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Record W2593701668 · doi:10.2118/183882-ms

Evaluating the Performance of Advanced ESP Motor Technology in a Steam Assisted Gravity Drainage SAGD Field in Canada

2017· article· en· W2593701668 on OpenAlexafffundabout
John Graham, Bryan Coates, Carlos Montilla, Oscar Hernán Madrid Padilla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsSuncor Energy (Canada)
FundersSuncor Energy Incorporated
KeywordsSteam-assisted gravity drainagePetroleum engineeringMotor oilElectric motorEngineeringEnvironmental scienceDrainageSteam injectionAutomotive engineeringMechanical engineeringAerospace engineeringMaterials science

Abstract

fetched live from OpenAlex

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.821

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 head0.016
GPT teacher head0.279
Teacher spread0.264 · 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 designOther design
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

Citations4
Published2017
Admission routes3
Has abstractyes

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