Тип публикации: статья из журнала
Год издания: 2022
Идентификатор DOI: 10.24887/0028-2448-2022-5-84-89
Ключевые слова: oil production, wells exploitation, production casing leaks, machine learning, failures prediction, production monitoring, добыча нефти, эксплуатация скважин, негерметичность эксплуатационной колонны (НЭК), машинное обучение, прогнозирование аварий, мониторинг разработки
Аннотация: В ПАО «НК «Роснефть» большое внимание уделяется применению современных методов обработки информации для повышения эффективности мониторинга разработки месторождений. Актуальным является получение экономического эффекта от продления срока службы или периода безаварийной работы сушествуюших скважин. Аварии и осложнения в работе скважПоказать полностьюин приводят к издержкам, которых можно избежать, применяя методы предиктивной аналитики и новые цифровые технологии. Результаты и подход, представленные в работе, могут быть использованы при создании интеллектуальных систем мониторинга разработки месторождений. A large amount of data is accumulating during the exploitation of oil wells. The data characterize the operating mode and properties of the extracted raw materials. It is not always use in a systematic and objective way, and not all possibilities for their application have been explored. The work is aimed at obtaining an understanding of the possible use of an array of such data to analyze the state of the well and predict the timing when an accident may occur. Relevant data were selected and a comparative analysis of operating parameters before failures and parameters in normal operating modes was carried out. There is a correlation between the characteristics of the well operating mode and the probability of failures (in particular, due to production casing lealks, etc.). The output results of machine learning algorithms for the separation of emergency and normal operating states were analyzed. It is shown how the trained algorithms work on the entire period of well operation (presented in the data and excluded from training). A typical picture of daily forecasts of production casing leaks type pre-emergency states on wells where such failures were occurred is very different from normal operating wells. There are a series of positive predictions over long intervals until a production casing leak is detected. The article proposes an evaluation of the results at different time intervals and a possible interpretation for use in production. Many of the other failures intersect or overlap each other, which makes it difficult to perform a multi-class separation and unambiguous conclusions about the effectiveness of their prediction. The presented results, at least in part, can clarify the issue of the probability and timing of failures and be used in the oil production monitoring. A large amount of data is accumulating during the exploitation of oil wells. The data characterize the operating mode and properties of the extracted raw materials. It is not always use in a systematic and objective way, and not all possibilities for their application have been explored. The work is aimed at obtaining an understanding of the possible use of an array of such data to analyze the state of the well and predict the timing when an accident may occur. Relevant data were selected and a comparative analysis of operating parameters before failures and parameters in normal operating modes was carried out. There is a correlation between the characteristics of the well operating mode and the probability of failures (in particular, due to production casing leaks, etc.). The output results of machine learning algorithms for the separation of emergency and normal operating states were analyzed. It is shown how the trained algorithms work on the entire period of well operation (presented in the data and excluded from training). A typical picture of daily forecasts of production casing leaks type pre-emergency states on wells where such failures were occurred is very different from normal operating wells. There are a series of positive predictions over long intervals until a production casing leak is detected. The article proposes an evaluation of the results at different time intervals and a possible interpretation for use in production. Many of the other failures intersect or overlap each other, which makes it difficult to perform a multi-class separation and unambiguous conclusions about the effectiveness of their prediction. The presented results, at least in part, can clarify the issue of the probability and timing of failures and be used in the oil production monitoring. © 2022.
Журнал: Нефтяное хозяйство
Выпуск журнала: № 5
Номера страниц: 84-89
ISSN журнала: 00282448
Место издания: Москва
Издатель: Нефтяная компания "Роснефть", ОАО "Зарубежнефть", Российский межотраслевой научно-технический комплекс "Нефтеотдача", Научно-техническое общество нефтяников и газовиков им. акад. И.М. Губкина, АНК "Башнефть", ПАО "Татнефть"