SAGD) scheme, is steaming the well-pair simultaneously. As for the producer wellbore, a typical completion implies a two-stage workover with installation of the Electric Submersible Pump (ESP) and instrumentation right after the completion of the steaming phase. A novel method of a single stage workover - going direct to SAGD with steaming past the ESP's was trialed at Suncor Energy's Firebag field in Northern Alberta. The first full direct-to-SAGD (D-SAGD) Firebag pad faced multiple challenges during steaming phase and production startup. One of them was long-term exposure of the ESP systems to the high temperature steam along with challenging temperature cycling during the fall-off tests that contributed to early failures in the power delivery system. Continued D-SAGD implementation needed a system that is capable of withstanding high-temperature steam exposure for several months along with multiple temperature cycles. After thorough engineering analysis of failure modes, a plan was established to deliver a new, redesigned system. Multiple new concepts have been developed for mechanisms and materials with the criteria to meet the reliability requirements and were successfully qualified after being subjected to extensive testing. As a result, an ESP system specific to D-SAGD style of completion was developed, deployed, and successfully started. New design has contributed to positive economic and environmental effects at both Firebag and MacKay River projects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".