Integration of digital twins and the Internet of Things into oil and gas asset management systems to increase reliability and reduce operating costs

Abstract

Modern oil and gas companies are faced with the need to improve the efficiency of asset management in the face of increasing complexity of equipment operation and stricter economic requirements. A distinctive feature of the current stage of development is the introduction of innovative technologies such as the Internet of Things (IoT) and digital twins, which have the potential to transform the approach to data monitoring, management and analysis. This paper is devoted to the study of the integration of these technologies to improve system reliability and reduce operating costs. The research used a methodology for designing and implementing digital twins based on real-time data using IoT devices. The key stages of integration are analyzed: data collection and transmission, development of digital equipment models, their testing and adaptation. A systematic approach was applied to modeling asset exploitation processes, as well as predictive analytics methods based on machine learning and big data. The results of the work confirmed that the use of IoT devices provides a continuous flow of data necessary for the creation and updating of digital twins. The integration of these tools makes it possible to identify potential malfunctions even before they occur, optimize maintenance schedules, and predict equipment wear. Experimental data showed a reduction in operating costs by up to 20% and an increase in equipment service life by 15% while minimizing the number of emergencies. The integration of digital twins and the Internet of Things into oil and gas asset management systems demonstrates significant potential in improving infrastructure reliability and reducing costs. The results of the study confirm the need for further development and implementation of these technologies to ensure the sustainable and safe operation of the oil and gas complex.