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Record W4297916246 · doi:10.2118/210328-ms

Installation Steps and Fishing Design of Rigless Cable Deployed Conventional ESP System

2022· article· en· W4297916246 on OpenAlexaff
Mengpan Zhao, Yuandong Hong, Fanqian Zeng, Qiang Zhang, Lisha Zhu, Qijun Li

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2022
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsNova Chemicals (Canada)
Fundersnot available
KeywordsCable harnessReliability (semiconductor)Robustness (evolution)Computer scienceCompatibility (geochemistry)Reliability engineeringMarine engineeringEngineeringTelecommunicationsCable gland

Abstract

fetched live from OpenAlex

Abstract Cable Deployed Electrical Submersible Pumping (CDESP) System is a new generation of Rigless ESP technology, it utilizes the specialized cable for ESP changeout, which will save rig cost and reduce field operation time significantly.Cable deployed conventional ESP system is a game-changing technology in the Rigless ESP industry, which makes the CDESP compatibility possibleby using conventional ESP instead of inverted ESP assembly. However, reliability is always the main concern of clients, especially when it fails to retrieve ESP assembly by the specialized cable, a rig will have to be neededto carry out the ESP replacement jobs. This paper will introduce the design philosophy and installation procedure of Rigless Cable Deployed Conventional ESP System to demonstrate how to ensure the reliability and robustness. In addition, emergency fishing design is proposed to pull out from weak point in case of failing to retrieve ESPby cable and fully eliminate the rigs inESP replacement-jobs.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.224
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2022
Admission routes1
Has abstractyes

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