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Record W2925819463 · doi:10.2118/195186-ms

Improved Rod-Pump Run Time in Sandy Wells Two Stages Filtration

2019· article· en· W2925819463 on OpenAlexaff
John VanNatta, Phillip Logan, Alyssa Stieg, Gustavo Gonzalez, Luis Guanacas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsFiltration (mathematics)Control (management)Production (economics)Computer scienceDeposition (geology)Identification (biology)Process engineeringPetroleum engineeringEngineeringReliability engineeringEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Abstract Failures due to solid particles flowing with the production fluid is one of the main causes of interventions in wells with beam pumping systems. When this problem is accompanied with chemical deposition like scale, leads to a very common intervention during well operation. This paper proposes an analytical methodology that consists of evaluation of the particle size distribution, viability for the use of sand screens and centrifugal separation systems for sand control management in wells with short run time. These systems have proven effective for failure wells that requires a sand control management system when if not addressed increase the lifting costs leading many projects to be infeasible from an economic standpoint. All the technical considerations are explained focusing on the information required and the parameters analyzed to recommend the most accurate design for sand control; selected approaches and models that have been developed to improve the run time due to sand issues are shown in this paper. A case study is showed in a well with average run time of 27 days indicating that identification of particle size distribution was a key factor to provide the right solution for sand control management. These novel applications help operators to reduced OPEX (operating expense), by minimizing well Interventions, decreasing failures in the pump; stabilizing the production and reducing the unforeseen interruption.

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 categoriesInsufficient payload (model declined to judge)
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 score0.043
Threshold uncertainty score1.000

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 head0.209
Teacher spread0.205 · 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.

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
Published2019
Admission routes1
Has abstractyes

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