Improving ESP Survivability in the SAGD Environment: An Integrated Approach to Managing ESP Performance in Challenging Conditions
Bibliographic record
Abstract
Summary Firebag SAGD (Steam Assisted Gravity Drainage) asset has shown steady growth in well count, with ESPs (Electric Submersible pumps) as the primary mode of lift. In the high temperature environment, robust design and material selections are important considerations to achieve long run lives. However, key constraints such as economic conditions, increasing well count and changing well performance require solutions that optimize cost and reliability. This is critical to offering flexibility in approaches to addressing the top failure modes of the ESPs at Firebag. The paper discusses Suncor's efforts to prioritize not only prevention, but also management of the key failure modes to optimize reliability and cost in the SAGD environment by developing a better understanding of the problem. This includes novel approaches to treat an installed ESP as a repairable system through cable-only replacement (non-serviced motor reruns) and re-landing an ESP as-is when a cable failure is located close to the surface. Risk mitigation is done primarily through data analysis of teardown information and statistical survivability rates at both the system and sub-component level. This risk mitigation includes building the foundation for information gathering through a clear teardown process on every ESP pulled from service, gaining insights on failures, challenging traditional assumptions based on the data obtained, and driving focused trials and initiatives.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".