## 2 Related Works  
The permeability of rocks is an important factor influencing the percentage of hydrocarbon recovery, reservoir management, and carbon dioxide sequestration during oil production. During in situ leaching of uranium, rock permeability is the most important factor in deciding whether to install downhole filters and predicting ore recovery. To solve the problems of permeability predicting, porosity, and other petrophysical properties of rocks in mining, regression models are often used [11], which allow developing more accurate and robust models than traditional empirical, statistical models [17]. Such models have been studied since the 1990s. For example, in Ref. [18], probably for the first time, a multilayer neural network was used to estimate the porosity of rocks. To assess the permeability of rocks, a hybrid algorithm using neural networks (ANNs) was proposed in [19]. An ANN-based regression analysis was used to obtain a set of relationships between the permeability, porosity, and pore size in [20]. ANNs as a non-linear regression method is used to estimate the porosity and permeability of an oil reservoir based on log data [21; 22]. A similar problem is considered in [23], where the authors compared ANN, SVM, and fuzzy neural network models. The authors concluded that ANNs can be used to estimate the permeability of a heterogamous carbonate reservoir based on three parameters: the bulk density, the neutron porosity, and a mobility index introduced by the authors with a mean square error of 0.28. In this paper, the authors also used ANNs to estimate the permeability of an oil-bearing reservoir in the Persian Gulf of Egypt and obtained very high values of the coefficient of determination \(R^{2}=96.5\%\). In Ref. [24], the porosity of oil reservoirs was studied using ANNs based on seismic sounding data. It was possible to study the petrophysical properties in the interwell space and identify zones of bypass sand channels and leaks that were not visible on the structural maps and attribute slices. The high result of the estimation of the porosity of the oil reservoir based on the SVM model was described in [25], where the correlations between the model estimates and real data exceeded 0.96. The authors of the paper stated that machine learning models worked more accurately than traditional estimation methods [26], reaching RMSE = 0.38 and \(R^{2}=97\%\) for the SVM-based model when processing log data from carbonate reservoirs of southwestern Iran. Well logs can be considered as a type of images that can be analyzed using deep learning models when there are enough data. For example, to estimate the permeability in the process of oil production in [27], convolutional neural networks (CNNs) were used, which showed an advantage over ANNs. The use of CNNs to predict the properties of subsurface rocks in the process of drilling wells in real time is discussed in [28]. The authors show that the model is able to distinguish between different rock types such as cemented sandstone, unconsolidated sands, and shales. A CNN-based model was proposed in [29] to accompany the technological process of well construction, namely, to assess the integrity of cement in cased wells. The deep learning model was used to identify the similarity of geological interlayers, and thus, with higher accuracy, allows estimating cross-well correlation [30]. New Zealand and Norway open datasets were used to tune the model. The accuracy of the model was 0.926, which was significantly higher than the base models based on gradient boosting. A similar problem of estimating the interwell space of an oil and gas reservoir is considered in [31], where a three-dimensional CNN is used, which shows better results compared with ANNs. As shown above, the use of machine learning methods is very popular in the assessment of permeability, porosity, and interwell space in oil fields, where such methods in many cases show good results. However, the situation is different for uranium deposits.