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Record W4290471517 · doi:10.2118/209914-ms

Cable Deployed ESP System Applied with Conventional ESP Assembly: A New Generation of Rigless ESP Technology

2022· article· en· W4290471517 on OpenAlexaff
Mengpan Zhao, Qiang Zhang, Ping Wei, Yuandong Hong, Qijun Li, Lisha Zhu

Bibliographic record

VenueIADC/SPE Asia Pacific Drilling Technology Conference and Exhibition · 2022
Typearticle
Languageen
FieldEngineering
TopicBelt Conveyor Systems Engineering
Canadian institutionsNova Chemicals (Canada)
Fundersnot available
KeywordsCable harnessFactory (object-oriented programming)Cable glandRobustness (evolution)Reliability (semiconductor)Computer scienceEngineeringReliability engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A new generation of Rigless ESP technology, Cable Deployed ESP (CDESP) System uses specialized cable to replace ESP, which saves rig cost and reduces production deferment tremendously. However, most CDESP in the market are applied with inverted ESP assembly, which has high requirements and more uncertainties on customized ESP. This paper will introduce the design philosophy of a CDESP applied with conventional ESP assembly, which is more compatible. Rounds of multiple factory tests were successful, pressure test of hanger assembly and penetrator system were up to 7,500 psi,cable connector tension tests meet the specialized cable and ESP weight requirement. Also, the electrical tests were qualified. The factory tests and field applications verified the reliability and robustness of this new generation of Rigless ESP technology.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.191
Teacher spread0.176 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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