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Record W4386996159 · doi:10.2118/214742-ms

Collaborative Development of a Shared ESP Troubleshooting Guidelines Document

2023· article· en· W4386996159 on OpenAlexaff
Sean Kennedy, Theophilus Olugbenga Babatunde, Federico Gaviria, Richard Delaloye, Leora Waldner, Charles E. Radke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsTroubleshootingWorkflowComputer scienceSet (abstract data type)Identification (biology)Knowledge managementAsk priceSoftware engineeringProcess managementWorld Wide WebEngineeringDatabaseOperating system

Abstract

fetched live from OpenAlex

Abstract When an electric submersible pump (ESP) has tripped or is not operating as intended, operators need to quickly assess the underlying factors to determine if any corrective measures can be taken and if the ESP needs to be pulled. To assist, a new Troubleshooting Guidelines Document (TGD) has been collaboratively developed, incorporating the knowledge and experience of several ESP operators with diverse ESP applications and geographies. The TGD development involved a group of ESP operators agreeing on a logical and easy-to-follow document structure, reviewing and consolidating existing industry resources, and contributing technical knowledge based on the operators’ own experiences and internal practices. The focus was on documenting the knowledge and experience required to ask the right questions during a troubleshooting scenario, which is often stored only in the minds of experienced individuals (sometimes referred to as institutional knowledge). Asking the right questions during troubleshooting can help operators avoid unnecessary well interventions and reduce the time required to bring the system back online. The TGD is intended to serve as a technical reference for anyone involved in ESP troubleshooting and to provide the end-user with a structured troubleshooting workflow and list of questions to ask and probable solutions. This paper will illustrate the workflow based on two example ESP troubleshooting scenarios. The workflow starts with a set of observations describing the symptoms of the problem and leads to the identification of potential contributing factors, diagnostic checks, and corrective actions. The example scenarios will illustrate the structured workflow for complex troubleshooting situations and highlight some aspects or questions that may often be overlooked. The TGD is intended to compliment, not replace, operator site specific procedures and best practices and provides reminders that only competent and qualified personnel should execute troubleshooting tasks. The TGD is considered a "living" document, with new experience, tools, technologies, and knowledge being continually added. To the authors’ knowledge, it is the only shared, living ESP troubleshooting document of its kind that contains input from multiple ESP applications and geographies. The troubleshooting procedure outlined in the paper can be used in any ESP application and can provide a structured way for companies to capture and apply their ESP troubleshooting experience.

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)0.039
metaresearch head score (Gemma)0.072
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: Other · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.072
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.003
Science and technology studies0.0040.002
Scholarly communication0.0100.007
Open science0.0040.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.006

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 head0.026
GPT teacher head0.294
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 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
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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