Group optimization and modeling methods of mechanized wells under intermittent operating conditions

UDK: 622.276.53.001

DOI: 10.24887/0028-2448-2025-8-69-73

Key words: intermittent operational mode, periodic mode, unsteady-state mode, electric submersible pump (ESP), group optimization, automatic re-closing mode, production enhancement

Authors: E.V. Yudin (Gazpromneft STC LLC, RF, Saint Petersburg); G.A. Piotrovskiy (NEDRA LLC, RF, Saint Petersburg); N.A. Smirnov (NEDRA LLC, RF, Saint Petersburg); M.A. Petrushin (OIL AND GAS PRODUCTION TOOLS LLC, RF, Moscow); S.M. Isaeva (NEDRA LLC, RF, Saint Petersburg); S.V. Zamakhov (NEDRA LLC, RF, Saint Petersburg); K.A. Popravko (Saint Petersburg State University, RF, Saint Petersburg); E.Y. Kherson (Saint Petersburg State University, RF, Saint Petersburg); N.A. Antipin (Ufa State Petroleum Technological University, RF, Ufa)

The article addresses the increasingly important issue of effectively managing low-rate wells equipped with electric submersible pumps (ESP), particularly at mature stages of reservoir exploitation. Intermittent operation is a cost-effective strategy to increase economic outcomes. However, successful implementation of this approach demands complex, robust algorithms capable of simultaneously optimizing multiple operational parameters. Specifically, optimal schedules should balance power consumption, maximize production efficiency, and minimize operational costs. Moreover, when multiple wells operate under periodic modes, synchronization of their respective work/idle cycles becomes critically important. Misalignment of these cycles can cause significant pressure fluctuations, negatively impacting the hydraulic stability and performance of the oil gathering network. This paper presents innovative computational methods designed explicitly for joint optimization of well clusters. The approach leverages advanced transient multiphase flow modeling techniques, effectively capturing the dynamic behavior of fluid flow under non-steady-state conditions. The developed algorithms efficiently address both individual well dynamics and inter-well interactions, significantly reducing computational complexity while preserving predictive accuracy. The proposed methodology was extensively validated through pilot implementations in oil fields of Western Siberia. Field tests demonstrated the effectiveness of the developed algorithms in improving decision-making processes and operational efficiency. Results clearly indicate improved economic outcomes, optimized resource management, and improved stability of the gathering network. Consequently, the presented methods exhibit high potential for broad industrial application, ensuring sustainable and economically viable reservoir management strategies.

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