Advancing permeability prediction in fractured and porous media: A bibliometric and systematic review (2020–2024) integrating machine learning, Lattice Boltzmann Methods, and multiscale modeling

Title : Advancing permeability prediction in fractured and porous media: A bibliometric and systematic review (2020–2024) integrating machine learning, Lattice Boltzmann Methods, and multiscale modeling

Abstract:

This study presents a comprehensive bibliometric and systematic review of permeability modeling in fractured and porous media, covering the period 2020–2024. Following the PRISMA framework and employing bibliometric analysis through the Biblioshiny web application, 212 documents from the Scopus database were systematically analyzed to evaluate the intellectual structure, thematic trends, and collaborative networks in this research domain. The review identifies significant advancements in numerical modeling techniques, particularly the Lattice Boltzmann Method (LBM) and Computational Fluid Dynamics (CFD), which have overcome limitations of traditional models such as the Kozeny–Carman equation. A notable emerging trend is the integration of machine learning and artificial intelligence approaches, enhancing prediction accuracy and computational efficiency for complex fracture and matrix porosity conditions. Performance analysis reveals China as the dominant contributor in terms of publications (253 documents) and citations (1,011), with Tsinghua University and the China University of Mining and Technology among the most active institutions. Thematic mapping identifies three primary clusters: material and structural properties, computational methodologies, and biomechanical applications. Key challenges persist, including over-reliance on idealized models, high computational demands, lack of standardized methodologies, and insufficient integration of sustainability principles. Future research directions emphasize AI-powered modeling frameworks, multiscale approaches, and applications in carbon capture, geothermal energy, and next-generation energy storage systems.

Keywords: Permeability prediction, porous media, fracture porosity, matrix porosity, bibliometric analysis, machine learning, Lattice Boltzmann Method, systematic literature review

Biography:

Yaseen ur Rehman is a master's student in safety sciences and engineering in China with a background in chemical engineering. His academic interests include risk assessment, process safety, environmental engineering, and industrial safety systems. He has also engaged in international education activities and interdisciplinary studies across engineering and management. His current research focus is on advancing safety analysis methods and sustainable engineering solutions while developing expertise in safety and environmental applications.