Abstract
Amid the digital transformation of the global economy, Big Data analytics and advanced analytics technologies are becoming key tools for enhancing business efficiency and sustainability. Their application is particularly relevant in the capital-intensive and high-risk oil and gas industry, where data-driven decision-making offers significant competitive advantages. This study examines the opportunities and benefits of implementing Big Data and analytical solutions across various stages of the oil and gas production cycle – from geological exploration and drilling to processing and transportation. The study presents the main data sources and types characteristic of the industry, as well as modern analytical methods, including descriptive, predictive, prescriptive, and real-time analytics. Special attention is given to machine learning and artificial intelligence algorithms used for predicting equipment failures, optimizing drilling parameters, and modeling reservoir behavior. Based on the analysis of case studies from leading international companies such as BP, Equinor, Gazprom Neft, and others, it is shown how digital tools can improve decision-making accuracy, reduce operational costs, and minimize technological risks. The study also examines the key challenges hindering the widespread adoption of Big Data in the sector, including a shortage of qualified personnel, integration difficulties between legacy and modern systems, cybersecurity concerns, and the high cost of digital transformation. The analysis leads to the conclusion that data and analytics constitute strategic assets for the future development of the oil and gas industry. Digital technologies open up new horizons in forecasting, management, and sustainable production, paving the way for next-generation intelligent oil and gas enterprises.