The approach to estimating gas separation in a periodic operation mode of a well equipped by an electric submersible pump. Part 1
UDC: 622.276.53+622.276.57
DOI: -
Authors:
GORIDKO K.A.
1,
FEDOROV A.E.1,
KHABIBULLIN R.A.2,
VERBITSKY V.S.2,
IVANOV V.A.2,
KARTAVTSEVA I.A.3
1 RN-BashNIPIneft LLC, Ufa, Russia
2 National University of Oil and Gas "Gubkin University", Moscow, Russia
3 Gazpromneft-Khantos LLC, Khanty-Mansyisk, Russia
Keywords: natural separation of gas, electric submersible pump (ESP), low flow rate, modelling, periodic operation modes of ESP, short-term periodic mode
Annotation:
The use of electric submersible pumps (ESP) in oil wells provides the opportunity to bring bottomhole pressure down to target values that would help to achieve the required levels of oil production. When the pressure drops below the bubble point, gas comes out and multiphase flow is formed, also at the ESP intake. However, it is important to minimize the negative impact of free gas on ESP performance at the stage of well operation design by engineering calculations. The gas natural separation coefficient is an extremely important parameter in the process of determining the operational mode of a well, equipped by ESP.
Both literature review and field experience indicate the fact that there is currently no universal methodology for calculating natural gas separation at the ESP intake for short-term intermittent well operation. This research is intended to develop the method of estimating natural gas separation of wells, equipped by ESP operating in short-term periodic mode for planning wells operational mode and design of submersible pumping equipment.
The authors of the article describes a new engineering approach developed by combining mathematical models that take into account the periodic counter-current fluid and gas flow in the annulus of the well and the mechanistic correlation of the natural separation calculation by R. Marquez. The proposed method is realized as a computational module suitable for engineering calculations based on field data.
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