Approach to adaptive management of well stock with electric vane pump installation

UDK: 621.651/.69:004

DOI: 10.24887/0028-2448-2025-7-130-134

Key words: digitalization, adaptive control, mechanized well stock, complicating factors, predictive analytics

Authors: M.I. Kuzmin (Empress Catherine II Saint Petersburg Mining University, RF, Saint Petersburg); A.I. Ponomareva (Empress Catherine II Saint Petersburg Mining University, RF, Saint Petersburg); R.V. Gerasimov (Gubkin University, RF, Moscow)

There is a current need to change approaches to managing the oil production process developed over more than half a century. The article proposes a concept for adaptive management of oil well stock equipped with electric vane pumps considering the high probability of operational complications. The core of the concept lies in the integration of predictive analytics, Internet of Things (IoT) technologies, and real time analysis of field data. A detailed analysis of equipment failure cases at a Western Siberia oilfield for the period 2018–2020 was conducted, identifying key parameters preceding malfunctions, including fluctuations in electric current, temperature, and pressure. Based on these findings, an adaptive control algorithm was developed to dynamically adjust pump operation modes in accordance with the predicted risk of complications. This approach improves the operational reliability of the artificial lift system and enables a reduction in operating expenses by 25 % and a decrease in downtime by 30 %. Special attention is paid to the verification of sensor data, the construction of digital twins, and the implementation of feedback mechanisms for automatic system response. The proposed methodology contributes to reducing unplanned failures and supports decision-making under uncertainty. Thus, the presented approach highlights the potential of digital transformation in oilfield operations management and facilitates the transition toward intelligent decision support systems within the framework of the smart oilfield concept.

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