Research of methods for optimizing automation of technological processes in the oil and gas complex using artificial intelligence

Abstract

The development of the oil and gas complex requires the introduction of modern digital technologies to increase efficiency and reduce production costs. The use of artificial intelligence (AI) methods is becoming a key tool in automating technological processes. The purpose of this research is to develop and analyze methods for optimizing the automation of production processes in the oil and gas industry using AI. In the course of the work, machine learning methods, neural networks and deep learning algorithms were used to build forecasting and management systems. Particular attention is paid to hybrid approaches combining adaptive algorithms with traditional automation models. The study also included the analysis of operational data and modeling of production processes to find optimal solutions. The analysis demonstrated that the use of AI makes it possible to improve forecasting accuracy, reduce operational risks, and optimize equipment maintenance costs. The creation of intelligent control systems has made it possible to quickly adjust technological processes in real time. The application of the proposed methods in pilot installations has shown an increase in efficiency of up to 20% compared with traditional automation approaches. The main advantage of implementing AI is the ability to analyze large amounts of data and predictive process management. The study showed that promising areas are the development of self-learning systems and scaling solutions at all stages of the production chain. At the same time, the main challenge remains the need to adapt AI to the complex and dynamic conditions of the oil and gas environment. The introduction of artificial intelligence methods into the automation of technological processes in the oil and gas complex provides a significant increase in productivity and cost reduction. Further research should be aimed at improving hybrid models and their industrial implementation in large-scale industry projects.