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Record W2973610746 · doi:10.2118/195848-ms

Breaking Barriers: 350°C Rated Permanent Magnet Motor Design for Electric Submersible Pumps in SAGD wells

2019· article· en· W2973610746 on OpenAlexaff
Hassan Mansir, Michael Rimmer, Leon Waldner, John Graham, Claire Hong, Kerry Wycislik, Bruce Duong

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsAlberta InnovatesCenovus Energy (Canada)Suncor Energy (Canada)
Fundersnot available
KeywordsReliability (semiconductor)MagnetElectric motorAutomotive engineeringEngineeringDC motorMechanical engineeringPetroleum engineeringEnvironmental scienceElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The development of a High-Temperature Permanent Magnet Motor (PMM) was initiated with the main objective to bring forth a technical solution to significantly increase temperature capability and run life of ESPs in Steam Assisted Gravity Drainage (SAGD) beyond current technology. This is in response to operators needs for improved safety margins and increased production rates. Existing ESP motor technologies are limited to approximately 300°C internal motor winding temperatures, driven by the available motor electrical insulation systems. The use of PMMs in SAGD was also prohibited by the availability of magnet materials capable of operating in such temperatures, without partial or full de-magnetization. The project's aim is to break this barrier and extend internal temperatures to 350°C and beyond, allowing well ambient temperatures to be pushed beyond the 260°C downhole environment. In addition, for assurance of motor reliability, rigorous and methodical design validation and qualification testing of basic materials, components, sub-assemblies were undertaken.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.230
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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