Ms. Ebenezer Aniyom | Environmental Biotechnology | Best Researcher Award
Graduate Engineer at Hydroserve Oil Services, Nigeria
Engr. Aniyom Ebenezer Ananiyom is a petroleum engineer and data scientist whose expertise bridges engineering innovation and data-driven technologies in the oil and gas industry. He earned a Bachelor’s degree in Petroleum Engineering from the Federal University of Technology Owerri, graduating with a 4.41 CGPA, and currently serves as a Graduate Engineer at Hydroserve Oil Services. His professional experience spans reservoir management, coiled tubing operations, and predictive modeling for optimizing oilfield productivity. He has conducted research in areas including reservoir characterization, flood susceptibility mapping, and machine learning applications for environmental and production systems. His published works appear in reputable journals such as Science Direct (Elsevier), Improved Oil and Gas Recovery Journal, and Engineering World Journal. Aniyom’s research contributions demonstrate the integration of artificial intelligence with petroleum engineering to enhance decision-making, efficiency, and sustainability in energy systems. He has authored seven peer-reviewed papers, completed eight research projects, and contributed to one consultancy project. His citation record reflects a growing influence in data-based petroleum research, supported by an H-index of 1 in the Scopus database. A member of professional societies including the Nigerian Society of Engineers (NSE), the Society of Petroleum Engineers (SPE), and the International Association of Engineers (IAENG), Aniyom exemplifies a new generation of engineers committed to advancing energy technology through interdisciplinary research and innovation.
Featured Publications
Okoli, E. A., Josephine, K. M., Agoha, C. C., Ikoro, D. O., Oyinebielador, D. O., Aniyom, E. E. A., Oladipupo, J. T., & Emenyonu, U. D. (n.d.). Integrated flood susceptibility mapping using machine learning and geospatial techniques: A case study of Imo State, Southeastern Nigeria. Science Direct (Elsevier).
Chikwe, A. O., Aniyom, E. E., & Mbah, S. (n.d.). Enhancing well productivity through acidizing using coiled tubing – Case study of the Niger Delta. Improved Oil and Gas Recovery Journal, 9.
Anyadiegwu, C. I., Okalla, C. E., Kerunwakerunwa, A., Uzor, C. D., Uzohuzoh, J. C., Aniyom, E. E. A., & Dike, C. F. D. (n.d.). Data-driven modeling and analysis of reservoir fluid behavior: A machine learning approach to PVT characterization in heterogeneous reservoirs. Engineering World Journal, 7(11).
Aniyom, E. E., & Chikwe, A. O. (n.d.). Prediction of leak on gas pipeline using a hybrid machine learning model. Improved Oil and Gas Recovery Journal, 9.
Nmesoma, L. W., Aniyom, E. E. A., & Okere, N. (n.d.). Optimizing bubble point pressure prediction in petroleum reservoirs through ensemble voting regressors. Society of Petroleum Engineers – SPE Nigeria Annual International Conference and Exhibition, NAIC.
Chikwe, A. O., Aniyom, E. E. A., Nwanwe, O. I., & Odo, J. E. (n.d.). Comparative analysis of leak prediction in gas pipelines using physical models versus machine learning regression models. Journal of Petroleum and Mining Engineering, 0(0), 1–6.
Ihenetu, V. N., Aniyom, E. E., Jean Claude, W., Ewuzie, U., & Okoli, E. A. (n.d.). Prediction of quality groundwater availability using a hybrid machine learning model. Nigerian Association of Petroleum Explorationists Bulletin.
Aniyom, E. E., Chikwe, A. O., & Odo, J. E. (n.d.). Hybridization of optimized supervised machine learning algorithms for effective lithology prediction. Society of Petroleum Engineers – SPE Nigeria Annual International Conference and Exhibition, NAIC.