Application of multi-agent technologies for adaptive management of distributed oil and gas complex networks in real time

Abstract

The purpose of this article is to study the application of multi-agent technologies for adaptive management of distributed networks in the oil and gas complex in real time. Management of complex distributed systems, such as oil and gas complex networks, requires a high degree of automation and adaptation to changing conditions. Traditional management approaches exhibit a number of limitations, such as low flexibility and insufficient responsiveness to changes, which makes them less effective in modern conditions. The study considers a multi-agent approach based on the interaction of multiple autonomous software agents, each of which performs separate tasks related to monitoring, analysis, forecasting and decision-making. The multi-agent system model was formed using modern artificial intelligence tools and machine learning algorithms. To verify the developed system, numerical experiments were carried out simulating various scenarios in conditions of changing demand, emergency situations and external influences. The results obtained demonstrate that the use of multi-agent technologies significantly improves the management efficiency of distributed networks in the oil and gas complex. The developed system has shown the ability to quickly adapt to changes in real time, minimize possible risks and increase network reliability. The system's rapid response to external influences, as well as its high flexibility in decision-making, have been confirmed experimentally. The use of multi-agent technologies opens up new opportunities to ensure the reliability and stability of distributed networks in the oil and gas industry. This approach represents a promising direction for the further development of adaptive management systems in the oil and gas industry in the context of the increasing complexity and dynamism of modern systems.