Getting Smarter and Hotter With ESPs for SAGD
Bibliographic record
Abstract
Abstract This paper summarizes the results from a high-temperature test program completed in late 2009 and discusses a new electric submersible pumping (ESP) configuration that was validated by ConocoPhillips for operations at 250°C and planned for 2010 field trials. This new prototype ESP system was jointly tested by ConocoPhillips and Schlumberger for 42 days in the C-FER Technologies high-temperature flow loop at fluid temperatures ranging from 150°C to 260°C, and at 250°C and above for approximately 40% of the total time. While the primary objective of this test program was to validate this ESP system for use in 250°C fluid temperatures, the additional instrumentation at the test facility also offered an opportunity to investigate the temperature dynamics of the fluid flowing past the motor, and the pressure and temperature behavior of the motor. The test data was also compared to data from a similar test completed in 2008 and the evaluation provided some interesting observations, which will be discussed. The information in this paper will be of value to any operator that has ESPs installed in high-temperature applications. In addition, the lessons learned from this test program may also be used to increase ESP reliability in conventional applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".