Design and Performance Analysis of an Oil Pump Powered by Solar for a Remote Site in Nigeria
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".