ARTIFICIAL INTELLIGENCE AS A DRIVER OF INNOVATIVE TRANSFORMATIONS IN THE OIL AND GAS INDUSTRY

Abstract

The purpose of this article is to provide a comprehensive justification of the role of artificial intelligence as a key driver of innovative transformations in the oil and gas industry at the global and national levels, with a focus on the impact of AI on the operational efficiency, environmental performance, and industrial safety systems of energy companies. The paper systematically explores the nature of the digital transformation of the oil and gas sector amid tightening environmental regulations, escalating geopolitical risks, the destruction of energy infrastructure due to the war in Ukraine, and a shortage of skilled personnel. It is shown that under such conditions, artificial intelligence ceases to be an optional tool for targeted optimization and becomes a fundamental element of new business models focused on data-driven management of all stages of the value chain-from geological exploration and reservoir modeling to production, transportation, processing, trading, and maintenance. The methodological framework of the study comprises methods of analysis and synthesis, systemic and structural-functional approaches, as well as comparative analysis, which enabled a comparison of practices among leading international companies. The use of the case study method allowed for an in-depth exploration of the specifics of creating and scaling AI solutions for predictive equipment maintenance, intelligent support for engineers, monitoring of greenhouse gas emissions, automation of drilling operations, optimization of energy consumption, and industrial safety risk management. A synthesis of empirical data from corporate reporting and industry analytical studies showed that the implementation of AI systems provides a significant reduction in downtime, lower maintenance costs, increased asset productivity, reduced methane and carbon dioxide emissions, and creates new sustainable competitive advantages for leading companies. Particular attention is paid to the role of data management systems as a critical infrastructure prerequisite for the effectiveness of AI in the oil and gas industry. It is argued that the quality, integrity, security, and controlled access to production and operational data are essential for the proper functioning of machine learning algorithms, the development of reliable predictive models, and the implementation of computer vision solutions to monitor compliance with occupational safety regulations. It is shown that for Ukrainian energy companies, the priority is the phased implementation of AI solutions with a focus on areas that provide the greatest impact on operational efficiency and safety-predictive maintenance, emissions monitoring systems, video analytics of production processes, and the digitization of operational documentation. The approach proposed in this article can serve as a conceptual foundation for developing roadmaps for the digital transformation of oil and gas enterprises and for further interdisciplinary research in the field of the digital energy economy.