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Record W2071713133 · doi:10.2118/134528-ms

Getting Smarter and Hotter With ESPs for SAGD

2010· article· en· W2071713133 on OpenAlexaff
Shauna Noonan, Maura Dowling, Lindsay D’Ambrosio, W. Klaczek

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2010
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsInstrumentation (computer programming)Reliability (semiconductor)Test (biology)Fluid dynamicsComputer scienceFlow (mathematics)Environmental scienceSimulationPetroleum engineeringNuclear engineeringMechanical engineeringEngineeringGeologyOperating systemPhysicsMechanics

Abstract

fetched live from OpenAlex

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.

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 score0.414
Threshold uncertainty score0.351

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 head0.008
GPT teacher head0.222
Teacher spread0.213 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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