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Cheng Chen

Professor

Stevens Institute of Technology

United States

Research Interests

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About

Cheng Chen is a Professor at the Stevens Institute of Technology, United States. His research primarily focuses on porous media, geological carbon sequestration, and the application of machine learning in geological studies. Recent publications include work on solute dispersion in unsaturated porous media, as well as the use of hybrid neural network architectures for property predictions in porous materials. He actively investigates the implications of fracture conductivity and two-phase flow dynamics for underground hydrogen storage.

Articles (14)

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2026

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2024

Collaborators (6)

Sherilyn Williams-Stroud

Professor of Practice and Director, Berg-Hughes Center

Texas A&M University

UNITED STATES

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Yang Liu

Virginia Tech

UNITED STATES

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Qingqi Zhao

University of Stuttgart

GERMANY

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Carlos Torres‐Verdín

Professor

University of Texas at Austin

UNITED STATES

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Maxim Lebedev

Professor

Edith Cowan University

AUSTRALIA

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Parisa Bazazi

Assistant Professor

Colorado School of Mines

UNITED STATES

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Cheng Chen

Research Interests

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About

Articles (14)

Carbon Black Suspension as Electrically Conductive Fracturing Fluid for Fracture Diagnostics in Enhanced Geothermal Systems

Stress and temperature regulated ion exchange selectivity in smectite within deep geological repository environments

Hydrogen adsorption and transport in clay-rich geomaterials: Implications for large-volume underground hydrogen storage

Pore-Scale Study on the Positive Feedback Between Stress and Porosity Caused by Pressure Solution in Porous Media

Pore-scale study on solute dispersion in the aqueous phase within unsaturated porous media

Using a Well-Controlled Heterogeneous Permeability Field to Study Its Role on Miscible Density-Driven Convection in Porous Media and the Implications to Geological Carbon Sequestration

Experimental and numerical investigation of fracture conductivity between non-smooth rock surfaces with and without proppant

A computationally efficient hybrid neural network architecture for porous media: Integrating convolutional and graph neural networks for improved property predictions

Using X-ray computed tomography and pore-scale numerical modeling to study the role of heterogeneous rock surface wettability on hydrogen-brine two-phase flow in underground hydrogen storage

Review: Pre-Darcy flows in low-permeability porous media

Comparative Assessment of U-Net-Based Deep Learning Models for Segmenting Microfractures and Pore Spaces in Digital Rocks

Time‐And‐Space Averaging Applied to Intermittent Multiphase Flow Experiments

Machine learning and deep learning for mineralogy interpretation and CO2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Underground hydrogen storage: A recovery prediction using pore network modeling and machine learning

Collaborators (6)

Sherilyn Williams-Stroud

Yang Liu

Qingqi Zhao

Carlos Torres‐Verdín

Maxim Lebedev

Parisa Bazazi

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