Agwu, Okorie Ekwe

Agwu, Okorie Ekwe

Dr.

Positions

Member, Centre of Reservoir Dynamics , Institute of Sustainable Energy & Resources
Senior Lecturer, Petroleum Engineering Department , Faculty of Engineering
Senior Lecturer, Universiti Teknologi PETRONAS 2010 -

member of

Petroleum Engineering Department

selected publications

academic article
- Applications of artificial intelligence algorithms in artificial lift systems: A critical review. Flow Measurement and Instrumentation. 97:102613. 2024
- Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure. Chemical Engineering Research and Design. 205:672-694. 2024
- Explicit Data-Based Model for Predicting Oil-Based Mud Viscosity at Downhole Conditions. ACS Omega. 9:6684-6695. 2024
- Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines. Geoenergy Science and Engineering. 233:212584. 2024
- Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps. Journal of Petroleum Exploration and Production Technology. 14:1205-1233. 2024
- Drilling fluid design for depleted zone drilling: An integrated review of laboratory, field, modelling and cost studies. Geoenergy Science and Engineering. 226:211706. 2023
- Evaluation of influential parameters for supersonic dehydration of natural gas: Machine learning approach. Petroleum Research. 7:372-383. 2022
- Modelling oil and gas flow rate through chokes: A critical review of extant models. Journal of Petroleum Science and Engineering. 208:109775. 2022
- Ultrasonic thermophysical enhancement of a novel nanodoped vapour compression fluid for high cooling efficiency. Thermal Science and Engineering Progress. 36:101485. 2022
- A comprehensive review of laboratory, field and modelling studies on drilling mud rheology in high temperature high pressure (HTHP) conditions. Journal of Natural Gas Science and Engineering. 94:104046. 2021
- A critical review of drilling mud rheological models. Journal of Petroleum Science and Engineering. 203:108659. 2021
- Modeling the downhole density of drilling muds using multigene genetic programming. Upstream Oil and Gas Technology. 6:100030. 2021
- Artificial neural network model for predicting drill cuttings settling velocity. Petroleum. 6:340-352. 2020
- Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells. Journal of Petroleum Exploration and Production Technology. 10:1081-1095. 2020
- Potentials of waste seashells as additives in drilling muds and in oil well cements. Cleaner Engineering and Technology. 1:100008. 2020
- Experimental Analysis of <i>Persea americana</i> as Filtration Loss Control Additive for Non-Aqueous Drilling Fluid. International Journal of Engineering Research in Africa. 44:8-21. 2019
- Rice husk and saw dust as filter loss control agents for water-based muds. Heliyon. 5:e02059. 2019
- Artificial intelligence techniques and their applications in drilling fluid engineering: A review. Journal of Petroleum Science and Engineering. 167:300-315. 2018
- Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies. Powder Technology. 339:728-746. 2018
- Using agro-waste materials as possible filter loss control agents in drilling muds: A review. Journal of Petroleum Science and Engineering. 163:185-198. 2018
conference paper

education and training

Ph.D. in Petroleum Engineering, University of Uyo 2016 - 2019
M.Sc. in Petroleum Engineering, Norwegian University of Science and Technology 2010 - 2012
B.E. in Petroleum Engineering, University of Uyo 2003 - 2008

full name

Okorie Ekwe Agwu

primary email

okorie.agwu@utp.edu.my

webpage

Publications in VIVO

Co-author Network

Contact Info

okorie.agwu@utp.edu.my

Websites