Evaluating the Performance of Advanced ESP Motor Technology in a Steam Assisted Gravity Drainage SAGD Field in Canada
Bibliographic record
Abstract
Abstract A significant percentage of all ESP failures are electrical failures and this becomes even more noteworthy in harsh, high temperature applications such as Steam Assisted Gravity Drainage (SAGD). For this reason, it is extremely important to continue the enhancement of ESP motor technologies that are specifically designed to address the challenging and unique SAGD environments that include wide bottom hole temperature ranges, abrasives and gas rich fluids. Through experience and testing, it has been learned that for these types of applications it imperative to design not only to a high temperature limit, but also to withstand extreme temperature cycles experienced on steam injection facility shutdown. A combination of historic evidence with controlled laboratory evidence yielded improvement areas for a new high-temperature ESP motor development. The new high ultra-temperature motor breaks paradigms and opens a new generation of motors that looks towards above 300°C downhole temperatures. This paper will review the performance of the motor at Suncor's Firebag SAGD field where 92 units have been installed since January 2015 in bottom hole (BHT) temperatures reaching 240°C. Description of the laboratory qualification, major design characteristics and field results will also be discussed on the paper.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".