Стремительное развитие технологий искусственного интеллекта и машинного обучения открывает новые возможности для решения задач промышленной безопасности в нефтегазовом секторе. Учитывая высокий уровень риска аварийных ситуаций на объектах отрасли, использование нейросетевых алгоритмов представляется перспективным для повышения эффективности прогнозирования и предотвращения критических ситуаций. В исследовании рассмотрены возможности применения нейронных сетей для анализа данных, поступающих с датчиков промышленного оборудования, установленных на объектах нефтегазовой инфраструктуры. Основное внимание уделяется разработке модели глубокого обучения с использованием рекуррентных и сверточных нейронных сетей. Алгоритмы были обучены на больших объемах исторических данных, включая параметры оборудования, временные ряды и отчеты о предыдущих инцидентах. Для оценки эффективности моделей использовались такие метрики, как точность, полнота и f-мера. Разработанные алгоритмы продемонстрировали высокую способность к раннему выявлению отклонений в работе оборудования, что позволяет предсказывать потенциальные аварии с точностью более 90%. Кроме того, анализ временных рядов показал, что внедрение предложенной системы в реальных условиях сокращает время реакции на инциденты и уменьшает вероятность крупных аварий на 25%. Дополнительно было выявлено, что использование автономных систем прогнозирования повышает экономическую эффективность благодаря снижению затрат на ремонты и незапланированные простои. Применение нейросетевых алгоритмов в автоматизированных системах прогнозирования и предотвращения аварий на объектах нефтегазовой отрасли демонстрирует высокий потенциал в повышении безопасности и оперативности реагирования. Перспективы дальнейших исследований связаны с интеграцией различных источников данных, а также созданием комплексных моделей, способных учитывать не только технические, но и внешние факторы.
The rapid development of artificial intelligence and machine learning technologies opens up new opportunities for solving industrial safety problems in the oil and gas sector. Given the high risk of accidents at industry facilities, the use of neural network algorithms seems promising to increase the effectiveness of forecasting and preventing critical situations. The study examines the possibilities of using neural networks to analyze data coming from sensors of industrial equipment installed at oil and gas infrastructure facilities. The main focus is on developing a deep learning model using recurrent and convolutional neural networks. The algorithms were trained on large amounts of historical data, including equipment parameters, time series, and reports on previous incidents. Metrics such as accuracy, completeness, and f-measure were used to evaluate the effectiveness of the models. The developed algorithms have demonstrated a high ability to detect equipment malfunctions early, which makes it possible to predict potential accidents with an accuracy of more than 90%. In addition, time series analysis has shown that implementing the proposed system in real-world conditions reduces incident response time and reduces the likelihood of major accidents by 25%. Additionally, it was revealed that the use of autonomous forecasting systems increases economic efficiency by reducing the cost of repairs and unplanned downtime. The use of neural network algorithms in automated systems for predicting and preventing accidents at oil and gas facilities demonstrates a high potential for improving safety and responsiveness. The prospects for further research are related to the integration of various data sources, as well as the creation of complex models that can take into account not only technical, but also external factors.
The rapid development of artificial intelligence and machine learning technologies opens up new opportunities for solving industrial safety problems in the oil and gas sector. Given the high risk of accidents at industry facilities, the use of neural network algorithms seems promising to increase the effectiveness of forecasting and preventing critical situations. The study examines the possibilities of using neural networks to analyze data coming from sensors of industrial equipment installed at oil and gas infrastructure facilities. The main focus is on developing a deep learning model using recurrent and convolutional neural networks. The algorithms were trained on large amounts of historical data, including equipment parameters, time series, and reports on previous incidents. Metrics such as accuracy, completeness, and f-measure were used to evaluate the effectiveness of the models. The developed algorithms have demonstrated a high ability to detect equipment malfunctions early, which makes it possible to predict potential accidents with an accuracy of more than 90%. In addition, time series analysis has shown that implementing the proposed system in real-world conditions reduces incident response time and reduces the likelihood of major accidents by 25%. Additionally, it was revealed that the use of autonomous forecasting systems increases economic efficiency by reducing the cost of repairs and unplanned downtime. The use of neural network algorithms in automated systems for predicting and preventing accidents at oil and gas facilities demonstrates a high potential for improving safety and responsiveness. The prospects for further research are related to the integration of various data sources, as well as the creation of complex models that can take into account not only technical, but also external factors.