The main goal of the work is to review and describe the algorithms currently being developed in order to obtain reliable well operation parameters using machine learning (ML) methods, existing constraints and results achieved. The work covers a number of ML applications. The methodology of bottomhole pressure modeling is described, which, in the absence or in case of failure of pressure sensors at an ESP inlet, allows more reliably, in comparison with a conventional approach, to determine the bottomhole pressure in wells and thereby improve the efficiency of selecting well candidates for well interventions. To assess well flow rates, another key indicator, an ESP instant flow ate simulator has been developed that allows obtaining information about the instantaneous production rate at any time, thus increasing the accuracy of cyclic wells measurements, and promptly implementing the necessary corrective measures. Approaches to modeling the impact of injection on the production well rates have also been considered.

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