Modeling of Mechanized Wells Operating in Alternating Frequency Mode Considering Check Valve Leakage and Practical Application for Efficient Well Management

Abstract Effective management of mechanized wells is becoming increasingly complex, particularly in mature fields characterized by significant reservoir depletion. This challenge is especially prominent in Western Siberia where the majority of producing assets are in late stages of development. Thus, operators often rely on intermittent operation modes in order to maintain production of low-rate ESP wells. This mode typically involves alternating between active pumping cycles (with non-zero ESP frequency) and idle periods (with ESP frequency set to zero), making it a widely accepted strategy for balancing production and power efficiency. However, as field conditions become more unstable, the limitations of standard intermittent mode become more pronounced. One critical challenge is check valve leakage, which can significantly affect fluid flow during idle periods and demands new approach for operation – Alternating Frequency Mode – to compensate it. Accurate modeling of this leakage effect is essential for any meaningful optimization of ESP-operated wells in such regimes. Further investigation into this issue revealed an even more complex and often overlooked phenomenon – the Pump-As-Turbine (PAT) mode, where the ESP acts as a hydraulic turbine during shut-in cycles due to reverse flow. Although this effect deserves dedicated study, it is fundamentally linked to the leakage problem and must be considered as part of a comprehensive modeling strategy. This paper presents a physically driven extension of an existing ESP transient well simulation framework, incorporating both check valve leakage modeling and PAT-mode behavior under alternating frequency conditions. The extended model captures the transient interactions between wellbore hydraulics, pump mechanics, and surface oil gathering network, providing a detailed view of system behavior under non-stationary flow conditions. To demonstrate the robustness and applicability of our advanced modeling approach, extensive case studies and practical implementation results are provided. The upgraded simulation platform supports automated model adaptation algorithms based on operational data and enables solving complex optimization tasks for both individual wells and well clusters. Field-proven applications are presented, showing how these capabilities support high-resolution diagnostics, predictive analytics, and decision-making processes for production strategy optimization. These developments have led to measurable improvements in decision-making processes and substantial gains in operational efficiency and economic performance in the pilot field in Western Siberia.