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Record W4401679450 · doi:10.2118/219516-ms

Vortex Barbell System Improving Sucker Rod Pump Efficiencies and Decreasing Failure Frequencies in High-Angle Wellbores

2024· article· en· W4401679450 on OpenAlexaff
Corbin Coyes, Colby Jensen, J. Hardin, J. Desautels, J. Páez, B.W. Williams, J. Saponja, David J. Holcomb, A.G. Dixon, R. Beeton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsRed Deer Polytechnic
Fundersnot available
KeywordsSucker rodVortexMechanicsPhysicsMaterials scienceControl theory (sociology)EngineeringMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The conventional sucker rod pump system is comprised of ball and seat-type traveling and standing check valves that use positive displacement to produce oil from the reservoir (Takacs 2015). With the advent of horizontal drilling and fracturing, sucker rod pumps are increasingly landed at greater depths and inclinations from vertical to maximize access to reserves and extend the production life of the well. This paper builds upon existing downhole vortex valve systems research to test and understand the potential of a vortex fluid flow profile. The Vortex Barbell System (VBS) was designed to establish and elongate a vortex fluid flow profile that sustains fluid velocity in downhole environments. Early field trials with the VBS showed promising capability to land pumps in the curve with production success. This prompted further laboratory testing. The laboratory environment was designed to model downhole fluid conditions and wellbore trajectory. The results were compared to field data from wells running the VBS. The analysis draws connections between laboratory results and field trials to explain how the VBS elongates a vortex flow profile, sustains fluid velocity, and increases pump fillage so that rod pumps can be landed deeper in high inclination wells (in the curve) to retain pump efficiency, increase production, reduce failure frequency, and extend the pump life.

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 score0.329
Threshold uncertainty score0.639

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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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
Published2024
Admission routes1
Has abstractyes

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