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Record W4386803138 · doi:10.23977/acss.2023.070702

Study on Sensitivity of Plunger Characteristic Parameters to Gas Well Production

2023· article· en· W4386803138 on OpenAlexvenueno aff
Junping Wu, Yi Wang, Wanli Xiong, Zhichao Hu, Bilian Li

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPlungerDewateringPlunger pumpPetroleum engineeringExtraction (chemistry)Volume (thermodynamics)Natural gasSeparator (oil production)MechanicsMaterials scienceEngineeringMechanical engineeringChemistryWaste managementGeotechnical engineeringChromatographyThermodynamics

Abstract

fetched live from OpenAlex

Plunger dewatering gas extraction is a technology primarily used for low-yield natural gas collection. To maximize the efficiency of plunger dewatering gas extraction, optimization design is necessary. This study is based on the principles of plunger lifting dewatering gas extraction. By calculating the distribution of fluid and pressure in the wellbore during the plunger's upward movement at startup, as it reaches the wellhead, and during well shut-in, the interconversion relationship between plunger upward liquid discharge, self-jet flow, and well shut-in pressure recovery processes is analyzed. An optimization algorithm for plunger design is developed. When calculating the impact of plunger characteristic parameters on gas well production, the study considers the effect of gas compressibility factor varying with temperature and pressure, which makes the research results more realistic. The findings of this study contribute to optimizing the process parameters of plunger dewatering gas extraction and enhancing gas well production.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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