РЕШЕНИЕ ЗАДАЧИ КЛАССИФИКАЦИИ ЭКСПЛУАТАЦИОННОГО ФОНДА СКВАЖИН И ДИФФЕРЕНЦИАЦИИ ОСТАТОЧНЫХ ЗАПАСОВ В КАРБОНАТНЫХ ПЛАСТАХ НА ПРИМЕРЕ ТУРНЕЙСКИХ ЗАЛЕЖЕЙ НОВО-ЕЛХОВСКОГО МЕСТОРОЖДЕНИЯ

Abstract

Background The article covers the results of generalization and comprehensive analysis of the current state of operating wells and the structure of remaining reserves in carbonate reservoirs of Tournasian stage in Novo-Elkhovskoye field using statistical methods of data processing - the method of artificial neural networks (ANN) and the method of principal components (MPC). Studies are relevant at the stage of making decision with regard to optimization of the development system and justification of the complex geological and technical activities (GTA) with the aim to improve the system of stimulation and involvement in the active development of residual oil. Aims and Objectives The main objective of the study is classification of operating wells by their geological, physical and field parameters with the subsequent identification of the groups of wells in relation to the areas with high residual reserves, so that to rationale GTA in areas with high residual reserves not yet stimulated. Methods Set objectives of the study are implemented on the basis of generalization, systematization and complex analysis of the large array of geological and field information with the use of multivariate analysis methods. The accuracy of the methods was determined using a comparative analysis of the classification of objects (wells) by ANN method and the method of principal components. Results Classification was developed and identification of operating well stock by the most important geologic and field parameters was performed on the basis of compilation and analysis of geological and field information related to the carbonate deposits of Tournasian stage in Novo-Elkhovskoye field. The results of classification by ANN method and the method of principal components were qualitatively assessed. Residual reserves were differentiated by layers, zones and deposits. In view of the developed classification, promising ways to improve the efficiency of oil recovery from oil deposits in carbonate reservoirs were determined.