Calculation of the characteristics of rock samples based on their images using deep machine learning algorithms

Abstract

Porosity, absolute permeability and diffusion coefficient are important characteristics of the flow of fluids in the pore space of rocks, the determination of which is resource-intensive and time-consuming. With the development of deep machine learning methods over the past 3–4 years, artificial neural networks have begun to be actively used in determining the transport properties of the “liquid-porous medium” system and the geometric characteristics of the pore space of samples based on their images. This method allows you to quickly determine the desired properties with acceptable accuracy. Therefore, the question arises about the effectiveness and adequacy of deep machine learning methods for these purposes.
 This article provides a scientific review of open literature sources on the determination of absolute permeability, diffusion coefficient and porosity from images obtained by different scanning methods. We also used our own data, namely images for 4 carbonate samples, and presented the results of predicting the connected porosity of these samples based on their X-ray images using the convolutional neural network model we built.
 The review showed that images of rock samples obtained using various scanning methods make it possible to calculate their transport properties with high reliability in a significantly short time. This means that deep machine learning can be a good alternative tool for calculating the properties of rock samples based on their images. The model we built showed the predictive ability of the porosity of 3 carbonate samples with a reliability coefficient of 0.936–0.976.