Creation of intelligent monitoring and control systems for equipment in the oil and gas industry to improve production efficiency and safety

Abstract

Modern oil and gas industry enterprises face the need to increase efficiency, reduce costs and ensure safe working conditions. However, traditional methods of monitoring equipment often turn out to be insufficiently accurate and reactive, which can lead to emergencies, reduced productivity and economic losses. This work is devoted to the development of intelligent monitoring and control systems for equipment based on modern data processing and artificial intelligence technologies. As part of the research, an algorithm was developed that includes machine learning and real-time data collection systems. Various types of sensors were used to measure equipment operation parameters such as temperature, vibration, pressure, and flow rate. The system analyzed the data obtained using predictive analytics methods, allowing it to predict possible malfunctions. The proposed architecture is integrated with cloud platforms for remote management and process optimization. The experimental results showed that the introduction of an intelligent monitoring system reduced equipment downtime by 25%, as well as reduced the number of emergencies by 40%. Additionally, the system provided more accurate forecasting of equipment wear, which allowed for improved maintenance planning. Individual enterprises have managed to reduce maintenance costs by 15%. These results confirm the high efficiency of the proposed methodology. The proposed equipment monitoring and control system demonstrates significant potential in improving the safety and economic efficiency of oil and gas industry enterprises. The developed technologies can be adapted for use in various industrial sectors. In the future, further work is expected to integrate with Internet of Things (IoT) systems and expand functionality.