Geofluids

Intelligent Approaches for Multi-Scale Fluid Flow in Geofluid Resources


Publishing date
01 May 2022
Status
Closed
Submission deadline
07 Jan 2022

Lead Editor
Guest Editors

1University of Science and Technology Beijing, Beijing, China

2China University of Petroleum (East China), Qingdao, China

3Texas A & M University, College Station, USA

This issue is now closed for submissions.
More articles will be published in the near future.

Intelligent Approaches for Multi-Scale Fluid Flow in Geofluid Resources

This issue is now closed for submissions.
More articles will be published in the near future.

Description

In the field of multi-scale fluid flow in geofluid resources, intelligent approaches that integrate big data and artificial intelligence (AI) technology into traditional simulation and experimental methods have received widespread attention.

The rapid development of data science and computing technology has brought new opportunities as well as challenges to the field, while accelerating the process of digitization in geofluid systems. Moreover, AI algorithms guided by seepage laws can often surpass the limitations of some traditional methods and show great potential, especially for multi-scale flow problems under extremely complex geological conditions. Machine learning, big data analysis, numerical simulation, and other related computing techniques are expected to be helpful in predicting fluid flow behavior in complex geofluid systems and analyzing the underlying relationships between key features.

This Special Issue aims to promote the integration of fluid mechanics, big data analysis, and computer science, and to help solve scientific problems related to the development of geofluid resources. Investigators are invited to contribute to this Special Issue with original research articles as well as comprehensive review articles addressing recent advances in intelligent approaches for multi-scale fluid flow in geofluid resources.

Potential topics include but are not limited to the following:

  • Machine learning with applications to multi-scale fluid flow in geofluid resources
  • Advances in the use of artificial intelligence for the development of geofluid resources
  • Intelligent approaches in fluid-solid-heat coupling combined with numerical simulations
  • Application of deep learning technology for determining reservoir physical properties
  • Computing technology with applications to geomechanics and fluid mechanics in porous media
  • Multi-scale and multi-field coupling simulation with applications to geofluid resource development
  • Multi-phase porous flow simulation with applications to geofluid resource development
  • Application of blockchain technology to the petroleum industry
  • Intelligent simulations of adsorption and thermo-chemical transport processes at the pore scale
  • Interfacial phenomena in multiphase flow systems at the pore scale
Geofluids
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Acceptance rate57%
Submission to final decision62 days
Acceptance to publication25 days
CiteScore2.200
Journal Citation Indicator0.630
Impact Factor2.006
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.