Развитие нефтегазового комплекса требует создания эффективных инструментов управления технологическими процессами в условиях растущей сложности производственных объектов. Внедрение больших данных открывает новые возможности для повышения точности и оперативности управления за счет обработки больших объемов информации в реальном времени. Целью настоящей работы является разработка интегрированной системы управления технологическими процессами на основе анализа и использования методов работы с большими данными. В работе применен подход, основанный на использовании технологий машинного обучения, искусственного интеллекта и аналитики больших данных. Были исследованы методы сбора, обработки и анализа данных с различных этапов производственного цикла нефтегазового комплекса. Для построения систем управления использовалась архитектура IIoT (Интернет вещей для промышленности) в сочетании с платформами облачных вычислений и инструментами для прогнозной аналитики. Создана прототипная система управления технологическими процессами, которая включает модули для автоматизации сбора данных, реализации алгоритмов предиктивного анализа и визуализации информации для принятия оперативных решений. Предложенный подход позволил улучшить ключевые показатели эффективности, включая снижение времени реакции на аварийные ситуации до 30% и повышение эффективности планирования до 25%. Также продемонстрирована возможность интеграции системы с существующими платформами автоматизированного управления. Работа показала высокий потенциал использования больших данных для оптимизации технологических процессов в нефтегазовой отрасли. Результаты исследований могут быть применены для повышения производительности, снижения производственных рисков и затрат, а также для обеспечения устойчивого развития комплекса. Для дальнейшего совершенствования предложенного подхода планируется проведение дополнительных тестов на реальных объектах и разработка моделей для новых направлений, таких как прогнозирование экологических рисков.
The development of the oil and gas complex requires the creation of effective process management tools in the context of the growing complexity of production facilities. The introduction of big data opens up new opportunities for improving the accuracy and efficiency of management by processing large amounts of information in real time. The purpose of this work is to develop an integrated process management system based on the analysis and use of methods of working with big data. The paper uses an approach based on the use of machine learning technologies, artificial intelligence and big data analytics. Methods of data collection, processing and analysis from various stages of the oil and gas complex production cycle were investigated. The IIoT (Internet of Things for Industry) architecture was used to build control systems in combination with cloud computing platforms and predictive analytics tools. A prototype process management system has been created, which includes modules for automating data collection, implementing predictive analysis algorithms, and visualizing information for making operational decisions. The proposed approach has improved key performance indicators, including reducing emergency response time by up to 30% and increasing planning efficiency by up to 25%. The possibility of integrating the system with existing automated control platforms is also demonstrated. The work has shown the high potential of using big data to optimize technological processes in the oil and gas industry. The research results can be applied to increase productivity, reduce production risks and costs, as well as to ensure the sustainable development of the complex. To further improve the proposed approach, it is planned to conduct additional tests on real objects and develop models for new areas, such as forecasting environmental risks.
The development of the oil and gas complex requires the creation of effective process management tools in the context of the growing complexity of production facilities. The introduction of big data opens up new opportunities for improving the accuracy and efficiency of management by processing large amounts of information in real time. The purpose of this work is to develop an integrated process management system based on the analysis and use of methods of working with big data. The paper uses an approach based on the use of machine learning technologies, artificial intelligence and big data analytics. Methods of data collection, processing and analysis from various stages of the oil and gas complex production cycle were investigated. The IIoT (Internet of Things for Industry) architecture was used to build control systems in combination with cloud computing platforms and predictive analytics tools. A prototype process management system has been created, which includes modules for automating data collection, implementing predictive analysis algorithms, and visualizing information for making operational decisions. The proposed approach has improved key performance indicators, including reducing emergency response time by up to 30% and increasing planning efficiency by up to 25%. The possibility of integrating the system with existing automated control platforms is also demonstrated. The work has shown the high potential of using big data to optimize technological processes in the oil and gas industry. The research results can be applied to increase productivity, reduce production risks and costs, as well as to ensure the sustainable development of the complex. To further improve the proposed approach, it is planned to conduct additional tests on real objects and develop models for new areas, such as forecasting environmental risks.