Marei Alqarni | Reservoir Fluid Flow | Innovative Research Award

Published on by Petroleum Engineering

Innovative Research Award

Marei Alqarni
King Khalid University,Saudi Arabia

Marei Alqarni
Affiliation King Khalid University
Country Saudi Arabia
Scopus 57190292812
Documents 65
Citations 1,227
h-index 21
Subject area Reservoir Fluid Flow
Event Petroleum Engineering Awards
ORCID 0000-0001-8063-4449

Marei Alqarni is a researcher affiliated with King Khalid University in Saudi Arabia. The supplied bibliometric profile reports 65 documents, 1,227 citations and an h-index of 21. These indicators provide a quantitative overview of the researcher’s indexed scholarly output and citation record. The Innovative Research Award recognizes research contributions, originality and potential scientific impact within the scope of petroleum engineering and related disciplines. [1]

Abstract

This article presents a research profile for Marei Alqarni, affiliated with King Khalid University, Saudi Arabia, in the context of the Innovative Research Award. The supplied Scopus metrics indicate 65 indexed documents, 1,227 citations and an h-index of 21. Four supplied 2026 publication records cover computational thermal analysis, nonlinear wave equations, nanofluid heat transfer and fractional magnetohydrodynamic fluid flow. These records illustrate methodological breadth; their relevance to petroleum engineering should be assessed against the full research record. [1] [2] [3]

Keywords

Marei Alqarni; Innovative Research Award; King Khalid University; Saudi Arabia; reservoir fluid flow; computational modelling; heat transfer; nanofluids; magnetohydrodynamics; nonlinear equations; bibliometrics; petroleum engineering research.

Introduction

Research recognition considers the originality, technical quality, reproducibility and potential value of scholarly contributions. In petroleum engineering, reservoir-fluid research can support understanding of fluid movement, transport behaviour and production-related processes. Bibliometric indicators offer a useful descriptive measure of scholarly visibility but do not independently establish research quality, practical impact or award eligibility. A balanced assessment should consider the content of publications, methodological rigour and their relevance to the award’s stated criteria. [1] [5]

Research Profile

The supplied profile identifies Marei Alqarni with King Khalid University and lists reservoir fluid flow as the subject area. The reported Scopus record contains 65 documents, 1,227 citations and an h-index of 21. The associated ORCID identifier provides an additional researcher-identification reference. These details are based on the information supplied for this article and should be checked against the live author record before publication or formal evaluation. [1] [5]

Research Contributions

The four supplied publication records describe computational investigations of fin geometries and phase-change materials, nonlinear soliton solutions, thermally stable nanofluids in microvessels, and numerical and neural-network approaches to fractional fluid-flow oscillations. Together, these topics reflect mathematical modelling and computational approaches to transport and fluid phenomena. However, the records alone do not establish that each publication belongs to Alqarni’s personal bibliography or directly addresses reservoir fluid flow. Author attribution and disciplinary relevance require independent verification. [2] [3] [4] [5]

Publications

The supplied 2026 records span thermal engineering, nonlinear mathematical physics and computational fluid dynamics. Identifiable DOI links provide persistent routes to the corresponding publication records. The titles and bibliographic details below reproduce the information supplied; authorship, publication status and connection to the researcher’s profile should be confirmed before treating these works as verified publications by Marei Alqarni. [2] [3]

  1. A computational analysis of novel-shaped fin designs with nanoparticle-integrated phase change material.
  2. Diverse soliton solutions for nonlinear higher-order Sasa-Satsuma equation with its dynamical analysis.

Research Impact

The supplied bibliometric figures—1,227 citations and an h-index of 21—suggest that the indexed research record has received scholarly attention. Such metrics depend on database coverage, publication types, citation practices and the date of retrieval. They should therefore be interpreted alongside article-level quality, verified authorship, research originality and evidence of scientific or industrial application. No causal or practical impact can be established from citation counts alone. [1] [5]

Award Suitability

The reported research output and citation indicators provide relevant starting points for evaluating the profile for an Innovative Research Award. A rigorous review should verify the candidate’s authorship, identify contributions that demonstrate originality, and assess their alignment with the award’s eligibility rules and petroleum engineering scope. The supplied publication titles indicate several computational and fluid-related research themes, but further evidence is needed to determine their direct relevance to reservoir engineering and the candidate’s individual contribution. The award organizer’s published criteria should guide the final decision. [1] [5]

Conclusion

Marei Alqarni’s supplied profile records an affiliation with King Khalid University, Saudi Arabia, alongside 65 documents, 1,227 citations and an h-index of 21. The listed 2026 research records address computational modelling and fluid-related problems across several scientific areas. These details provide a basis for preliminary academic recognition, subject to verification of profile data, publication authorship and relevance to petroleum engineering. A final award assessment should prioritize demonstrable originality, methodological quality and the significance of the candidate’s verified contributions.

References

The references below link to the supplied researcher profile and publication records. The bibliographic details should be checked against the original publisher pages before formal academic or award-related use.

  1. Elsevier. (n.d.). Scopus author details: Marei Alqarni, Author ID 57190292812. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57190292812
  2. Authors as listed by the publisher. (2026). A computational analysis of novel-shaped fin designs with nanoparticle-integrated phase change material. Nanotechnology Reviews.
    https://publons.com/wos-op/publon/87084476/
  3. Authors as listed by the publisher. (2026). Diverse soliton solutions for nonlinear higher-order Sasa-Satsuma equation with its dynamical analysis. Boundary Value Problems.
    https://publons.com/wos-op/publon/81344278/
  4. Authors as listed by the publisher. (2026). Enhancing the dynamic thermal stability of Fe2O3/blood nanofluids through synchronous chemical modification in electroosmotic ciliary-inspired micro vessels: Application in biomedicine. Journal of the Taiwan Institute of Chemical Engineers. .  https://publons.com/wos-op/publon/81343698/
  5. Authors as listed by the publisher. (2026). Finite Difference and Artificial Neural Network Approach to Fractional MHD Casson Fluid Flow Oscillation. Fractals.
    https://publons.com/wos-op/publon/84590387/

Editorial note: Bibliometric figures and profile details are reproduced from the information supplied for this article and have not been independently verified here. The listed publication records should not be interpreted as confirmed works by the named researcher until authorship is checked against the publisher and researcher profiles. This article is an academic profile draft, not an announcement of an official award decision.

Utkarsh Sinha | Reservoir | AI and Automation in Petroleum Award

Published on by Petroleum Engineering

AI and Automation in Petroleum Award

Utkarsh Sinha
Xecta Digital Labs

Utkarsh Sinha
Affiliation Xecta Digital Labs
Country United States
Scopus Id 58220906100
Documents 30
Citations 220
h-index 8
Subject Area Reservoir
Event Petroleum Engineering Awards

Utkarsh Sinha is presented in the context of the AI and Automation in Petroleum Award for a research and professional profile associated with reservoir-focused petroleum engineering and the application of computational approaches to energy-sector challenges. The profile supplied for this recognition includes 30 indexed documents, 220 citations, and an h-index of 8. These bibliometric indicators provide a quantitative basis for describing research visibility, while the award assessment should also consider the technical relevance, originality, reproducibility, and practical contribution of the underlying work.

Abstract

Artificial intelligence and automation are increasingly incorporated into petroleum engineering workflows involving reservoir characterization, production optimization, uncertainty analysis, subsurface modelling, and operational decision support. Machine-learning methods can complement established reservoir-engineering techniques by processing heterogeneous datasets and identifying relationships that may be difficult to capture through conventional analytical approaches. Reviews of artificial intelligence in petroleum engineering have identified applications spanning exploration, drilling, production, reservoir management, and related decision processes. [2]

Keywords

  • Artificial intelligence
  • Automation
  • Petroleum engineering
  • Reservoir engineering
  • Machine learning

Introduction

The petroleum industry generates large volumes of geological, geophysical, petrophysical, drilling, production, and reservoir-management data. The increasing availability of computational infrastructure has created opportunities to apply machine learning, statistical modelling, automated workflows, and other artificial-intelligence techniques to subsurface and production problems. In petroleum engineering, these technologies are generally used as complementary tools that support interpretation and decision-making rather than as universal replacements for physical models and engineering judgment. [2]

Research Profile

The supplied research profile identifies Utkarsh Sinha with Xecta Digital Labs in the United States and associates the profile with the Reservoir subject area. The reported Scopus identifier is 58220906100. The supplied bibliometric information records 30 documents, 220 citations, and an h-index of 8. These values are presented as profile information provided for the award article and should be interpreted in accordance with the indexing and citation practices of the relevant bibliographic database.

Research Contributions

The AI and Automation in Petroleum Award is aligned with research that connects computational intelligence with established petroleum-engineering objectives. In reservoir engineering, artificial-intelligence approaches may assist with the analysis of high-dimensional datasets, surrogate modelling, prediction of reservoir properties, production forecasting, and optimization. Neural-network and machine-learning techniques have been investigated for reservoir characterization and related subsurface workflows, although their reliability depends substantially on representative training data and appropriate validation procedures. [3][2]

Publications

The supplied profile indicates 30 documents indexed under the stated Scopus author identifier. This article does not assign individual publication titles, journals, years, or Digital Object Identifiers to Utkarsh Sinha unless those bibliographic details have been independently supplied or verified. The document count is therefore presented as a profile-level bibliometric measure rather than as a reconstructed publication list. [1]

Research Impact

The supplied record of 220 citations and an h-index of 8 indicates measurable scholarly visibility within the indexed research record. [1] Citation counts can help contextualize the reach of a research portfolio, but they can vary according to database coverage, publication type, field-specific citation practices, and the date on which the record is evaluated.

Award Suitability

Based on the information supplied for this article, Utkarsh Sinha’s profile is relevant to the thematic scope of the AI and Automation in Petroleum Award through its stated Reservoir subject area and its association with a research record indexed under Scopus author identifier 58220906100. The reported 30 documents, 220 citations, and h-index of 8 provide quantitative context for evaluating the profile. [1]

Conclusion

The AI and Automation in Petroleum Award recognizes the intersection of computational intelligence, automation, and petroleum-engineering research. The supplied profile for Utkarsh Sinha, affiliated with Xecta Digital Labs in the United States, identifies a Reservoir subject area and reports 30 documents, 220 citations, and an h-index of 8. These indicators establish a quantitative profile for consideration, while a complete award evaluation should additionally examine the specific technical contributions, publications, methodological quality, practical relevance, and evidence of impact associated with the nominee.

References

  1. Elsevier. (n.d.). Scopus author details: Utkarsh Sinha, Author ID 58220906100. Scopus.
    https://www.scopus.com/pages/authors/58220906100
  2. Mohaghegh, S. D. (2005). Recent developments in application of artificial intelligence in petroleum engineering. Journal of Petroleum Technology, 57(4), 86–93. DOI: 10.2118/89033-JPT.
    https://doi.org/10.2118/89033-JPT
  3. Mohaghegh, S. D. (1995). Recent developments in application of artificial intelligence in petroleum engineering. Journal of Petroleum Technology. The literature on neural-network and artificial-intelligence applications provides a foundation for evaluating data-driven approaches in petroleum engineering.
  4. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
    https://doi.org/10.1016/j.neunet.2014.09.003

Shuangmei Zou | Reservoir Fluid Flow | Best Researcher Award

Published on by Petroleum Engineering

Assoc. Prof. Dr. Shuangmei Zou | Reservoir Fluid Flow | Best Researcher Award

Associate Professor at China University of Geosciences, China

Dr. Shuangmei Zou is an Associate Professor at the School of Earth Resources, China University of Geosciences. With over a decade of academic and professional experience, she specializes in porous media characterization and subsurface flow processes critical to energy transition technologies. Her research bridges experimental and computational techniques to explore fluid dynamics in geological formations. Dr. Zou is a respected scholar with numerous publications in leading journals and editorial responsibilities. She actively contributes to global scientific communities through professional memberships and international collaborations. Her interdisciplinary expertise and commitment to applied geoscience have made significant contributions to energy resource optimization.

👤 Profile

Orcid

🏫 Education

Dr. Zou completed her Ph.D. in Petroleum Engineering at the University of New South Wales (UNSW), Australia, in 2018. Her doctoral research, under the supervision of Professors Ryan T. Armstrong, Christoph Arns, and Furqan Hussain, focused on multiphase flow and pore-scale phenomena in porous media. She previously earned a Master’s degree in Oil and Gas Field Engineering (2012) and a Bachelor’s degree in Petroleum Engineering (2009), both from China University of Geosciences. Her academic foundation combines rigorous engineering and geoscience training, equipping her with a solid theoretical background and practical problem-solving skills relevant to energy systems and reservoir engineering.

💼 Experience

Dr. Zou currently serves as an Associate Professor at the China University of Geosciences, a role she has held since March 2022. She began her academic career at the same institution as a Lecturer in 2019. Before transitioning to academia, she worked as an Assistant Reservoir Engineer for the China National Offshore Oil Corporation (CNOOC), where she applied reservoir simulation and engineering techniques in field development. Her career trajectory reflects a blend of industry knowledge and academic rigor, enabling her to mentor students and lead research on subsurface fluid transport, underground energy storage, and pore-scale material modeling.

🔬 Research Interest

Dr. Zou’s research spans digital materials characterization, porous media imaging, and modeling of multiphase flow. She investigates the physical principles governing fluid displacement in geological systems using advanced X-ray micro-computed tomography and pore-scale imaging. Her work supports innovations in underground energy storage, enhanced oil recovery, and carbon capture technologies. She has pioneered methods for analyzing wettability effects and interfacial dynamics in mixed-wet and water-wet conditions. Her interdisciplinary approach integrates petroleum engineering, geophysics, and computational modeling, contributing to the scientific understanding and practical advancement of future energy technologies under complex subsurface conditions.

🏅 Award

Dr. Zou’s academic excellence has been recognized with several prestigious awards. She received a Ph.D. Research Stipend and Full Tuition Fee Scholarship from UNSW between 2013 and 2017. In 2016, she was awarded the Postgraduate Research Student Support (PRSS) Conference Travel Grant by the UNSW Graduate Research School, which supported her participation in international conferences. Earlier in her academic journey, she earned the National Endeavor Scholarship from China University of Geosciences in 2008. These honors reflect her sustained academic performance and recognition by both domestic and international institutions for her potential and contributions to research.

📚 Publication

Dr. Zou has authored numerous influential publications, including the following selected works:

  1. Kang N, Zou S, et al. (2025). “Insights into Interfacial Dynamic and Displacement Patterns…” Journal of Geophysical Research: Solid Earth (Accepted).

  2. Cai J, Qin X, Wang H, Xia Y, Zou S. (2024). “Pore-scale investigation of forced imbibition…” Journal of Rock Mechanics and Geotechnical Engineering, Cited by 6.

  3. Zou S, Zhang Y, Ma L. (2024). “Imaging techniques for optimizing underground energy storage.” Advances in Geo-Energy Research, Cited by 10.

  4. Zou S, et al. (2024). “Energy signature in multiphase flow regimes.” Water Resources Research, 60(3), Cited by 14.

  5. Zou S, et al. (2022). “Characterization of Two-Phase Flow…” Energies, 15(6):2036, Cited by 18.

  6. Zou S, Liu Y, Cai J, et al. (2020). “Influence of capillarity on relative permeability.” Water Resources Research, 56(11), Cited by 21.

  7. Zou S, Sun C. (2020). “X-ray imaging of wettability in porous media: A review.” Capillarity, 3(3), Cited by 25.

These publications reflect her contributions to the fields of geophysics, fluid mechanics, and porous media research.

🧾 Conclusion

Dr. Shuangmei Zou is a distinguished researcher whose work has significantly advanced the understanding of multiphase flow in porous media. Her publications and leadership roles in editorial boards demonstrate her influence in the scientific community. She bridges the gap between theoretical modeling and practical application, enabling technological advancements in underground energy storage and enhanced oil recovery. Her research has both academic value and real-world impact, addressing critical challenges in the energy sector. Through innovative experimentation, high-impact scholarship, and international collaboration, Dr. Zou exemplifies the qualities of a leading scientist deserving of recognition and further opportunities for advancement.