Advancements in Geological Fluid Flow and Mechanical Properties
A special issue of Geosciences (ISSN 2076-3263).
Deadline for manuscript submissions: 31 December 2026 | Viewed by 24
Special Issue Editors
Interests: petroleum geomechanics; hydraulic fracturing; poroelastoplastic modeling; CO2 sequestration; hydrogen storage
Interests: subsurface hydrology; steady-state and turbulent fluid mechanics; porous media modelling; stochastic modeling; erosion and sand production; hydro-mechanical coupling
Special Issue Information
Dear Colleagues,
This Special Issue aims to highlight recent advancements in the understanding and modeling of geological fluid flow and the advanced mechanical behavior of rocks under varying subsurface conditions. Subsurface environments are increasingly significant for applications such as carbon capture and storage, hydrogen storage, and geothermal energy. These processes involve complex interactions between fluid flow, rock deformation, and thermal or chemical effects, which require integrated, multidisciplinary approaches for effective characterization and prediction.
We welcome original research articles, comprehensive reviews, and high-quality case studies that explore experimental numerical and analytical modeling approaches to fluid transport and rock and fracture mechanics. Topics of interest include, but are not limited to, the following:
- Applications in CO2 sequestration, hydrogen storage, petroleum recovery, geothermal energy, and geotechnical engineering.
- Coupled hydro-mechanical and thermo-hydro-mechanical processes.
- Multiphase and reactive fluid flow in porous and fractured media.
- Fracture propagation, fault activation, and permeability evolution.
- Experimental and numerical studies of rock mechanical behavior.
- Machine learning and data-driven approaches in geo-fluid and rock mechanics.
The Special Issue seeks to provide a platform for geoscientists, engineers, and researchers to share innovative methodologies and findings that contribute to the advancement of knowledge in this field. By fostering cross-disciplinary dialog and collaboration, this Special Issue aims to support the development of more accurate predictive models and safer, more sustainable subsurface engineering practices.
Dr. Ernestos Sarris
Dr. Elias Gravanis
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- CO2 sequestration
- hydrogen storage
- geological multiphase flows
- experimental rock mechanics
- hydro-mechanical coupling
- thermo-hydro-mechanical processes
- fluid driven fracture propagation
- petroleum geomechanics
- sand production prediction
- poroelasticity and poroelastoplasticity
- machine learning in geosciences
- geothermal systems
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