Современные нефтегазовые компании сталкиваются с необходимостью повышения эффективности добычи углеводородов, особенно в условиях сложных геологических и технологических факторов. Одной из перспективных областей оптимизации является интеграция алгоритмов машинного обучения (ML) в автоматизированные системы управления (АСУ). Настоящее исследование посвящено изучению возможностей применения таких систем для оптимизации процессов добычи нефти и газа. Работа основана на комплексном анализе существующих подходов к интеграции ML-алгоритмов в АСУ и разработке модели, адаптированной к требованиям добычи углеводородов в сложных условиях. Основой исследования стало применение методов обработки больших данных (Big Data), алгоритмов прогнозирования на основе нейронных сетей и механизма обратной связи для автоматического регулирования параметров процесса добычи. Были проведены лабораторные и полевые испытания предложенной системы на реальных данных из нефтегазовых месторождений. Разработанная интегрированная АСУ продемонстрировала значительное улучшение параметров добычи – повышение коэффициента извлечения нефти на 15% и снижение расхода энергоресурсов на 10%. Алгоритмы ML позволили на ранних этапах прогнозировать сбои в работе оборудования и выявлять снижение производительности. Внедрение системы также позволило сократить влияние человеческого фактора и минимизировать экологические риски за счет более точной регулировки добывающих процессов. Полученные результаты показывают, что использование ML в АСУ открывает новые возможности для оптимизации добычи нефти и газа в сложных геологических условиях. Рекомендуется дальнейшее развитие технологии, включая разработку специализированных алгоритмов для работы с различными геологическими сценариями и масштабирование системы на новые месторождения.
Modern oil and gas companies face the need to increase the efficiency of hydrocarbon production, especially in conditions of complex geological and technological factors. One of the promising areas of optimization is the integration of machine learning (ML) algorithms into automated control systems (ACS). The present study is devoted to exploring the possibilities of using such systems to optimize oil and gas production processes. The work is based on a comprehensive analysis of existing approaches to integrating ML algorithms into automated control systems and developing a model adapted to the requirements of hydrocarbon production in difficult conditions. The research was based on the use of big Data processing methods, neural network-based forecasting algorithms, and a feedback mechanism for automatically regulating the parameters of the mining process. Laboratory and field tests of the proposed system were carried out on real data from oil and gas fields. The developed integrated control system demonstrated a significant improvement in production parameters – an increase in the oil recovery coefficient by 15% and a reduction in energy consumption by 10%. ML algorithms made it possible to predict equipment failures at an early stage and detect a decrease in performance. The implementation of the system also made it possible to reduce the impact of the human factor and minimize environmental risks by more precisely adjusting the extraction processes. The results show that the use of ML in automated control systems opens up new opportunities for optimizing oil and gas production in difficult geological conditions. Further development of the technology is recommended, including the development of specialized algorithms for working with various geological scenarios and scaling the system to new deposits.
Modern oil and gas companies face the need to increase the efficiency of hydrocarbon production, especially in conditions of complex geological and technological factors. One of the promising areas of optimization is the integration of machine learning (ML) algorithms into automated control systems (ACS). The present study is devoted to exploring the possibilities of using such systems to optimize oil and gas production processes. The work is based on a comprehensive analysis of existing approaches to integrating ML algorithms into automated control systems and developing a model adapted to the requirements of hydrocarbon production in difficult conditions. The research was based on the use of big Data processing methods, neural network-based forecasting algorithms, and a feedback mechanism for automatically regulating the parameters of the mining process. Laboratory and field tests of the proposed system were carried out on real data from oil and gas fields. The developed integrated control system demonstrated a significant improvement in production parameters – an increase in the oil recovery coefficient by 15% and a reduction in energy consumption by 10%. ML algorithms made it possible to predict equipment failures at an early stage and detect a decrease in performance. The implementation of the system also made it possible to reduce the impact of the human factor and minimize environmental risks by more precisely adjusting the extraction processes. The results show that the use of ML in automated control systems opens up new opportunities for optimizing oil and gas production in difficult geological conditions. Further development of the technology is recommended, including the development of specialized algorithms for working with various geological scenarios and scaling the system to new deposits.