Permeability prediction from thin sections: 3D reconstruction and Lattice‐Boltzmann flow simulation

By Y. Keehm, Tapan Mukerji, A. Nur. 2004

152 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/1d1669a6dc24437188a7b1f7cf3e73d3e10dac6e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Joanne T. Fredrich
2. Claudio Madonna
3. Joshua A. White
4. Erik H. Saenger
5. Bjarne S. G. Almqvist
6. Ronaldo I. Borja

In Computer Vision and Pattern Recognition

1. Joanne T. Fredrich
2. Claudio Madonna
3. Joshua A. White
4. Erik H. Saenger
5. Bjarne S. G. Almqvist
6. Ronaldo I. Borja

In Ocean Engineering

1. Claudio Madonna
2. Bjarne S. G. Almqvist
3. Joanne T. Fredrich
4. Joshua A. White
5. Erik H. Saenger
6. Ronaldo I. Borja
7. Thomas Bohlen

Citations in the corpus

• Calculating the effective permeability of sandstone with multiscale lattice Boltzmann/finite element simulations (2006, methodology): “The lattice Boltzmann technique is uniquely suited to modeling the complexities of fluid flow through disordered media, including flow through porous geomaterials [2, 13, 14, 16, 18, 19, 21].”
• Digital rock physics: numerical prediction of pressure-dependent ultrasonic velocities using micro-CT imaging (2012, methodology): “These techniques allow for calculation of transport properties (Arns et al. 2001; Keehm et al. 2004; Harting et al. 2005; Knackstedt & Arns 2005) and effective mechanical properties (Arns et al. 2002; Saenger et al. 2004; Madadi et al. 2009).”