Digital Technologies for Oil Recovery and Sustainability
A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Mathematical and Computational Fluid Mechanics".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 368
Special Issue Editors
Interests: cardiovascular hemodynamics; image-based computational fluid dynamics; Lattice Boltzmann Methods (LBM); pore-scale porous media flows
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue, "Digital Technologies for Oil Recovery and Sustainability", will explore the use of advanced digital tools, such as computational fluid dynamics (CFD), artificial intelligence (AI), and machine learning (ML), to enhance oil recovery while promoting sustainability. It will feature research on optimizing fluid flow, real-time reservoir management, enhanced oil recovery (EOR), and minimizing environmental impact. By showcasing the latest innovations, the issue aims to provide a platform for industry professionals and researchers to share knowledge, driving the development of more efficient and environmentally responsible oil recovery practices.
Dr. Huidan (Whitney) Yu
Prof. Dr. Luiz Adolfo Hegele
Guest Editors
Manuscript Submission Information
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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. Fluids 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
- digital oil fields
- computational fluid dynamics (CFD)
- enhanced oil recovery (EOR)
- artificial intelligence in oil recovery
- sustainable oil extraction
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