Calculating porosity and permeability from synthetic micro‐ CT scan images based on a hybrid artificial intelligence

Nowadays, lattice Boltzmann is one of the standard and exact methods of simulation in micro‐CT images of rock. However, it has a high weakness in run time. Therefore, the effort in this article is to reach a comprehensive substitute method for permeability calculation with less run time than the lattice Boltzmann method. The other purposes are the automation of processing operations, preparation of images, and in the end, the calculation of porosity. The best way to achieve these outcomes is to use hybrid artificial intelligence. In this research work, comprehensive model architecture has been used to design a hybrid artificial intelligence to be able to calculate permeability and porosity in complex images. A thousand images were randomly generated with high complexity, which makes the model comprehensive and extensible, and image processing was applied. After that, the lattice Boltzmann method as the direct simulation was selected. Finally, the convolutional neural network and multilayer perceptron based on a new and comprehensive model were evaluated for the first time; the mean squared error resulting from the evaluation of training data is 0.01, and the test data is 0.03. Expert systems have been used as a subset of artificial intelligence for automated image processing and porosity calculation. In this way, problems related to the direct implementation of classical algorithms for image processing, models, and patterns related to machine learning and needing an expert were solved to an acceptable extent, and an error of less than 5% was achieved.