Digital core: study of textural heterogeneities in a rock

Relevance. When real fluid flows through channels of limited dimensions, it experiences a constant loss of mechanical energy (hydraulic resistance) due to the action of viscous friction. Hydraulic drag has two components: drag along the length of the flow and local drag. The drag along the length of the flow arises because the fluid encounters a force per unit area perpendicular to the direction of the flow. Local drag results from the viscous interaction between the fluid and the channel walls. Local hydraulic resistances include sudden and gradual constrictions/expansions (diffusers/confusions) of the fluid channel, angular and choke gaps. The value of the hydraulic resistance coefficient depends on the type of channel geometry, conditions of fluid entry (wettability of the rock texture) and on the flow regime. When fluid flows through channels of different cross-section, as the channel narrows, the flow velocity increases and the pressure, based on Bernoulli's equation, decreases. Objects. Polymictic sandstones of the Tyumen Formation. Methods. The developed algorithms for identifying areas of contraction/expansion (confusers/diffusers) in the rock texture (polymictic sandstone) are based on neural network reconstruction of core data. The study of geometric diversity of geological rock images is based on generalization of real spatial forms in combination with the axiom of hyperplanes. A methodical approach for finding distribution of inertial exciters (confusers/diffusers) affecting the character of multiphase flow front changes in the rock texture is based on the metrics for estimating possible distributions. Results. The paper introduces the initial stages of core digital transformation data. It was determined that the main problem of geological data digital projection on a large scale is the need to take into account the systems of inertial effects, both in the available volume of the rock and in the extrapolated space of it. In order to adequately represent the character of multiphase fluid flow for different scales, the authors have developed the algorithms for segmentation of geological images of fluid space with further identification of distribution laws of inertial exciters (confusers/diffusers) in the rock texture. For training and testing of the developed algorithms we used the computer tomography images of core material and detailed resolution images of rock sections. The source code of neural network algorithms was written in the Python pr