Development of integrated automation systems for monitoring and control of technological equipment in the oil and gas complex based on big data

Abstract

The modern oil and gas industry is faced with the need to improve the efficiency and reliability of technological equipment. The integration of automation with Big Data analysis is becoming an important tool for monitoring, diagnosing, and optimizing production processes. The purpose of the research is to develop an integrated automation system capable of collecting, analyzing and interpreting information about the condition of equipment based on big data processing, enabling timely monitoring and management decision-making. The development of the system is based on the use of modern big data technologies, including machine learning algorithms and real-time analytics. The research includes designing the system architecture, creating software for collecting data from sensors and IoT devices, as well as implementing visualization tools. To test the developed system, an experimental stand was used that simulates the operation of equipment in the oil and gas complex. The effectiveness and accuracy of the proposed methods were assessed by parameters such as data processing speed, accuracy of failure prediction, and adaptability of the system to changes. The resulting integrated automation system allows you to automatically collect data from a variety of sources, analyze them in real time, identify deviations in the operation of equipment and predict potential malfunctions. Testing demonstrated up to a 20% reduction in equipment downtime and a 35% increase in diagnostic accuracy compared to traditional approaches. The developed automation system offers industrial enterprises a tool to increase the efficiency of technological processes and minimize the risks associated with equipment failure. Future development of the system involves integration with cloud platforms and expansion of functionality, including real-time process management.