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Record W2091779225 · doi:10.2118/137168-ms

Multiphase Progressive Cavity Pumps Operated In Harsh Conditions

2010· article· en· W2091779225 on OpenAlexaff
Laurent Seince, David Caballero, N.. Chacin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsCanadian Society of Microbiologists
Fundersnot available
KeywordsOverheating (electricity)Test benchMechanical engineeringAbrasion (mechanical)SuperheatingMaterials scienceProgressive cavity pumpEngineeringPetroleum engineeringNuclear engineeringComputer scienceElectrical engineeringHydraulic pumpReciprocating pumpThermodynamics

Abstract

fetched live from OpenAlex

Abstract The multiphase progressing cavity pump, PCM Moineau HR (Hydraulically Regulated Progressive Cavity Pump), is a major improvement in the operating philosophy of the traditional PCP. By design, the volumetric pump is capable of handling solid, liquid and vapor phases. However, solid leads to abrasion and vapor to overheating and reduced life span where liquid is usually the added value in the process. Several papers have been issued at the SPE PCP, showing first the promising characteristics of this new principle on test bench and then the first industrial version in Venezuela. The learning curve associated with the failures has enabled to come up with a new design that resolves the abrasion and the mechanical weakness of the former NPCP (old name for this multiphase pump), keeping the core advantages of the multiphase design, that is balancing pressure and temperature homogeneously all along the pump, to get an optimum performance and a mucher higher runlife. This paper presents the case story of a HRPCP installed in Venezuela with Equimavenca, running in harsh condition with heavy gassy oil, as well as the improvement performed ever since. It has been running for more than 20 months, producing 1600 to 2300 scf/sbl of gas.

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 score0.093
Threshold uncertainty score0.677

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)0.0010.000

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 head0.004
GPT teacher head0.236
Teacher spread0.233 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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