Assistant Prof., University of Qom, Iran
Dr. Abbas Hashemizadeh is a distinguished petroleum engineer with robust academic, industrial, and research experience in enhanced oil recovery (EOR), drilling engineering, and corrosion mitigation. He is currently serving as an Assistant Professor at Hakim Sabzevari University and a Visiting Professor at Amirkabir University of Technology, Tehran, Iran. With a Ph.D. in Petroleum Engineering and a GPA of 19.04/20, Dr. Hashemizadeh’s academic journey reflects excellence. His work explores innovative applications of machine learning in reservoir modeling, drilling optimization, and smart fluids. He has authored numerous peer-reviewed publications, contributing significantly to petroleum science. With his experience as a Company Man Engineer at the Iranian Central Oil Fields Company, Dr. Hashemizadeh bridges academic theory with field practicality. His research has been cited globally and is known for its interdisciplinary approach, incorporating chemistry, data science, and reservoir simulation. He is a potential candidate for recognition through the Enhanced Oil Recovery Award.
Professional Profile
🎓 Education
Dr. Abbas Hashemizadeh holds a Ph.D. in Petroleum Engineering from Amirkabir University of Technology, Tehran (2018), where he graduated with an exceptional GPA of 19.04/20. His academic excellence is complemented by international exposure through the prestigious Excellence Research Programme at the University of Santiago de Compostela, Spain, in 2016. Prior to his doctoral studies, he earned an M.Sc. in Petroleum Engineering from Sahand University of Technology, Tabriz (2008), with a GPA of 17.26/20. He began his academic journey with a B.Sc. in Petroleum Engineering from the Petroleum University of Technology, Ahwaz (2006), achieving a GPA of 16.21/20. Throughout his educational path, Dr. Hashemizadeh demonstrated consistent scholarly dedication and a strong foundation in petroleum sciences, fluid mechanics, and applied geomechanics, laying the groundwork for his innovative contributions to drilling, enhanced oil recovery, and corrosion science.
💼 Experience
Dr. Hashemizadeh has over 15 years of integrated academic and industrial experience. He is currently an Assistant Professor at the Petroleum & Petrochemical Engineering School, Hakim Sabzevari University (since 2010), where he has also served as Head of the Petroleum Engineering Department and Vicar of the Faculty. Since 2012, he has held the title of Visiting Professor at Amirkabir University of Technology, mentoring graduate students and conducting interdisciplinary research. Parallel to his academic work, he worked as a Company Man Engineer at the Iranian Central Oil Fields Company (ICOFC) from 2014 to 2019, handling field-level drilling operations and production monitoring. Earlier in his career, he trained as a Driller Trainee at Oriental Oil Kish Company. This rare blend of academic and hands-on industry experience enriches his teaching and enhances the real-world relevance of his research in enhanced oil recovery and drilling optimization.
🔬 Research Focus
Dr. Hashemizadeh’s research lies at the intersection of Enhanced Oil Recovery (EOR), drilling engineering, corrosion science, and machine learning applications in petroleum systems. His studies on magnetic water flooding, polymer injection, and acid gas EOR contribute to more sustainable and efficient oil extraction. He has explored how magnetic fields can enhance HCl behavior in acidizing and reduce casing corrosion. He also integrates machine learning—using support vector machines, KNN, and ensemble models—to predict parameters like mud density and rate of penetration. This hybrid of field experimentation and computational intelligence enables smarter, cost-effective decisions in wellbore and reservoir management. His recent focus on microbial degradation and polymeric nanoparticles for drilling fluids expands the EOR toolkit. He continues to explore novel simulation methods for fractured reservoirs and their geomechanical implications. His work positions him at the forefront of intelligent oilfield technologies.
📚 Publication Top Notes
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Experimental measurement and modeling of water-based drilling mud density using adaptive boosting decision tree, support vector machine, and K-nearest neighbors
📙 Journal of Petroleum Science and Engineering, Vol. 207, 109132 (2021)
📈 Citations: 47
➤ This paper presents a hybrid machine learning approach to predict mud density in drilling operations. Adaptive Boosting, SVM, and KNN were used on real-time field data, enhancing predictive accuracy and aiding safer well planning.
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The possibility of enhanced oil recovery by using magnetic water flooding
📙 Petroleum Science and Technology, 32(9), 1038–1042 (2014)
📈 Citations: 19
➤ Investigates how applying magnetic fields to injection water can improve oil recovery rates. The study revealed increased wettability and better sweep efficiency, showcasing a non-chemical method to improve recovery.
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Prediction of elastic parameters in gas reservoirs using ensemble approach
📙 Environmental Earth Sciences, 82(11), 269 (2023)
📈 Citations: 12
➤ Uses ensemble learning for predicting Young’s modulus and shear modulus. It aids reservoir engineers in planning stimulation treatments and reducing uncertainty in rock mechanics analysis.
Conclusion
Dr. Abbas Hashemizadeh is a highly suitable candidate for the Best Researcher Award. His rich combination of practical and theoretical contributions, focus on enhancing petroleum engineering through innovation and machine learning, and consistent academic productivity make him stand out. With a stronger emphasis on international collaborations, citations impact, and global dissemination, he has the potential to rise further as a leading researcher in the petroleum and energy sector.