The purpose of the present research is to provide a comprehensive analysis of data on the geological and physical properties of formations and the fluids saturating them in the Volga-Ural oil and gas province using the methods of geological and statistical model ranking. The discriminant analysis conducted on the basis of qualitative criteria (reservoir type and stratigraphic confinement) identified in all cases the zones of uncertainty, which affect the effectiveness of managerial decision-making in the conditions of analog objects. On this score, the results for six models were refined and updated according to the principle of rank uniqueness value calculation by three methods, both for each model individually and for model systems while using them within the obtained distributions of objects in the axes of canonical discriminant functions. Theoretical and practical recommendations were given regarding the use of geological and statistical models in the development of Volga-Ural oil and gas province fields. The results obtained can be used to solve a wide range of practical problems of proactive resource management, which enable effective determination of the best strategy for the successful extraction of residual and hard-to-recover oil reserves. The proposed parameter ranking table allows both to determine the most unstable parameters with a high degree of probability and to level the factor of heterogeneity and disequilibrium of field data. The conducted study established that identification of object association with a particular group in the axes of canonical discriminant functions leads to the formation of the zone of uncertainty. The latter increases the risks of making ineffective managerial decisions when developing different categories of subsoil users’ assets. Using the methods of ranking geological and statistical models, an algorithm for constructing a hierarchical system is proposed, which allows to expand the application field of the results of geological and statistical modeling in the oil and gas industry as well as to reduce the risk of nonrepresentative results.