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The purpose of the article is to present an algorithm developed on the scientific and methodological foundations of quantitative and qualitative processing of geological and commercial data to implement the procedure for deep identification of deposits. The developed algorithm consists of two levels: the initial stage includes facility identification by tectonic and stratigraphic characteristics resulting in the formation of a number of megagroups of objects. Then they are subjected to deep differentiation using the elements of data factor analysis, which is carried out together with the monitoring of the highly identical objects. The presented approach to solving the problems of field effective grouping is the most effective due to a comprehensive and reasonable assessment of the groups of facilities formed as a result of modeling. The developed algorithm was tested on example of a number of fields associated with terrigenous reservoirs of the Devonian and carboniferous systems of the Volga-Ural oil and gas province. After two calculation stages the percentage of correctly grouped objects averaged 96.8 %, which is a high result. To make the search for analogous objects qualitative and objective eighteen equations have been obtained that combine twenty parameters describing the geological and physical characteristics of productive formations as well as the physico-chemical properties of the fluids saturating them at the sufficient level of reliability. Based on the results of using the developed algorithm for deep identification of deposits, the authors obtained a number of relevant mathematical dependencies between various parameters, graphical distributions of objects in the axes of the main components, which all together enable efficient and systematic search for analogous objects in the deposits of terrigenous reservoirs of the Devonian and carboniferous systems of the Volga-Ural oil and gas province. Besides, the presented identification diagrams enable successful management of the processes of oil recovery within the micro- and macro-levels of facility distribution in the axes of the main components. They also allow to form a list of general recommendations that will contribute to the optimal development of liquid hydrocarbon resources.
Gilyazetdinov et al. (Sun,) studied this question.