title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²ÑÑÐ¾ÐºÐ¾ÑÐ°ÑÑÐ¾ÑÐ½ÑÑ Ð¿ÑÐ¾Ð¼ÑÑÐ»Ð¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ Ð½ÐµÑÑÑÐ½Ð¾Ð³Ð¾ Ð¸Ð½Ð¶Ð¸Ð½Ð¸ÑÐ¸Ð½Ð³Ð°
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: Application of intellectual methods of high-frequency data analysis for solving problems of oil engineering
Creators: name: ÐÑÐ´Ð¸Ð»Ð¾Ð², ÐÐ¸ÑÐ°Ð¸Ð»
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 ÑÐ´Ñ (Ð¼Ð°Ñ.) Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ
ÐÐµÑÑÑÐ½ÑÐµ Ð¸ Ð³Ð°Ð·Ð¾Ð²ÑÐµ ÑÐºÐ²Ð°Ð¶Ð¸Ð½Ñ
Ð´ÐµÑÐµÐºÑÐ¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹
Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑ
Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð¾Ð²Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð°
anomaly detection
autoencoder
monitoring system

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description: The thesis presents the results of studying intellectual methods of telemetry data analysis to search for deviations at wells equipped with ESPs. The tasks that were solved during the research: 1. Analysis of the main causes of accidents at wells equipped with ESPs. 2. Analysis of the influence of complicating factors on ESP operation. 3. Review the methods of detecting the anomalous behavior of the wells equipped with ESP installations on the basis of statistical approaches and machine-learning models. 4. Creation of a model for identification of anomalies on the basis of the developed algorithms. The algorithms based on machine learning that are able to cover the main reasons of ESP installations failures have been proposed. Algorithms considered in the work: Random Forest, Dynamic Time Warping, hierarchical clustering, autoen-coder, CUSUM, STL. The presented algorithms are combined into one model allowing detection of any deviation patterns in the wells equipped with ESPs by analyzing high-frequency telemetry data in a streaming mode.