AbstractConvolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($$\varphi$$
φ
), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with $$\varphi \in (0.37,0.99)$$
φ
∈
(
0.37
,
0.99
)
which covers five orders of magnitude a span for permeability $$k \in (0.78, 2.1\times 10^5)$$
k
∈
(
0.78
,
2.1
×
10
5
)
and tortuosity $$T \in (1.03,2.74)$$
T
∈
(
1.03
,
2.74
)
. It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and $$\varphi$$
φ
has been obtained and compared with the empirical estimate.
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