Modeling of the oil recovery process for managing the development of deposits using artificial intelligence

Based on a comprehensive study of the process of oil recovery from deposits in carbonate reservoirs of the Tournaisian tier of the Birskaya saddle, a forecast of the final coefficient of oil recovery for deposits that have been in development for a long time was carried out. The choice of the research object is due to the presence of a significant proportion of residual oil reserves, which can potentially be extracted as part of the implementation of an effective and sound asset management strategy. Previously, more than 60 objects were divided into four groups using one of the well-known methods of reducing the dimensionality of the problem ‒ the method of principal components, after which typical dependencies were established that make it possible to successfully group objects. Using statistical forecasting methods, models of the process of developing oil reserves were obtained differentially for various groups of selected objects, including a number of different parameters reflecting, among other things, geological heterogeneity. Based on the results of the interpretation of the obtained dependencies, the areas of their use in the plane of application of artificial intelligence systems in the development of deposits in the natural regime are established. Within the framework of determining the relationships between various parameters and evaluating the reliability of the obtained models, the need for object identification in solving a wide range of tasks to improve the efficiency of operation of both objects involved in the analysis and similar ones in terms of geological and commercial characteristics has been established. Keywords: oil recovery coefficient; oil field development; computer modeling; carbonate reservoirs of the Tournaisian tier of the Birskaya saddle; identification of objects.