Best Scholar Award
Mahdi Asghari – MAPSA Co., Iran.
Mahdi Asghari is a researcher associated with research interests in Matrix Acidizing, Numerical Modeling and Computational Software Development. His documented scholarly work focuses on computational approaches for field-scale simulation and prediction of matrix acidizing in carbonate reservoirs. [1]
Abstract
Mahdi Asghari’s documented research focuses on Matrix Acidizing, Numerical Modeling and computational software development. His listed publications address numerical and machine-learning approaches for modeling reactive transport and predicting matrix acidizing behavior at field scale in carbonate reservoirs. [1]
Keywords
Matrix Acidizing; Numerical Modeling; Reactive Transport; Carbonate Reservoirs; Adaptive Mesh Refinement; Physics-Constrained Machine Learning; Field-Scale Simulation; Computational Software Development.
Introduction
Matrix acidizing involves the interaction of reactive fluids with reservoir formations and requires numerical methods capable of representing transport and reaction processes. Computational modeling can support analysis of field-scale behavior, particularly where spatial and chemical variations make direct simulation computationally demanding. [2]
Research Profile
The profile identifies Mahdi Asghari with University of Tehran in the academic record and MAPSA Co. in the provided award information. His research subjects include Matrix Acidizing, Numerical Modeling and Computational Software Development, establishing a focused computational research profile. [1]
Research Contributions
One documented study develops an adaptive mesh refinement technique for field-scale simulation of matrix acidizing in carbonate reservoirs. Another listed study examines physics-constrained machine-learning upscaling of reactive transport for field-scale prediction, connecting computational modeling with data-driven prediction methods. [3]
Publications
The Google Scholar record lists Development of an adaptive mesh refinement technique for field-scale simulation of matrix acidizing in carbonate reservoirs, published in Fuel, volume 399, article 135645, and a 2026 study on physics-constrained machine-learning upscaling of reactive transport. [1]
Research Impact
The Award profile records four documents, ten citations, and an h-index of two, while the attached Google Scholar record reports four citations and an h-index of one. These figures should therefore be understood as profile-specific and potentially different according to the database and retrieval date. [1]
Award Suitability
The documented specialization in matrix acidizing and computational reactive transport provides a defined technical research profile for scholarly recognition. The listed publications and research themes offer identifiable areas through which originality, methodological contribution, and relevance to the candidate’s subject area can be evaluated.
Conclusion
Mahdi Asghari’s documented research centers on computational modeling of matrix acidizing and reactive transport in carbonate reservoirs. His listed work incorporates adaptive numerical methods and physics-constrained machine learning, providing a focused basis for academic recognition within the stated research area.
External Links
References
- Google Scholar. Mahdi Asghari — Google Scholar Profile.
https://scholar.google.com/citations?user=mga6EZAAAAAJ&hl=en
- Asghari, M., & Jahanbakhshi, S. (2025). Development of an adaptive mesh refinement technique for field-scale simulation of matrix acidizing in carbonate reservoirs. Fuel, 399, 135645.
https://doi.org/10.1016/j.fuel.2025.135645
- Asghari, M., Jahanbakhshi, S., & Delaviz, M. N. (2026). Physics-Constrained Machine Learning Upscaling of Reactive Transport for Field-Scale Prediction of Matrix Acidizing in Carbonate Reservoirs. Results in Engineering, 112607.
https://doi.org/10.1016/j.rineng.2026.112607
- Elsevier. Scopus Author Details: Mahdi Asghari, Author ID 59984992200. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=59984992200