On the need to apply a differentiated approach to the creation of a scientific and methodological base for the use of artificial intelligence in solving development tasks

Based on the study of the process of restoring well productivity after drilling in the conditions of deposits in the carbonate reservoirs of the Volga-Ural oil and gas province, the need for deep differentiation of deposits within various tectonic and stratigraphic elements has been established, which allows more accurately solving development tasks and reducing the risks of making erroneous decisions. Models are proposed that make it possible to differentially assess the degree of contamination, the time of cleaning the bottom-hole zone of the formation, according to different groups of selected objects at the stage of field commissioning, as well as to predict the real productivity of wells that best reflects the real properties of the formation at the point of its opening. An algorithm is proposed for evaluating the effectiveness of primary and secondary reservoir opening and impact on the bottom-hole zone during well development in a differentiated manner according to various groups of objects identified on the basis of deep identification of objects. The obtained results make it possible, in conditions of a limited volume of hydrodynamic and field studies, to form a relevant strategy related to increasing the efficiency of the selection of residual oil reserves. The role of the differentiated approach in this case is significant and relevant in connection with the need to develop multi-layered and complex deposits, the development of which is carried out for quite a long time. With the help of the obtained models, it is possible to evaluate the effectiveness of the implementation of measures for the treatment of the bottom-hole zone of the formation, in particular, hydrochloric acid exposure. The relevance of their use in this case is not regulated by the geological and physical characteristics of productive formations, which expands the range of tasks solved using methods of geological and statistical modeling and pattern recognition. Keywords: well productivity; time for cleaning the bottom-hole formation zone; computer modeling; well development using hydrochloric acid treatments; deep identification of deposits.