title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿Ð¾Ð´Ñ Ð¾Ð´Ð¾Ð² Ð´Ð»Ñ Ð¾Ð¿ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ°ÑÐµÐ³Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¾ÑÐºÐ»Ð¾Ð½ÐµÐ½Ð¸Ð¹ Ð² Ð´Ð¾Ð±ÑÑÐµ ÑÐ³Ð»ÐµÐ²Ð¾Ð´Ð¾ÑÐ¾Ð´Ð¾Ð²
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: Development of approaches for operational determination and classification of deviations in hydrocarbon production
Creators: name: ÐÑÐ´Ð¾, 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: 2022
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
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Ð¾ÑÐºÐ»Ð¾Ð½ÐµÐ½Ð¸Ñ Ð² Ð´Ð¾Ð±ÑÑÐµ
classification of deviations
deviations in production

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description: The topic of the final qualifying work: "Development of approaches for the operational definition and categorization of deviations in the production of hydrocarbons". The present work is devoted to the study of the possibility of using machine learning algorithms and search and classification algorithms performed in the Python environment to search and classify deviations in hydrocarbon production, to determine the overflow of a well based on indirect data. In the course of the study, the following tasks were solved: 1. The industrial classification of oil production deviations is considered. 2. The parameters by which production control is carried out are determined. 3. Work has been carried out to create algorithms for searching and classifying production deviations both for general reasons and for a separate detailed reason for production deviations. 4. Algorithms for determining and classifying deviations using machine learning methods have been created. The work was carried out jointly with Gazpromneft â STC. The Company provided data on deposits for different periods, including data on individual wells. Based on these materials, an analysis was carried out for the presence of deviations using both simple search algorithms and algorithms based on machine learning. The results of the analysis allowed us to confirm the assumptions about the possibility of determining and classifying deviations in oil production by various search algorithms. The resulting products can be used in production to optimize work.