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Record W2156338596 · doi:10.1109/icps.2012.6229611

Power consumption evaluation for electrical submersible pump systems

2012· article· en· W2156338596 on OpenAlexaff
Xiaodong Liang, Ernesto Fleming

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsSubmersible pumpRule of thumbPower consumptionReliability engineeringEnergy consumptionComputer sciencePower (physics)Consumption (sociology)Electric power systemElectric powerProduction (economics)Evaluation methodsSet (abstract data type)Automotive engineeringEngineeringMarine engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Due to cost increase in the current energy market, the requirement for power consumption evaluation and improvement for electrical submersible pump (ESP) systems remains strong. However, there are no developed standards for such evaluation, which makes it a difficult task for the oil industry. This paper intends to introduce suitable approaches and general criteria for the power consumption evaluation and improvement purpose. A comprehensive study and research is performed using two methods to evaluate power consumption for ESP systems. Method 1 is to determine power consumption using the measured KW-hours during a specific period divided by the production rate in barrels per day and by lifted fluid in feet. Method 2 is to calculate overall pumping system efficiency. The status of the two existing methods is that Method 1 is developed with some rule of thumb criteria, while Method 2 unfortunately only consists of a set of calculation formula but no criterion available. Relationship between the two methods is analyzed. General criterion for power consumption evaluation and improvement for ESP wells are proposed in the paper. A case study is provided to clearly demonstrate the application of the criterion.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.210

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)0.0000.000

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 head0.279
Teacher spread0.247 · 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 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

Citations6
Published2012
Admission routes1
Has abstractyes

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