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Okorie Ekwe Agwu

Observed names: Agwu, Okorie .E., Agwu, Okorie Ekwe, O. E. Agwu, Okorie E. Agwu, Okorie Agwu, Okorie Agwu Ekwe, Okorie E Agwu, Okorie E. Agwu, Okorie E. Agwu .
ORCID: 0000-0003-3855-8352
Observed ORCIDs: 0000-0003-3855-8352, 0009-0009-7645-2023
OpenAlex ID: A5053185056
A critical review of drilling mud rheological models
2021 · Okorie E. Agwu, Julius U. Akpabio, et al. · Journal of Petroleum Science and Engineering · 162
Artificial intelligence techniques and their applications in drilling fluid engineering: A review
2018 · Okorie E. Agwu, Julius U. Akpabio, et al. · Journal of Petroleum Science and Engineering · 154
Using agro-waste materials as possible filter loss control agents in drilling muds: A review
2018 · Okorie E. Agwu, Julius U. Akpabio · Journal of Petroleum Science and Engineering · 77
A comprehensive review of laboratory, field and modelling studies on drilling mud rheology in high temperature high pressure (HTHP) conditions
2021 · Okorie E. Agwu, Julius U. Akpabio, et al. · Journal of Natural Gas Science and Engineering · 74
A Comparative Study of Diesel Oil and Soybean Oil as Oil-Based Drilling Mud
2015 · Okorie E. Agwu, Anietie N. Okon, et al. · Journal of Petroleum Engineering · 70
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Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies
2018 · Okorie E. Agwu, Julius U. Akpabio, et al. · Powder Technology · 65
Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells
2019 · Okorie E. Agwu, Julius U. Akpabio, et al. · Journal of Petroleum Exploration and Production Technology · 50
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Rice husk and saw dust as filter loss control agents for water-based muds
2019 · Okorie E. Agwu, Julius U. Akpabio, et al. · Heliyon · 43
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Artificial neural network model for predicting drill cuttings settling velocity
2019 · Okorie E. Agwu, Julius U. Akpabio, et al. · Petroleum · 33
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Modelling oil and gas flow rate through chokes: A critical review of extant models
2021 · Okorie Ekwe Agwu, Emmanuel Emeka Okoro, et al. · Journal of Petroleum Science and Engineering · 28
Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps
2024 · Okorie Ekwe Agwu, Ahmad Alkouh, et al. · Journal of Petroleum Exploration and Production Technology · 27
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Carbon capture using ionic liquids: An explicit data driven model for carbon (IV) Oxide solubility estimation
2024 · Okorie Ekwe Agwu, Saad Alatefi, et al. · Journal of Cleaner Production · 26
Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen
2024 · Saad Alatefi, Okorie Ekwe Agwu, et al. · Results in Engineering · 24
Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure
2024 · Saad Alatefi, Okorie Ekwe Agwu, et al. · Process Safety and Environmental Protection · 24
Toward explicit learning frameworks for predicting the solubility of CO2 – N2 gas mixtures in brine: Implication for impure CO2 storage in saline aquifers
2025 · Saad Alatefi, Menad Nait Amar, et al. · Journal of Contaminant Hydrology · 23
Applications of artificial intelligence algorithms in artificial lift systems: A critical review
2024 · Okorie Ekwe Agwu, Saad Alatefi, et al. · Flow Measurement and Instrumentation · 23
Advancing hydrogen storage: Explainable machine learning models for predicting hydrogen uptake in metal-organic frameworks
2025 · Saad Alatefi, Okorie Ekwe Agwu, et al. · Results in Engineering · 22
Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents
2025 · Saad Alatefi, Okorie Ekwe Agwu, et al. · Fuel · 22
Potentials of waste seashells as additives in drilling muds and in oil well cements
2020 · Okorie Ekwe Agwu, Julius Udoh Akpabio, et al. · Cleaner Engineering and Technology · 22
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Evaluation of the Cuttings Carrying Capacity of a Formulated Synthetic-Based Drilling Mud
2015 · Anietie N. Okon, Okorie E. Agwu, et al. · SPE Nigeria Annual International Conference and Exhibition · 20
Explicit Data-Based Model for Predicting Oil-Based Mud Viscosity at Downhole Conditions
2024 · Ahmad Alkouh, Khaled Elraies, et al. · ACS Omega · 19
Accurate prediction of water activity in ionic liquid-based aqueous ternary solutions using advanced explainable artificial intelligence frameworks
2025 · Saad Alatefi, Menad Nait Amar, et al. · Chemical Engineering Science · 18
Drilling fluid design for depleted zone drilling: An integrated review of laboratory, field, modelling and cost studies
2023 · Cosmas B. Orun, Julius U. Akpabio, et al. · Geoenergy Science and Engineering · 18
A Review of Nigerian Bentonitic Clays as Drilling Mud
2015 · Okorie E. Agwu, Anietie N. Okon, et al. · SPE Nigeria Annual International Conference and Exhibition · 17
Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines
2023 · Okorie Ekwe Agwu, Khaled Abdalla Elraies, et al. · Geoenergy Science and Engineering · 15
Works count: 73
Citation count: 1,220
H-index: 20
I10-index: 31
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