S.S Mortazavi | Artificial Intelligence in Petroleum Engineering | Best Academic Researcher Award

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Prof. Dr. S.S Mortazavi | Artificial Intelligence in Petroleum Engineering | Best Academic Researcher Award

Professor at Shahid Chamran University of Ahvaz, Iran

Dr. Seyed Saeidollah Mortazavi is a distinguished academic in the field of Electrical Engineering, currently serving as a faculty member at Shahid Chamran University of Ahvaz. With over two decades of teaching, research, and technical leadership, he has made substantial contributions to power systems, control engineering, and intelligent algorithms. His academic career is marked by a rigorous pursuit of innovation in adaptive control, power quality, and fuzzy systems, and he is well-regarded for both his applied and theoretical impact in the energy sector.

Profile

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Education

Dr. Mortazavi obtained his Ph.D. in Electrical Engineering from the prestigious Indian Institute of Technology (IIT), New Delhi in 1999. His doctoral thesis, titled “Fuzzy Logic Control System Design and Tuning”, established his foundation in intelligent systems. Prior to this, he completed both his Master’s and Bachelor’s degrees in Electrical Engineering from Ferdowsi University of Mashhad in 1994 and 1992, respectively, focusing on control systems and power system coordination. His academic path has consistently reflected a strong commitment to bridging complex theoretical concepts with practical applications in electrical systems.

Experience

Dr. Mortazavi began his academic tenure at Shahid Chamran University in 2000, where he initially held the position of Assistant Professor and was later promoted to Associate Professor in 2010. He has taught a wide array of courses, including Electric Circuits, Signals and Systems, Control Systems, SCADA, Neural Networks, and Soft Computing. He has also held several administrative roles, such as Director of the University Informatics Center and Secretary of the Informatics Council, and led major IT infrastructure initiatives. In addition, he has served as a technical reviewer for numerous scientific journals and conferences since 1999 and has supervised dozens of postgraduate theses.

Research Interests

Dr. Mortazavi’s primary research interests lie in the areas of intelligent control systems, power system stability, reactive power compensation, and power quality improvement. He has extensively explored the application of fuzzy logic, neural networks, and genetic algorithms in load balancing, harmonic estimation, fault detection, and optimization in power systems. His research contributions often emphasize practical implementation, such as in adaptive active power filters, FACTS device coordination, and SCADA systems, reflecting a deep commitment to enhancing reliability and efficiency in modern electrical grids.

Awards

Throughout his career, Dr. Mortazavi has been recognized for his excellence in research and teaching. He has been a recurring participant and contributor to high-profile national and international engineering conferences and has represented Shahid Chamran University in various technical forums. His involvement in the establishment of university-level informatics systems and his contributions to regional and national electricity grid optimization projects highlight his impact beyond academia.

Publications

Dr. Mortazavi has authored numerous research papers in reputable journals and conferences. Some notable publications include:

  1. “Fuzzy logic controlled adaptive active power filter for harmonics minimization and reactive power compensation under fast load variation”, WSEAS Transactions on Power Systems, 2008 (Cited by 80+ articles).

  2. “A novel method of coordinating PSSs and FACTS Devices in Power System Stability Enhancement”, WSEAS Transactions on Power Systems, 2009.

  3. “Harmonic estimation in a power system using a novel hybrid Least Squares-Adaline Algorithm”, Electric Power System Research, Elsevier, 2009.

  4. “Comparison of different control strategies for robust shunt active power filter”, Int. Rev. of Electrical Engineering, 2009.

  5. “Electric Power Quality Disturbance Assessment Using Wavelet Transform Analysis”, Int. Rev. of Electrical Engineering, 2010.

  6. “Power Quality Improvement Using a Fuzzy Logic Control of a Series Active Filter”, Journal of Theoretical and Applied Information Technology, 2010.

  7. “Fuzzy Logic Based Coordination of SVC and ULTC to Improve Power Quality”, WCSET 2010.
    These publications have received significant citations, especially those focusing on fuzzy control and power quality, reflecting the scholarly influence and applied relevance of his work.

Conclusion

Dr. Seyed Saeidollah Mortazavi exemplifies the values of academic excellence, research leadership, and public service. His multidisciplinary approach, bridging intelligent systems and electrical engineering, has not only enriched the academic community but also contributed tangibly to the optimization of energy systems in Iran and beyond. With a solid record of innovation, publication, and mentorship, Dr. Mortazavi is an outstanding candidate for any award recognizing excellence in engineering education and research.

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

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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.