A study on cloud and edge computing for the implementation of digital twins in the Oil & Gas industries

Abstract

In the Oil and Gas industry, minor accidents can negatively affect people, the environment, and the enterprise on a grand scale. For this type of business, it is crucial to have adequate monitoring and maintenance operational routines. The concept of digital twins has been widely discussed as an industrial solution capable of monitoring objects in real-time, predicting their state, integrity, and safety conditions, and providing user feedback. This analysis can also perform predictive maintenance and estimate efficiency levels. This paper follows a systematic approach and inspects the current state of digital twin adoption by the Oil and Gas industry, along with cloud and edge utilization in both scenarios. The survey was conducted by filtering and selecting articles published in three different queries regarding digital twins in the Oil and Gas industry, cloud and edge usage in digital twins, and cloud and edge usage in the Oil and Gas industry. The selected articles were classified and reviewed to present an overview of the current literature regarding digital twins in the oil and gas industry and evaluate cloud-based solutions for digital twins in this context. This review found that cloud and edge adoption in the Oil and Gas industry was late compared to other industries, mainly because of security and data privacy concerns. Digital twin adoption across all industries is still in its infancy, as most of the current works are theoretical or present partial implementations, which often follow different digital twin definitions. Cloud and edge computing grant access to increased storage and computational capabilities and decouple the necessity of relying on a robust local IT infrastructure, being critical enabling factors for digital twin implementation.