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Record W1975445077 · doi:10.1115/1.2000274

Optimum Design Parameters for Reciprocating Pumps Used in Natural Gas Wells

2005· article· en· W1975445077 on OpenAlexafffund
Jeffrey J. Rudolf, T. R. Heidrick, Brian A. Fleck, V. S. V. Rajan

Bibliographic record

VenueJournal of Energy Resources Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsUniversity of AlbertaHusky Energy (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReciprocating motionNatural gasPetroleum engineeringFlow (mathematics)Plunger pumpMechanical engineeringEngineeringMaterials scienceEnvironmental scienceMechanicsWaste managementPhysics

Abstract

fetched live from OpenAlex

Experimental and theoretical investigation of a recently patented down-hole direct-acting reciprocating pump system is presented. The technology, (US Patent No. 5,860,795) consists of operating a gas well with gas and liquid phases being produced separately by using the gas phase to power a pump to bring the liquid phase to the surface. This would increase the duration of profitability of many gas wells in North America. Experiments and modeling were used to determine optimum design parameters to maintain flow at a minimum reservoir pressure; an optimum area ratio for the gas/liquid pistons is approximately 40. The effect of friction in the pumping system was predicted to have a small effect on this optimum design. The results of this investigation will now be used to design and construct a prototype for field testing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.012
GPT teacher head0.225
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2005
Admission routes2
Has abstractyes

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