The density of the well grid is one of the most important parameters that has a significant impact on the rate and amount of oil reserves extraction. As practice shows, existing approaches to the reliable assessment and identification of the degree of influence of this indicator on oil recovery do not allow us to adequately form an idea of the dynamics of reservoir processes and their role in making managerial decisions in conditions of uncertainty. Taking into account the decrease in the number of hydrodynamic and geophysical studies conducted in relation to the total number of wells in operation at fields that are being developed for a long time or just being put into development, the issue of predicting the oil recovery coefficient according to the list of geological and field parameters remains open and requires an urgent solution, including those related to the use of unique complex algorithms. The purpose of the work is to determine the relationship between the density of the well grid and the final coefficient of oil recovery in the development of fields in natural conditions using computer modeling systems. The research methodology consists of the use of existing scientific and methodological approaches to the processing of geological and commercial information in conditions of different densities in combination with the use of various advanced systems for identifying hidden patterns. Geological and statistical models have been defined for the oil deposits of the Famensk tier of the South Tatar arch of the Volga-Ural oil and gas province, allowing for the forecasting procedure for groups of objects identified during differentiation, subject to the dynamic density of geological and field information and various technical and economic indicators of field development. A comprehensive interpretation of the nature and degree of influence of geological and technological parameters on the amount of oil reserves production has been carried out. The need for preliminary differentiation of deposits based on identification with the subsequent use of the presented geological and statistical models to reduce the risks of making erroneous decisions during activities aimed at optimizing the development of analog facilities and rational extraction of residual oil reserves is reliably substantiated.