Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media-Reference-Cited by-同舟云学术

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Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media

Published:2024-06 Issue: Volume:188 Page:105599
ISSN:0098-3004
Container-title:Computers & Geosciences
language:en
Short-container-title:Computers & Geosciences

Author:

Pawar Nikhil M.^ORCID,Soltanmohammadi Ramin^ORCID,Faroughi Shirko^ORCID,Faroughi Salah A.^ORCID

Funder

US Department of Energy Office of Environmental Management

US Department of Energy Office of Fossil Energy and Carbon Management

Publisher

Elsevier BV

Reference88 articles.

1. Determining Effective Permeability at Reservoir Scale: Numerical Simulations and Critical Path Analysis;Adeyemi,2021

2. Proposing a modified mechanism for determination of hydrocarbons dynamic viscosity, using artificial neural network;Ahmadi;Petrol. Sci. Technol.,2020

3. Temperature nanotracers for fractured reservoirs characterization;Alaskar;J. Pet. Sci. Eng.,2015

4. Data normalization and standardization: A technical report;Ali;Mach. Learn. Tech. Rep.,2014

5. Comparison of linear and nonlinear kriging methods for characterization and interpolation of soil data;Asa;J. Comput. Civ. Eng.,2012

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-driven methods for flow and transport in porous media: A review;International Journal of Heat and Mass Transfer;2024-12

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