Fluid Dynamics Modeling in Porous Media
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".
Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 21546
Special Issue Editors
Interests: multiphase flow; hydrology; multi-physics coupling; fractured porous media; numerical modeling; geomechanics
Interests: machine learning; surrogate model; reservoir simulation; flow and transport in porous media
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modeling fluid flows through fractured and/or deformable porous media remains an interesting but challenging topic in the geo-energy field. Success in geo-energy resources extraction, energy storage, CO2 geosequestration, and understanding ore-forming processes relies strongly upon the accurate modeling of single-/multi-phase fluid flow through porous media. The rapid advancement of physics-driven and data-driven approaches provides us with a rare opportunity to simulate and comprehend essential interplay between fluid flow, heat transfer, stress perturbation, chemical reaction, and pore/permeability evolution. The research in fluid dynamics modeling provides high support in the mitigation of greenhouse gas emissions and the efficient development and utilization of geo-energy resources.
This Special Issue titled “Fluid Dynamics Modeling in Porous Media” is intended to report innovative contributions to fluid dynamics modeling regarding numerical approaches, case studies, and data analytics, which may help advance our understanding of complex fluid flow behaviors underground. Our interests focus on, but are not limited to, the following topics:
- Reactive fluid/heat flow in (deformable) porous media;
- Development of geothermal energy and hydrocarbons;
- Enhanced/engineered geothermal systems;
- Fault slip in response to fluid injection;
- Geo-sequestration of carbon dioxide;
- Underground energy storage;
- Ore-forming fluid evolution;
- Proppant migration through fracture networks;
- Data-driven modeling.
Dr. Sanbai Li
Dr. Qinzhuo Liao
Dr. Shihao Wang
Guest Editors
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Keywords
- fluid flow
- geomechanics
- chemical reactions
- simulation
- greenhouse gas
- hydrocarbons
- geothermal energy
- fault reactivation
- ore-forming fluid
- machine learning
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