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Record W4322493006 · doi:10.24018/ejece.2023.7.1.496

Design and Performance Analysis of an Oil Pump Powered by Solar for a Remote Site in Nigeria

2023· article· en· W4322493006 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Journal of Electrical Engineering and Computer Science · 2023
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsSpillageSizingEnvironmental scienceOil productionProduction ratePetroleum engineeringCrude oilWaste managementEngineeringProcess engineering

Abstract

fetched live from OpenAlex

Oil companies typically abandon stripper wells with production rates below 15 barrels per day because the production and maintenance cost exceed the pumping rate. Oil spillage is the primary cause of low production rates; an example of such failure is the Oloibiri oil well in Nigeria. During the peak of operation, the flow rate was 5,100 barrels per day in 1960 and was abandoned due to the declining production rate. The Oloibiri oil site has 18 drilled wells, and only the oil well 17 can be classified as stripper well. Because Nigeria has high solar irradiance and insolation, a proper PV system sizing for a solar-powered pump that should lift oil from a depth of 3800 metres at a flow rate of 15 barrels per day is evaluated for two different running times. In that way, the solar-powered pump will be used to solve the ongoing issue of stripper oil wells by curbing oil spillage from the oil wells abandoned by these production companies and rendering a low-cost pumping system. This paper evaluates the pump performance and completes the system design. It compares the system design to the PVsyst and HOMER sizing.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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