title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ñ Ð½Ð° Ð¿ÑÐ¸ÐµÐ¼Ðµ Ð´Ð»Ñ ÑÐºÐ²Ð°Ð¶Ð¸Ð½, Ð¾ÑÐ½Ð°ÑÐµÐ½Ð½ÑÑ Ð£Ð­Ð¦Ð, Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Prediction of pressure at the intake of ESP using machine learning methods
Creators: name: ÐÐ°ÑÑÑÑÐ½Ð¾Ð²Ð°, ÐÐ»ÐµÐ½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÐÐµÑÑÑÐ½ÑÐµ Ð¸ Ð³Ð°Ð·Ð¾Ð²ÑÐµ ÑÐºÐ²Ð°Ð¶Ð¸Ð½Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
Ð³Ð¸Ð±ÑÐ¸Ð´Ð½ÑÐ¹ Ð¿Ð¾Ð´Ñ Ð¾Ð´
ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð½Ð° Ð¿ÑÐ¸ÐµÐ¼Ðµ
regression
gradient boosting
hybrid approach
physical modeling
intake pressure

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description: The paper presents the results of creating a system for predicting the pressure at the ESP intake using machine learning methods along with physical modeling. The analysis of available solutions and approaches to create a virtual sensor has been carried out. The architecture of the solution was compiled, including filtering, combining static and dynamic data, building a well model to enrich the feature space. The use of a hybrid approach based on the combination of calculations of the physical model of the well and the regression model of gradient boosting on decision trees is substantiated. The predictive ability of the model was tested on field data from one of the fields in Western Siberia. The developed solution is potentially capable of increasing the quality of control over the development of oil fields with a reduction in oil losses and an increase in the efficiency of submersible equipment.