Student at China university of Geosciences (Wuhan), China
Christopher Nyangi Mkono is a PhD candidate in Oil and Natural Gas Engineering at the China University of Geosciences, with a specialized focus on machine learning applications in source rocks potentiality, reservoir characterization, and hydrocarbon resource evaluation. He is deeply involved in the integration of artificial intelligence and machine learning models to enhance the understanding and management of subsurface resources. With a solid academic foundation, Mkono has contributed significantly to the fields of geosciences and petroleum engineering, blending his expertise in programming and numerical modeling with an understanding of geotechnical systems. His work has spanned multiple global platforms, presenting at key international conferences and contributing to cutting-edge research in the energy sector.
Profile
Scopus
Education
Mkono’s educational journey is marked by a commitment to advancing his knowledge in oil and gas engineering. He is currently pursuing a PhD at the China University of Geosciences, where he began his master’s program in 2019. Before that, he completed his Bachelor of Science in Applied Geology from the University of Dodoma, Tanzania, in 2016. This academic trajectory highlights a strong foundation in geosciences, complemented by advanced studies in petroleum engineering. His research combines theoretical and practical applications, particularly in the development of innovative computational models and machine learning techniques for resource estimation.
Experience
Christopher Mkono has gained significant experience in the fields of geosciences and petroleum engineering, focusing on innovative approaches to reservoir characterization and hydrocarbon potential analysis. His work involves the application of neural network algorithms, machine learning techniques, and artificial intelligence to improve the accuracy and efficiency of geophysical and geochemical analyses. Additionally, he is proficient in various programming languages, including MATLAB and Python, and has worked extensively with scientific software and numerical modeling tools such as Origin and Eclipse. This expertise enables him to manage databases and develop models that support the energy industry’s evolving needs.
Research Interests
Mkono’s research interests lie primarily in the intersection of machine learning and geosciences, with a particular focus on the application of these technologies in source rock evaluation and hydrocarbon resource prediction. His work aims to improve the understanding of subsurface geology by integrating advanced artificial intelligence techniques with traditional geological modeling. Mkono is particularly interested in improving the estimation of reservoir properties such as porosity and permeability, utilizing models that incorporate explainable artificial intelligence for greater transparency and interpretability in results. His research also extends to reservoir thermal maturity estimation and the application of hybrid machine learning approaches in basin modeling.
Awards
Throughout his academic career, Christopher Mkono has demonstrated exceptional academic and research potential. While still early in his career, his contributions to geosciences and petroleum engineering have been recognized at several levels, particularly his work in integrating AI into traditional geological processes. His innovative contributions have earned him opportunities to present at prominent international conferences and competitions, such as the China Petroleum Engineering Design Competition International Circuit. His ongoing contributions to his field position him as a promising researcher whose work is poised for significant impact in both academic and industrial contexts.
Publications
Christopher Mkono has authored several notable publications in high-impact journals, focusing on the application of machine learning and artificial intelligence in geosciences. His research has been well received by the academic community, with articles published in journals such as SPE Journal and Engineering Applications of Artificial Intelligence. A few of his key publications include:
Mkono, C. N., Chuanbo, S., Mulashani, A. K., Abelly, E. N., Kasala, E. E., Shanghvi, E. R., Emmanuely, B. L., & Mokobodi, T. (2025). “Improved Reservoir Porosity Estimation Using an Enhanced Group Method of Data Handling with Differential Evolution Model and Explainable Artificial Intelligence.” SPE Journal, 1-19.
Mkono, C. N., Shen, C., Mulashani, A. K., Carranza, E. J. M., Kalibwami, D. C., & Nyangi, M. J. (2025). “A Novel Hybrid Group Method of Data Handling and Levenberg Marquardt Model for Estimating Total Organic Carbon in Source Rocks with Explainable Artificial Intelligence.” Engineering Applications of Artificial Intelligence, 144, 110137.
Mkono, C. N., Shen, C., Mulashani, A. K., Mwakipunda, G. C., Nyakilla, E. E., Kasala, E. E., & Mwizarubi, F. (2025). “A Novel Hybrid Machine Learning and Explainable Artificial Intelligence Approaches for Improved Source Rock Prediction and Hydrocarbon Potential in the Mandawa Basin, SE Tanzania.” International Journal of Coal Geology, 104699.
Mkono, C. N., Shen, C., Mulashani, A. K., Ngata, M. R., & Hussain, W. (2024). “A Novel Hybrid Machine Learning Approach and Basin Modeling for Thermal Maturity Estimation of Source Rocks in Mandawa Basin, East Africa.” Natural Resources Research, 33(5), 2089-2112.
Mkono, C. N., Shen, C., Mulashani, A. K., & Nyangi, P. (2024). “An Improved Permeability Estimation Model Using Integrated Approach of Hybrid.”
These works reflect his expertise in enhancing the accuracy of geological assessments using artificial intelligence, with many of his papers garnering significant citations from both academic and industry professionals.
Conclusion
Christopher Mkono is an emerging scholar in the field of petroleum engineering, with a solid background in geosciences and a passion for integrating machine learning and artificial intelligence into his research. His work is positioned to make significant contributions to the fields of source rock analysis, reservoir characterization, and hydrocarbon resource evaluation. Through his publications, presentations, and participation in international conferences, Mkono is building a reputation as a forward-thinking researcher whose work will help shape the future of geosciences and petroleum engineering. His ongoing efforts in advancing AI applications in geosciences reflect both his academic potential and his commitment to addressing the challenges of energy resource management.