Permeability Prediction Via Multi Scale 3D CNN

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Deep Learning Permeability-Prediction-Via-Multi-Scale-3D-CNN
Overview

Permeability-Prediction-Via-Multi-Scale-3D-CNN

Data:

The raw CT rock cores are obtained from the Imperial Colloge portal.

The CT rock cores are sub-sampled into 150x150x150 sub-volumes with a variable stride as follow,

  • Bentheimer Sandstone: 50 voxles
  • Ketton Limestone: 50 voxles
  • Berea Sandstone: 25 voxles
  • Doddington Sandstone: 50 voxles
  • Estaillades Limestone: 50 voxles
  • Carbonate (C1): 50 voxles
  • Carbonate (C2): 50 voxles

The sub-volumes are simulated for absolute permeability using OpenFOAM and their results are summerized in the provided excel sheet having the following information,

  • Number of sub-samples = 65,248
  • Labels description:
    • casename = sub-sampling index per rock type sample
    • porosity = ratio of void fraction
    • eff_porosity = the connected porosity
    • rock_type = { 1:Bentheimer Sandstone, 2:Ketton Limestone, 3:Berea Sandstone, 4:Doddington Sandstone, 5:Estaillades Limestone, 6:Carbonate (C1), 7:Carbonate (C2) }
    • AR = anisotropy ratio
    • DOA = degree of anisotropy
    • k = absolute permeability
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