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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Application of Boosting Machine Learning for Mud Loss Prediction During Drilling Operations

2024SPE Nigeria Annual International Conference and ExhibitionPublication

10.2118/221583-ms Full text5 citations

Authors

4 authors across 3 institutions in 1 country.

M. I. OkaiFirst author
- Nile University of Nigeria (NG)
O. Ogolo
- Petroleum Training Institute (NG)
- Federal University of Petroleum Resource Effurun (NG)
Petrus Nzerem
- Nile University of Nigeria (NG)
as printed: P. Nzerem
Khairul S IbrahimSenior author
- Nile University of Nigeria (NG)
as printed: K. S. Ibrahim

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Drilling and Well Engineeringlevel 3 · 1.00
Hydraulic Fracturing and Reservoir Analysislevel 3 · 1.00
Mineral Processing and Grindinglevel 3 · 0.99

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2024 · SPE Nigeria Annual International Conference and Exhibition · 5 citations
https://openalex.org/W4401349730

Application of Boosting Machine Learning for Mud Loss Prediction During Drilling Operations · Sankore