Explore Richards Okiemute's peer-reviewed publications on Geo-energy (CCUS and Hydrogen storage), sustainable energy systems, and applied computational methods.

Current research

NuclearPolicyAfrica Energy

African Nuclear Standards and Technology Development Initiative

Richards Okiemute, Research Fellow

ASME Engineering for Change (E4C) Fellowship, in partnership with the American Society of Mechanical Engineers (ASME). Ongoing.

A structured assessment of the nuclear energy landscape across African nations: mapping national energy plans and key stakeholders, documenting knowledge gaps in nuclear technology, and identifying feasible routes for standards and technology transfer across the continent.

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2026

CCUSHydrogen StorageGeothermalAI/ML

Artificial Intelligence in Subsurface Energy Systems: A Comprehensive Review of CO₂ Storage, Hydrogen Storage, and Geothermal Energy

Okiemute, R. O.*, Oyetunji, O. S., Yakubu, K. S., Ibrahim, U. F., Olude, D. A., & Abubakar, A.

Science World Journal, 21(3), 928–936. Published 29 September 2026.

*Corresponding author

Abstract
The global shift toward clean energy is turning deep underground rock layers into a vital infrastructure for storing energy and cutting carbon emissions. This paper examines the role of artificial intelligence in subsurface energy systems, focusing on CO₂ storage, hydrogen storage, and geothermal energy, while evaluating data-driven methods for site characterization, performance prediction, and risk assessment.
View article PDF
Cite 
@article{okiemute2026subsurface,
  author  = {Okiemute, Richards Obada and Oyetunji, Oluwatimilehin Samuel and Yakubu, Kabiru Sani and Ibrahim, Umar Faruk and Olude, Dolapo Abraham and Abubakar, Abdulbasid},
  title   = {Artificial Intelligence in Subsurface Energy Systems: A Comprehensive Review of {CO$_2$} Storage, Hydrogen Storage, and Geothermal Energy},
  journal = {Science World Journal},
  volume  = {21},
  number  = {3},
  pages   = {928--936},
  year    = {2026},
  url     = {https://scienceworldjournal.org/article/view/24856}
}

2025

Nuclear FusionAI/MLSustainable Energy

Application of Artificial Intelligence in Shaping the Future of Sustainable Nuclear Fusion Energy

Emmanuel, E. T., Thanni, A. A., Jimson, I. O., Okiemute, R. O., Muritala, I. O., & Igbokwe, O. F.

World Journal of Advances in Engineering and Technology Studies (WJAETS), 17(01), 041–060. 2025.

Abstract
This review examines the expanding contribution of artificial intelligence to fusion science, focusing on machine learning and deep learning for modelling, control and optimization in future fusion systems.
View article DOI 10.30574/wjaets.2025.17.1.1378
Cite 
@article{emmanuel2025fusion,
  author  = {Emmanuel, E. T. and Thanni, A. A. and Jimson, I. O. and Okiemute, R. O. and others},
  title   = {Application of Artificial Intelligence in Shaping the Future of Sustainable Nuclear Fusion Energy},
  journal = {World Journal of Advances in Engineering and Technology Studies},
  volume  = {17},
  number  = {1},
  pages   = {041--060},
  year    = {2025},
  doi     = {10.30574/wjaets.2025.17.1.1378}
}
AI/MLOil & GasPredictive Maintenance

Leveraging Machine Learning and Data Analytics for Equipment Reliability in Oil and Gas Using Predictive Maintenance

Ekunke, O. V., Okiemute, R. O., Oke, B. O., Jimson, I. O., Iwalokun, A. O., Akinbolusere, M. O., & Pius, A. B.

World Journal of Advanced Research and Reviews, 25(01), 2212–2218. January 2025.

Abstract
The oil and gas industry operates under extreme conditions that make equipment reliability a persistent challenge. Predictive maintenance, now possible through machine learning and data analytics, can reduce downtime and improve operational efficiency.
View article DOI 10.30574/wjarr.2025.25.1.0295
Cite 
@article{ekunke2025predictive,
  author  = {Ekunke, Onyeka Virginia and Okiemute, Richards Obada and Oke, Bioluwatife Oluwaferanmi and Jimson, Israel Oluwaseun and Iwalokun, Austine Oluwole and Akinbolusere, Michael Oluwatosin and Pius, Adebayo Benjamin},
  title   = {Leveraging Machine Learning and Data Analytics for Equipment Reliability in Oil and Gas Using Predictive Maintenance},
  journal = {World Journal of Advanced Research and Reviews},
  volume  = {25},
  number  = {1},
  pages   = {2212--2218},
  month   = jan,
  year    = {2025},
  doi     = {10.30574/wjarr.2025.25.1.0295}
}

2024

CCUSDownstreamEmissions

Investigation into the Feasibility and Efficiency of Implementing Carbon Capture and Storage Technologies in Downstream Facilities: To Mitigate Greenhouse Gas Emissions

Godwin, E. O., Odey, P. O., Okiemute, R. O., Owunna, I. B., Asere, J. B., & Enem, C. E.

World Journal of Advanced Research and Reviews, 24(02), 1004–1016. November 2024.

Abstract
This research investigates the feasibility and efficiency of carbon capture and storage (CCS) technologies in downstream oil and gas facilities, such as refining, distribution, and processing operations, to mitigate greenhouse gas emissions.
View article DOI 10.30574/wjarr.2024.24.2.3438
Cite 
@article{godwin2024ccs,
  author  = {Godwin, Ekunke Odor and Odey, Peter Odey and Okiemute, Richards Obada and Owunna, Ikechukwu Bismarck and Asere, Joshua Babatunde and Enem, Chukwudike Eric},
  title   = {Investigation into the Feasibility and Efficiency of Implementing Carbon Capture and Storage Technologies in Downstream Facilities: To Mitigate Greenhouse Gas Emissions},
  journal = {World Journal of Advanced Research and Reviews},
  volume  = {24},
  number  = {2},
  pages   = {1004--1016},
  month   = nov,
  year    = {2024},
  doi     = {10.30574/wjarr.2024.24.2.3438}
}
EORReservoir Eng.Biopolymers

Enhanced Oil Recovery Methods Using Biodegradable Materials in Different Reservoirs

Ekunke, O. V., Nzereogu, S. K., Uyanah, J. J., Okiemute, R. O., Ikechukwu, S. V., Ifechukwu, E. J., Collins, I., Chadi, P., Ibe, M. E., & Dauda, Y. P.

Global Scientific Journal (GSJ), 12(9), 647–664. September 2024.

Abstract
This review examines the use of biodegradable materials in enhanced oil recovery (EOR), highlighting their environmental benefits over traditional methods and exploring how they improve recovery performance across different reservoir settings.
Read PDF
Cite 
@article{ekunke2024eor,
  author  = {Ekunke, Onyeka Virginia and Nzereogu, Stella Kosi and Uyanah, Joseph Junior and Okiemute, Richards Obada and Ikechukwu, Stephen Victor and Ifechukwu, Enem Jeffery and Collins, Ikiebe and Chadi, Peter and Ibe, Mary Ezinne and Dauda, Yakubu Peter},
  title   = {Enhanced Oil Recovery Methods Using Biodegradable Materials in Different Reservoirs},
  journal = {Global Scientific Journal},
  volume  = {12},
  number  = {9},
  pages   = {647--664},
  month   = sep,
  year    = {2024}
}
DrillingSustainabilityOil & Gas

Advancements in Eco-friendly Drilling Fluids: A Review of Recent Innovations and Their Environmental Impacts

Ekunke, O. V., Nzereogu, S. K., Oluyimika, J. O., Okiemute, R. O., Agbonze, N. G., Ugbine, O. F., & Bello, A. R.

International Journal of Advances in Engineering and Management (IJAEM), 6(9), 179–187. September 2024.

Abstract
With growing environmental concerns, the oil and gas industry is increasingly turning to sustainable alternatives to traditional drilling fluids, which have been linked to significant ecological impacts.
Read PDF
Cite 
@article{ekunke2024drilling,
  author  = {Ekunke, Onyeka Virginia and Nzereogu, Stella Kosi and Oluyimika, Jaiyeola Omowoleola and Okiemute, Richards Obada and Agbonze, Nosa Godwin and Ugbine, Oghenefegor Favour and Bello, Afolabi Rasheed},
  title   = {Advancements in Eco-friendly Drilling Fluids: A Review of Recent Innovations and Their Environmental Impacts},
  journal = {International Journal of Advances in Engineering and Management},
  volume  = {6},
  number  = {9},
  pages   = {179--187},
  month   = sep,
  year    = {2024}
}
IoTAI/MLCybersecurity

Identifying Internet of Things Devices through Unique Digital Signatures and Advanced Machine Learning Techniques

Akintayo, T. A., Okiemute, R. O., Owoeye, M. C., Balogun, O. S., Paul, C., Queenet, M. M. C., Okereke, R. O., Moluno, R. C., Adediran, A. S., Nzeanorue, C. A., & Akinlabi, D. F.

Path of Science, 10(7), 1001–1007. July 2024.

Abstract
The rapid growth of the Internet of Things (IoT) has led to a surge in connected devices across sectors, making reliable device recognition increasingly important. This study explores unique digital signatures and advanced machine learning methods to identify IoT devices with improved accuracy.
View article
Cite 
@article{akintayo2024iot,
  author  = {Akintayo, Taiwo Abdulahi and Okiemute, Richards Obada and Owoeye, Moyosore Celestina and Balogun, Oluwaseyi Sulaimon and Paul, Chadi and Queenet, Madumere Chiamaka and Okereke, R. O. and Moluno, Richard C. and Adediran,  and Nzeanorue, Charles and Akinlabi, David},
  title   = {Identifying Internet of Things Devices through Unique Digital Signatures and Advanced Machine Learning Techniques},
  journal = {Path of Science},
  volume  = {10},
  number  = {7},
  pages   = {1001--1007},
  month   = jul,
  year    = {2024}
}
Research and Fellowship Experience (Click to expand / collapse)

Research Fellow | ASME Engineering for Change (E4C) Fellowship

May 2026 – Nov 2026

  • Conducting desktop research on Africa Nuclear Energy and Technology Development Initiatives across South Africa, Egypt, Nigeria, Ghana, and Kenya.
  • Leading research on South Africa’s energy landscape, energy mix including Green Hydrogen, biomass, hydropower, solar energy, CSP, wind power, and emerging energy technologies (P2X, SMR, and others).
  • Developing Stakeholders Mapping database and evaluating local/international supply chains in South Africa's energy ecosystem.

Graduate Research Assistant | University of Benin, Nigeria

Jan 2023 – Nov 2025

  • Developed CO₂StoreCap, an assistant for estimating storage capacity, assessing trapping and leakage risks, and optimizing injection and monitoring strategies.
  • Designed a K-Means clustering model for lithology classification in sandstone–shale intercalations, enhancing understanding of subsurface heterogeneity.

Graduate Research Engineer (Computational Modeling & Systems Optimization) | Welltec Oilfield Services

Jan 2020 – May 2022

  • Conducted data collection, cleaning, and transformation for model training and validation.
  • Applied Random Forest, Linear Regression, and XGBoost to automate system performance prediction, achieving an R² score of 97.43% and reducing operational downtime by 15%.

Mathematics Teacher | Government Girls Secondary School (UBE), Rivers State, Nigeria (NYSC)

Feb 2019 – Oct 2020

  • Taught mathematics to Junior Secondary School classes (age group 8–13 years) for two terms.
  • Improved student passing score by 83% from 45%.
  • Graded over 200 examinations and test scripts.

Undergraduate Researcher | Petroleum Training Institute (PTI), Delta State, Nigeria

Jul 2018 – Sept 2018

  • Thesis: Analysis of Water Coning in Vertical Oil Wells in Niger Delta.
  • Performed parametric and sensitivity analysis using four models (Meyer and Garder, Schols, Chaperon, Hoyland et al.) to determine water breakthrough time and critical production rate.
  • Identified Hoyland et al. for optimal production rates and Meyer and Garder for breakthrough time prediction.

Undergraduate Researcher (National Diploma) | Petroleum Training Institute (PTI), Delta State, Nigeria

May 2014 – Oct 2014

  • Thesis: Reservoir Pressure Maintenance to Increase Oil Production by Water Injection Method (Obagi Oil Field, OML 58).
  • Conducted case study analyzing historical production and pressure trend data (1964–1996) to evaluate water injection as a secondary recovery method.
  • Performed correlation analysis showing increased injection volumes drove production recovery to 62,625 BOPD.

Gas Development & Energy Analyst Intern | Nigeria National Petroleum Corporation (NNPC-NAPIMS)

Apr 2017 – Sept 2017

  • Reviewed First E&P's OML 83 & 85 gas development plan and strategy, evaluating phased processing build-out (300 MMscfd initial, scaling to 900 MMscfd) against a joint product range and infrastructure plan.
  • Contributed to review of Assa North Field Development Plan (FDP) and supported gas division's gas flare-out programme compliance tracking.
Education (Click to expand / collapse)

BSc. Petroleum Engineering | University of Benin, Nigeria

Feb 2015 – Sept 2018

  • Dissertation: Analysis of Water Coning in Vertical Oil Wells in Niger Delta, Nigeria.
  • Advisor: Professor Ohenhen Ikponmwosa.

Diploma in Petroleum Engineering Technology | Petroleum Training Institute, Nigeria

Nov 2011 – Oct 2014

  • Dissertation: Reservoir Pressure Maintenance to Improve Oil Production from Depleted Reservoirs.
  • Advisor: Engr. Frank Egede.

Overall CGPA (WES Evaluated): 3.39/4.0

Standardized Test Scores

  • Graduate Record Examination (GRE): 333/340 (Verbal: 164, Quantitative: 169, AW: 3.5)