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Personal profile

Academic Biography

Johnson Ugwu, PhD, MBA, CEng, MEI holds a PhD in Petroleum Reservoir Engineering from Robert Gordon University, following research into development of own approach to modelling and simulation of Gas-Condensate reservoirs, an MSc and MBA degrees from Ahmadu Bello University, and BEng in Chemical Engineering from Anambra State University of Technology.

He is a training beneficiary of Petroleum Technology Development Fund (PTDF) and World Intellectual Property Organisation (WIPO).

He is a Chartered Engineer (CEng), Chartered Petroleum Engineer, Member Energy Institute (MEI), and Member of Society of Petroleum Engineers (SPE).

Dr Ugwu worked as a Research Officer and rose to Assistant Chief Research Officer, a position he held for over 10 years. He also served as a Visiting Lecturer to Aberdeen College and as an Oil and Gas Engineering Consultant in Aberdeen before joining Teesside University as a Senior Lecturer in Petroleum & Technology Engineering.

His domestic UK and international professional work experience spans Oil and Gas Upstream Industry Consultancy, Research and Development. He has held Reservoir and Process Engineering positions.

Summary of Research Interests

His main and specialist research and teaching areas include:

Oil and Gas field development planning, Carbon Capture and Storage (CCS), Production optimisation, Bench marking petroleum fluid property models, Multiphase flow in porous and permeable media, Classical and Numerical Reservoir modelling and Simulation, IOR/ EOR,Wells and production Engineering, hydraulic fracturing, heavy Oil production, and Facilities Engineering.

Dr Ugwu is a professional trainer in Oil and Gas industry with domestic and international work experience in reservoir engineering and management, well design, petroleum production and process engineering. He welcomes research collaboration and PhD research supervision.

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

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Research Output 1999 2019

Application of machine learning models in predicting initial gas production rate from tight gas reservoirs

Amaechi, U. C., Ikpeka, P. M., Ugwu, J. & Xianlin, M., 18 Mar 2019, (Accepted/In press) In : The Mining Geology Petroleum Engineering Bulletin.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Learning systems
Gases
Mean square error
Fluids
Tight gas

Development of Inflow Performance Relationship Model in High Temperature Gas-Condensate Reservoirs.

Faraji, F., Ugwu, J., Nabhani, F. & Chong, P. L., 8 Apr 2019.

Research output: Contribution to conferenceAbstractResearchpeer-review

Gas condensates
Viscosity
Temperature
Compressibility
Flow of fluids

Phase Change Tracking Approach to Predict Timing of Condensate Formation and its Distance from the Wellbore in Gas Condensate Reservoirs

Bilotu Onoabhagbe, B., Rezaei Gomari, S., Russell, P., Ugwu, J. & Ubogu, B. T., 12 Apr 2019, In : Fluids. 71, 4, 14 p., Fluids 2019,4,71.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
Gas condensates
Recycling
Recovery
Water injection
Gases
Open Access
Gas condensates
Horizontal wells
Recovery
Fluids
Multiphase flow

Modified Gas Condensate Down-hole PVT

Ugwu, J., Mason, E. & Gobina, E., 2011.

Research output: Contribution to conferencePaperResearchpeer-review

Open Access
Gas condensates
Compressibility
Density (specific gravity)
Viscosity
Phase behavior