Faten A Tawfik | Petroleum Geology | Best Researcher Award

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Dr. Faten A Tawfik | Petroleum Geology | Best Researcher Award

PhD Researcher at Faculty of Science | Sohag University | Egypt

Faten Adel Tawfik Mohamed is an emerging scholar in petroleum geology, currently pursuing her PhD at Sohag University, Egypt. With a solid academic foundation and diverse industry experience, she combines academic rigor with practical insights. Faten has worked extensively with Abydos Mining Company, transitioning from marketing to accounting, which enriched her understanding of business-geological intersections. She has contributed to scientific development through workshops, conferences, and university leadership. Known for her dedication and technical acumen, she exemplifies excellence in geological research and petroleum systems. Faten is a proactive learner, passionate about energy resources, and aims to bridge academic research with real-world petroleum exploration challenges.

Education

Faten Adel Tawfik earned her Bachelor of Science in Geology in 2018 from Sohag University. She pursued a Pre-Master’s degree in Petroleum Geology in 2020, graduating with excellent standing. From 2020 to 2024, she completed her Master of Science in Petroleum Geology and is currently a doctoral student in the same field at Sohag University. Her educational trajectory is marked by excellence, including a standout graduation project on seismic acquisition and interpretation, achieving a 99% score. Faten’s academic path reflects a persistent dedication to advancing knowledge in petroleum geosciences through structured, hands-on, and theory-rich education.

Experience

Faten’s professional journey includes over five years at Abydos Mining Company, where she worked as an accountant from 2019 to 2024 and earlier as a Marketing Manager from 2018 to 2019. This dual experience in administration and strategy allowed her to understand the mining sector’s operational and economic dimensions. Faten’s interdisciplinary exposure bridges geosciences with corporate operations, fostering an integrated skillset valuable in resource evaluation. In addition to her employment, she actively participated in student-led academic and scientific organizations. Her involvement in SEG-Sohag and university committees strengthened her leadership, project management, and technical communication skills within geological contexts.

Research Interest

Faten’s research interests lie in petroleum geology with a focus on seismic interpretation, petrophysics, remote sensing applications, and pressure engineering in hydrocarbon exploration. She is particularly passionate about integrating geophysical methods with geological models to enhance reservoir characterization. Her academic training and hands-on workshop experiences have sharpened her abilities in seismic data processing, mud logging, and well log analysis. Faten aims to develop innovative techniques to interpret subsurface features, contributing to more accurate resource estimation. As a doctoral student, she is committed to advancing sustainable exploration methods and addressing geological challenges in petroleum systems using modern computational tools.

Awards

Faten has received multiple recognitions throughout her academic journey. She earned an “Excellent” distinction in both her graduation and pre-master’s projects, reflecting her academic excellence. As the founder and vice-president of the SEG-Sohag University student chapter, she led several initiatives that gained institutional acknowledgment. Her participation in annual Science Day conferences further highlights her active engagement in academic knowledge dissemination. Faten also completed prestigious courses in digital transformation and English proficiency, adding to her professional credentials. These awards and recognitions illustrate her continuous pursuit of excellence, leadership, and academic growth within petroleum geology and broader geoscience fields.

Publications Top Notes

  1. Tawfik, F. A. “Seismic Facies and Reservoir Characterization of the Sohag Basin.”
    Year: 2023
    Citation: 7

  2. Tawfik, F. A. “Integrated Petrophysical Analysis Using Tech-log for Hydrocarbon Assessment.”
    Year: 2022
    Citation: 5

  3. Tawfik, F. A. “Mud Logging Interpretation for Formation Pressure Estimation in Nubian Sandstone.”
    Year: 2022
    Citation: 4

  4. Tawfik, F. A. “Applications of Remote Sensing in Exploration Geology: A Case Study from Sohag.”
    Year: 2021
    Citation: 3

  5. Tawfik, F. A. “Well Logging Techniques for Reservoir Evaluation in Western Desert, Egypt.”
    Year: 2021
    Citation: 6

Conclusion

Faten Adel Tawfik stands as a promising geoscientist, blending academic excellence, professional experience, and a deep passion for petroleum geology. Her educational pursuits, research contributions, and leadership roles reflect a dedication to both scientific inquiry and practical application. With several publications in reputable journals, she contributes meaningfully to the field. Faten’s proactive engagement in workshops and technical training demonstrates her commitment to continuous learning. As a doctoral student, she is poised to become a key contributor to sustainable energy exploration. Faten’s profile embodies the ideal qualities of a scholar deserving of recognition and support for her future contributions.

Dandan Zhu | Intelligent Drilling and Completion | AI and Automation in Petroleum Award

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Assoc. Prof. Dr. Dandan Zhu | Intelligent Drilling and Completion | AI and Automation in Petroleum Award

Associate Professor at China University of Petroleum, Beijing, China

Dr. Dandan Zhu is an accomplished Associate Professor at the China University of Petroleum, Beijing, in the College of Artificial Intelligence. With a strong academic foundation and innovative research output, she has emerged as a prominent expert in the integration of artificial intelligence with petroleum engineering. Her pioneering work in intelligent drilling technologies and wellbore guidance systems has advanced subsurface automation and decision-making under uncertainty. Dr. Zhu has led numerous national projects and maintains deep collaborative ties with China’s top energy enterprises. Through publications, patents, and cross-disciplinary innovations, she has significantly contributed to the modernization of petroleum exploration and production.

Profile

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Education

Dr. Zhu holds a Ph.D. in Precision Engineering from the University of Tokyo, where she developed advanced modeling tools for mechanical systems. Prior to this, she earned a Master’s degree in Aircraft Design from Beihang University, building her expertise in computational mechanics and control. This unique academic trajectory provided her with a solid grounding in both high-precision design and intelligent control systems, which later became central to her work in petroleum AI. Her educational foundation is characterized by a rigorous blend of systems engineering, applied mathematics, and programming, equipping her to bridge the gap between AI theory and practical energy applications.

Experience

Since joining the China University of Petroleum in 2015, Dr. Zhu has led over 40 national and provincial-level research projects, many in collaboration with energy industry leaders such as CNPC, Sinopec, and CNOOC. She has executed 27 consultancy and industry-sponsored projects, developing intelligent geo-steering, simulation environments, and adaptive drilling systems. Her responsibilities extend beyond research to include mentoring graduate students and integrating AI pedagogy into petroleum engineering curricula. Her professional trajectory reflects a deep commitment to both academic excellence and industrial application. Her field-tested tools and platforms demonstrate practical outcomes in hydraulic fracturing, reservoir modeling, and real-time trajectory control.

Research Interest

Dr. Zhu’s research focuses on the intersection of artificial intelligence and petroleum engineering, specifically intelligent drilling systems, reinforcement learning for wellbore trajectory control, and real-time decision-making in geo-steering. She has built generative simulation environments that replicate subsurface dynamics for training AI agents. A key innovation includes a high-interaction learning framework that integrates offline modeling, real-time drilling guidance, and strategic post-drilling analysis. Her recent work emphasizes robustness and adaptability in drilling operations by accounting for geological uncertainty. Through deep reinforcement learning, data-driven optimization, and control theory, Dr. Zhu is contributing to the intelligent automation of energy exploration systems.

Award

Dr. Zhu’s impactful research has positioned her as a strong candidate for the Best Researcher Award in Petroleum Engineering. Her innovations have earned recognition across academia and industry. She has received multiple provincial and institutional grants to pursue advanced simulation and AI research. Her patented technologies in wellbore trajectory control and geo-steering optimization are used in field applications. Furthermore, her interdisciplinary efforts have been integral to national science and technology programs in China. These accolades, combined with her extensive collaboration with major energy corporations, underscore her standing as a leading researcher advancing AI integration in energy systems.

Publication

Dr. Zhu has published 39 peer-reviewed journal articles, contributing significantly to petroleum AI literature. Select publications include:

  1. A target-aware well path control method based on transfer reinforcement learning.

  2. Gait coordination feature modeling and multi-scale gait representation for gait recognition.

  3. PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning

  4. An intelligent drilling guide algorithm design framework based on high interactive learning mechanism

  5. End-to-end multiplayer violence detection based on deep 3D CNN

  6. Investigation on automatic recognition of stratigraphic lithology based on well logging data using ensemble learning algorithm

  7. A reinforcement learning based 3d guided drilling method: Beyond ground control

Her work has received over 68 citations since 2020 and continues to shape the AI-petroleum research landscape.

Conclusion

Dr. Dandan Zhu exemplifies the integration of technical innovation and practical relevance in petroleum engineering. Through her work in artificial intelligence, simulation, and intelligent control, she is reshaping the way subsurface operations are conceptualized and executed. Her contributions extend beyond academic theory into field-proven tools and methods widely adopted by industry leaders. By mentoring future engineers, publishing transformative research, and collaborating across sectors, she has established herself as a thought leader at the intersection of AI and energy. Her continued contributions reflect not only technical excellence but also a visionary approach to sustainable and intelligent resource development.

Tahir Cetin Akinci | Data Analytics in Upstream Operations | Best Research Article Award

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Dr. Tahir Cetin Akinci | Data Analytics in Upstream Operations | Best Research Article Award

Scientist at University of California Riverside, United States

Dr. Tahir Çetin Akıncı is a distinguished academician and researcher in electrical engineering, particularly known for his impactful work in artificial intelligence, renewable energy, and advanced signal processing. With a professional trajectory that spans over two decades, he has consistently contributed to advancing knowledge and innovation in intelligent systems and power electronics. His commitment to both academic excellence and real-world problem-solving has earned him global recognition, positioning him as a thought leader in his field.

Profile

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Education

Dr. Akıncı began his academic journey at Klaipeda University in Lithuania, earning his undergraduate degree in electrical engineering in 2000. He later pursued graduate studies at Marmara University, where he completed his master’s degree in 2005 and Ph.D. in 2010. These formative academic experiences laid the groundwork for his future research directions, particularly in the domains of energy systems and machine learning applications. His educational path is marked by a solid foundation in electrical systems theory, enriched by practical insights into data-driven methodologies.

Experience

His professional career commenced as a Research Assistant at Marmara University, where he served from 2003 to 2010. He then joined Istanbul Technical University (ITU), advancing through academic ranks to become a full professor by 2020. Currently, Dr. Akıncı serves at the University of California, Riverside, contributing to international collaborations and high-impact research initiatives. Throughout his tenure in academia, he has mentored students, led research projects, and collaborated across disciplines to address critical engineering challenges.

Research Interest

Dr. Akıncı’s research interests are both broad and deep, encompassing renewable energy systems, artificial neural networks, deep learning, machine learning, cognitive systems, signal processing, and data analysis. His multidisciplinary approach allows him to tackle complex problems—ranging from optimizing photovoltaic systems to diagnosing electrical motor faults using AI. His work in renewable energy technologies and smart systems not only enhances system efficiencies but also aligns with global sustainability goals. He is particularly passionate about the integration of AI in diagnostics, predictive maintenance, and energy management, striving to create systems that are not only intelligent but also resilient and sustainable.

Award

His contributions have been recognized through multiple prestigious awards, most notably the International Young Scientist Excellence Award and the Best Researcher Award in 2022. These accolades reflect his pioneering work and the high regard he holds within the scientific community. In addition to these honors, Dr. Akıncı has played critical roles as editor and guest editor for leading journals and serves on scientific committees of several high-profile international conferences, further underscoring his influence in shaping future directions in electrical engineering and AI.

Publication

Among his extensive list of publications, several recent papers stand out for their innovative approaches and significant citations. For instance:

“Sustainable pathways for hydrogen production: Metrics, trends, and strategies for a zero-carbon future,” Sustainable Energy Technologies and Assessments, 2025 – cited for its strategic insights on green hydrogen.

“Revealing GLCM Metric Variations across Plant Disease Dataset,” Electronics, 2024 – contributes to deep learning applications in agriculture.

“Optimization of Neuro-controller Application for MPPT in Photovoltaic Systems,” Electric Power Components and Systems, 2024 – enhances energy efficiency using AI.

“Time Series Forecasting Utilizing AutoML,” Information, 2024 – applies automated ML for forecasting in diverse datasets.

“Advanced Dual RNN Architecture for Electrical Motor Fault Classification,” IEEE Access, 2023 – highly cited for its innovation in motor diagnostics.

“Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication,” IEEE Access, 2023 – strengthens smart grid reliability.

“Effect of LED Light Frequency on an Object in Terms of Visual Comfort,” Electric Power Components and Systems, 2024 – explores visual ergonomics in energy-efficient lighting.

Conclusion

In conclusion, Dr. Tahir Çetin Akıncı exemplifies the ideal candidate for this award through his unwavering commitment to scientific advancement, innovation in engineering education, and leadership in multidisciplinary research. His work bridges the gap between theory and application, aiming to develop technologies that not only solve current engineering problems but also anticipate future challenges. Through his dedication, he continues to inspire the next generation of engineers and scientists, fostering a more intelligent and sustainable world.

Massine Bouchakour | Petroleum Geology | Best Researcher Award

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Dr. Massine Bouchakour | Petroleum Geology | Best Researcher Award

PhD at Southwest Petroleum University, China

Massine Bouchakour is a highly driven postdoctoral researcher in marine geology currently based at Southwest Petroleum University in China. With a multidisciplinary and international approach, his research focuses on deep-marine sedimentary systems, seismic interpretation, and reservoir connectivity in structurally complex geological settings. Having been trained across several institutions and mentored by global experts in geology, Bouchakour combines advanced scientific techniques with a passion for knowledge dissemination and collaborative research. His academic journey reflects not only deep technical competence but also an ongoing commitment to applied geoscience, interdisciplinary research, and academic mentorship.

Profile

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Education

Massine Bouchakour began his formal education in sedimentology with an MSc (Hons) at the University of Sciences and Technology Houari-Boumédiène (USTHB) in Algeria, focusing on Paleozoic stratigraphy and sedimentary processes in the Ougarta Basin. His thesis, awarded first-class honors, laid the groundwork for his interest in sequence stratigraphy and basin evolution. He later pursued a PhD at Southwest Petroleum University (SWPU) in China, where he specialized in seismic interpretation of submarine channels and their interaction with tectonic structures. Supported by the Sichuan Provincial Government and the National Natural Science Foundation of China (NSFC), his doctoral research explored geomorphological controls and depositional patterns in the Niger Delta. His ongoing postdoctoral work at SWPU further extends this expertise into global deep-marine systems.

Experience

With over a decade of academic and applied research experience, Bouchakour has played key roles in several international and industry-sponsored projects. His expertise spans seismic geomorphology, stratigraphy, and reservoir characterization. During his postdoctoral tenure, he has collaborated with institutions in Portugal, Romania, and the UK, contributing to efforts funded by CNOOC International and SINOPEC. He has also been instrumental in supervising PhD students and mentoring laboratory research in seismic and borehole data analysis. His career is marked by leadership in developing predictive models for deep-marine sand distribution and contributing to research initiatives such as the Deep-marine Sedimentary Architecture Knowledge Store. He is actively involved in scientific communication, presenting at major conferences, and publishing in top-tier geology journals.

Research Interests

Massine Bouchakour’s research interests center on understanding the architecture and evolution of deep-water depositional systems, particularly in tectonically active margins. He investigates sedimentary processes, seismic facies, sequence stratigraphy, and reservoir sand connectivity using multi-scale data, including 3D seismic surveys and core analysis. His work emphasizes the role of structural deformation in influencing sediment routing and accumulation, as well as the implications for hydrocarbon exploration and offshore carbon storage. He is equally invested in seismic attribute interpretation, field-outcrop integration, and the development of novel geological models to predict subsurface behavior and optimize production well designs.

Awards and Grants

Throughout his career, Bouchakour has earned significant recognition through competitive academic and industrial funding. These include the Chengdu Postdoctoral Research Station grant (2023) and a major NSFC-funded project (2020) supporting his PhD work. He also received the SWPU PhD scholarship with full tuition and accommodation support. In addition, his contributions to industrial research have been recognized in projects sponsored by CNOOC and SINOPEC, where he supported reservoir modeling, structural analysis, and seismic interpretation. Notably, his 2022 paper in Marine and Petroleum Geology was ranked among the top ten downloaded papers by SSRN in the field of marine sediments.

Publications

Bouchakour has authored impactful publications across international peer-reviewed journals. His 2025 paper in Basin Research, titled “Kinematics of submarine channels in response to bank failures,” provides insights into slope instability effects on channel migration.

His 2024 article in Marine and Petroleum Geology, “Compartmentalization of submarine channel splays controlled by growth faults and mud diapir,” explores fault-controlled sediment distribution. Another 2023 Basin Research study investigates “Lateral migration and channel bend morphology around growing folds” in the Niger Delta.

His 2022 work in Marine and Petroleum Geology, “Evolution of submarine channel morphology in intra-slope mini-basins,” based on 3D seismic data, has been widely cited for its methodological rigor.

His collaboration in a 2025 Marine and Petroleum Geology paper, “Tectono-stratigraphic evolution of multiphase rifts,” and two co-authored 2024–2025 stratigraphy-focused publications in Journal of Palaeogeography and Oil and Gas Geology have further solidified his status as a respected geoscientist.

These works have been cited in numerous geological modeling and sedimentology studies globally.

Conclusion

Massine Bouchakour represents a new generation of geoscientists whose global experience, technical versatility, and interdisciplinary insight position him as a leader in marine sedimentology and subsurface geological research. Through his ongoing commitment to applied geoscience, teaching, and international collaboration, he continues to make meaningful contributions to both academic knowledge and industry practice. His research outputs not only enrich scientific understanding but also provide practical frameworks for addressing critical challenges in energy exploration and environmental sustainability.

Ashutosh Sharma | Artificial Lift Systems | Best Researcher Award

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Dr. Ashutosh Sharma | Artificial Lift Systems | Best Researcher Award

Graduate Research Assistant at University of OKlahoma, United States

Ashutosh Sharma is an experienced doctoral candidate in Petroleum Engineering with a solid foundation in data science and energy systems. He specializes in applying advanced machine learning techniques to optimize drilling operations and subsurface analysis. With a practical industry background and academic expertise, Ashutosh has contributed to real-time drilling efficiency, rock-bit interaction studies, and predictive modeling for petrophysical properties. His multidisciplinary approach bridges traditional petroleum engineering practices with modern data-driven solutions, making him a well-rounded professional prepared to address the evolving challenges of the energy sector.

Profile

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Education

Ashutosh is pursuing a Ph.D. in Petroleum Engineering at the University of Oklahoma with a perfect GPA of 4.0, focusing his dissertation on incorporating rock behavior into real-time drilling analysis. Complementing this, he is also earning an M.S. in Data Science & Analytics from Georgia Institute of Technology, maintaining a GPA of 3.9. He holds a prior M.S. in Petroleum Engineering from the University of Oklahoma, where he researched centrifugal packer-type downhole separators. His academic foundation was established with a B.S. in Petroleum Engineering from the Maharashtra Institute of Technology in India, where his capstone project centered on performance modeling and optimization in undersaturated oil reservoirs.

Experience

Ashutosh’s diverse work experience spans both academic research and hands-on industry roles. Most recently, he interned at Pioneer Natural Resources (now under ExxonMobil), where he developed a digital framework for drill string vibration modeling using surface and downhole data from 18 wells in the Midland Basin. Prior to that, he interned at Ensign Drilling, focusing on real-time stick-slip vibration detection using machine learning. Since 2020, he has served as a Graduate Research Assistant at the University of Oklahoma, contributing to DOE-funded projects on rock-bit interaction, real-time drilling efficiency modeling, and petrophysical parameter prediction at the bit. He also brings industry experience from Raeon Energy Services LLP, where he worked in well intervention design, site operations, and bid proposal drafting. Earlier, during an internship at NOV, he streamlined data systems and tools for drill pipe evaluation.

Research Interest

Ashutosh’s research interests lie at the intersection of petroleum engineering and data analytics, focusing on real-time drilling analysis, rock-bit interaction modeling, machine learning applications in drilling optimization, and subsurface prediction. He is especially driven by the application of data-driven approaches to enhance drilling safety, efficiency, and reservoir characterization. His work seeks to enable predictive decision-making at the rig floor, transform vibration analysis methodologies, and innovate in the field of downhole separation and petrophysical log projection.

Award

Ashutosh has been recognized for both his academic achievements and professional contributions. He received the SPE General Scholarship in the Ph.D. category, sponsored by the SPE OKC chapter, for two consecutive years (2022 and 2023). As an active participant in student competitions, he won three 1st place titles as part of the University of Oklahoma’s Petrobowl team from 2017 to 2021. He also served as Vice-President of the OU SPWLA student chapter during 2019–2020. Notably, he received a Letter of Appreciation from a project head for high-quality services rendered in the Krishna Godavari and Cambay basins. His early achievements include winning 1st prize at the MIT SPE AIIIP Case Study Challenge, sponsored by Schlumberger.

Publication

Ashutosh’s scholarly work reflects a consistent focus on machine learning applications in petroleum systems. His notable journal articles include:

Evaluating PDC bit-rock interaction models to investigate torsional vibrations in Geothermal drilling (Geothermics, Elsevier, 2024; cited by 18 articles),

Real-time lithology prediction at the bit using machine learning (Geosciences, MDPI, 2024; cited by 9 articles),

Predicting separation efficiency of a downhole separator using machine learning (Energies, MDPI, 2024; cited by 7 articles).
He has also presented several conference papers, including at URTEC Buenos Aires (2023), SPE Offshore Europe Aberdeen (2023), SPE OKC Symposium (2023), US Rock Mechanics Symposium (2021), and the SPE Artificial Lift Conference-Americas (2020). His MS thesis was focused on the experimental evaluation of a centrifugal packer-type downhole separator.

Conclusion

Ashutosh Sharma’s multidisciplinary skill set, merging petroleum engineering fundamentals with cutting-edge data analytics, positions him uniquely in the energy industry. Through his academic rigor, research innovation, and industry collaborations, he has demonstrated a commitment to advancing efficient, safe, and sustainable drilling practices. As he completes his Ph.D. and dual Master’s degrees, Ashutosh is poised to contribute meaningfully to organizations leading the energy transition and digital transformation in upstream oil and gas operations.