Huayu Long | Data Analytics in Upstream Operations | Best Researcher Award

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Dr. Huayu Long | Data Analytics in Upstream Operations | Best Researcher Award

Ph.D. at Tianjin University, China

Huayu Long is an emerging scholar and educator in the field of business administration, with a focus on innovation management, digital transformation, and supply chain resilience. Currently pursuing a Ph.D. in Business Administration at Tianjin University, Huayu combines a strong academic foundation with practical teaching experience. From teaching at Karamay Vocational and Technical College to conducting research on digital governance and supply chain restructuring, Huayu has demonstrated a commitment to addressing complex challenges facing modern businesses. With international academic training and a growing presence in scholarly publications and conferences, Huayu Long represents a new generation of business researchers actively shaping the discourse on organizational strategy and innovation in the digital era.

Profile

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Education

Huayu Long holds a Master’s degree in Human Resource Management from the University of Lincoln, earned between July 2014 and January 2016, where their studies concentrated on technology innovation management. This academic experience in the UK provided an international perspective on organizational development and technology integration. Building on this foundation, Huayu began Ph.D. studies at Tianjin University in September 2023, specializing in innovation management, supply chain management, and digital transformation. Their current research explores how these interconnected fields can be harnessed to drive organizational resilience and performance in rapidly evolving economic contexts.

Experience

From October 2016 to August 2021, Huayu Long served as a teacher at Karamay Vocational and Technical College. During this period, Huayu was responsible for designing and delivering courses that bridged theoretical knowledge with practical business applications. The role involved not only teaching but also mentoring students and contributing to curriculum development. This experience helped shape Huayu’s understanding of how to translate complex research concepts into accessible knowledge for learners and professionals alike. It also strengthened their interest in exploring real-world challenges through academic inquiry, ultimately leading to advanced research in supply chain and innovation management.

Research Interest

Huayu Long’s research interests lie at the intersection of digital transformation, innovation management, and supply chain restructuring. Their work investigates how organizations can adapt their internal structures and external relationships to remain competitive in digitally driven environments. Specific research themes include the role of digital governance in shaping government-business interactions, the impact of resource restructuring on supply chain resilience, and strategies for navigating supply chain disruptions in an era marked by anti-globalization pressures. By integrating theoretical frameworks with empirical analysis, Huayu aims to contribute actionable insights that inform both academia and industry.

Awards

Huayu Long has actively participated in leading academic conferences that showcase innovative research in business and management. These include the 18th International Conference of Operations and Supply Chain Management and the 2024 Strategic Supply Chain Experts 50 Forum held at Tongji University, as well as the 2nd China Enterprise Innovation and Platform Governance Academic Annual Conference hosted by Zhejiang University of Technology. Most recently, Huayu presented at the Organization Management, Strategy, and Innovation Research conference at Xi’an Jiaotong University in April 2025. These invitations reflect the academic community’s recognition of Huayu’s work and its relevance to ongoing discussions in innovation and supply chain research.

Publications

Huayu Long has authored and co-authored several peer-reviewed articles in reputable journals.

In 2025, the article “The Effect of Resource Restructuring on Supply Chain Resilience in the Context of Digital Transformation” was published in Systems and is increasingly cited in digital strategy literature.

Another 2025 publication, “How can digital governance ecology enable the clean development of the relationship between government and business?” appeared in E-Government, applying the TOE framework to explore the dynamics of digital governance—this work has sparked discussion among scholars of digital public administration.

Earlier, Huayu published “Research on economic effects of accounting” in Business News (2020), a Chinese-language article that attracted regional academic attention. Each of these publications highlights a different aspect of Huayu’s research focus and demonstrates their contribution to critical topics in business transformation.

Conclusion

Huayu Long exemplifies a rising academic voice in the fields of innovation management and digital-era business strategy. With a balanced portfolio of teaching, research, and academic engagement, Huayu brings intellectual curiosity, methodological rigor, and real-world relevance to their work. Their publications and conference contributions reflect a deep commitment to exploring the transformative impact of digital technologies on supply chains, governance structures, and organizational resilience. As they continue their doctoral research and broaden their academic collaborations, Huayu is well-positioned to make lasting contributions to the business research community. Their work not only aligns with current academic trends but also anticipates the evolving needs of global industries, making them a strong candidate for this award nomination.

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.