Christopher Mkono | Artificial Intelligence in Petroleum Engineering | Best Researcher Award

Published on by Petroleum Engineering

Mr. Christopher Mkono | Artificial Intelligence in Petroleum Engineering | Best Researcher Award

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.

Taiba Kouser | Petroleum Engineering | Best Researcher Award

Published on by Petroleum Engineering

Dr. Taiba Kouser | Petroleum Engineering | Best Researcher Award

Postdoctoral Fellow at King Fahd University of Petroleum and Minerals Dhahran, Saudi Arabia

Dr. Taiba Kouser is a distinguished postdoctoral researcher at the Advanced Research Center for Modeling and Simulation Technologies (ARCMST), King Fahd University of Petroleum and Minerals (KFUPM), where she has been contributing significantly to the advancement of computational fluid dynamics (CFD). Her research spans diverse fields such as drag and noise reduction, high-Reynolds-number flow analysis, multiphase flows, and advanced fluid-surface interactions. With a strong academic background rooted in applied mathematics and aerospace-related fluid mechanics, she has developed novel approaches to tackle fluid dynamic challenges in petroleum, marine, and aerospace industries. Dr. Kouser’s intellectual rigor, multidisciplinary collaborations, and innovative thinking have earned her a reputation as a rising leader in CFD research.

Profile

Scopus

Education

Dr. Kouser earned her Ph.D. from Huazhong University of Science and Technology (HUST), Wuhan, China, where she specialized in low Reynolds number flow behaviors and noise suppression mechanisms via superhydrophobic surfaces. Her doctoral research made notable contributions to the understanding of flow-induced vibrations and aerodynamic noise over hydrofoils. Prior to her Ph.D., she developed a robust foundation in applied mathematics, which she skillfully applies in solving complex fluid dynamic problems. Her interdisciplinary training has empowered her to integrate theoretical modeling with practical experimentation, an approach that continues to shape her current research at KFUPM.

Experience

Over the years, Dr. Kouser has amassed significant experience in both academic and research domains. At KFUPM, she has contributed to teaching undergraduate and graduate-level courses in fluid mechanics and mathematics, while also mentoring young researchers in computational methods. Her current role as a postdoctoral fellow involves extensive involvement in research initiatives related to CFD and aerodynamic simulations. Dr. Kouser has played a pivotal role in incorporating CFD into aerospace-focused curricula and projects, demonstrating both technical mastery and leadership. Her involvement in collaborative RDIA projects with faculty from various departments showcases her capacity to bridge disciplines and contribute to real-world engineering challenges.

Research Interest

Dr. Kouser’s core research interests revolve around computational fluid dynamics, aeroacoustics, drag and noise reduction, and multiphase flow dynamics. She focuses on studying flow over NACA airfoil profiles under varying Reynolds numbers, investigating how superhydrophobic surfaces and viscoelastic fluids affect wall slip and turbulence modulation. Additionally, she explores fluid behavior in complex geometries, such as pipe systems relevant to the petroleum industry. Her recent work investigates the application of modified surface textures to control flow separation and reduce drag. Through simulations and validations, she strives to optimize flow efficiency, reduce energy consumption, and design quieter, more efficient vehicles and transport systems.

Awards

Dr. Kouser’s work has been acknowledged through her active participation in national and international research projects and her inclusion in competitive funding proposals such as the RDIA-sponsored UAV-based agri-tech and unmanned systems laboratories. Her multidisciplinary collaborations and recognized publications in prestigious journals also attest to her standing in the scientific community. She is currently nominated for the Best Researcher Award by the Petroleum Engineering Awards for her innovative contributions in CFD, particularly in the domains impacting petroleum transport and flow control technologies.

Publications

Dr. Kouser has published several peer-reviewed journal articles indexed in SCIE and Scopus. Her recent publications include:

(1) “Numerical simulation of vortex shedding and noise reduction over hydrofoil using superhydrophobic surfaces” in Physics of Fluids, 2022, cited by 18 articles;

(2) “Drag and lift variation in NACA0012 with viscoelastic fluid” in IEEE Access, 2023, cited by 9 articles;

(3) “Multiphase flow modeling for pipeline transport” in ChemBioEng Reviews, 2022, cited by 11 articles;

(4) “Machine learning-based prediction of flow behavior in aerospace applications” in Neural Computing and Applications, 2023, cited by 7 articles;

(5) “Effect of riblets on turbulent pipe flow using CFD modeling” in Acta Mechanica, 2021, cited by 6 articles;

(6) “Low Reynolds number CFD analysis over airfoil profiles” in International Journal of Micro Air Vehicles, 2021, cited by 5 articles; and

(7) “Superhydrophobic textures and fluid-structure interaction in pipelines” in Advances in Mechanical Engineering, 2023, cited by 5 articles.

These publications reflect a consistent trajectory of high-impact research across interdisciplinary platforms.

Conclusion

Dr. Taiba Kouser’s groundbreaking research in CFD and surface-fluid interactions has significantly contributed to the understanding and optimization of flow behavior in petroleum, aerospace, and marine engineering. Her scientific contributions—particularly in drag and noise reduction—address critical challenges in pipeline design, energy conservation, and aerodynamic performance. Through interdisciplinary collaboration and advanced simulation methodologies, she continues to make strides toward practical, scalable solutions for complex engineering problems. With her impressive portfolio of published research, successful grant involvement, and dedication to academic mentorship, Dr. Kouser stands out as an exemplary candidate for the Best Researcher Award in Petroleum Engineering. Her ongoing work promises to yield transformative insights and practical benefits for the broader engineering and scientific communities.