Robust Digital Twin Approach Towards Operational Excellence and Sustainability Goals

Abstract The objective of this paper is to provide new insights and best practices towards the digital twin concepts, particularly in the oil and gas sector and the potential benefits towards achieving the operational excellence and sustainability goals. By using the digital twin technology for asset management, reservoir management, process optimization and training and simulation, the operating companies not only improve efficiency, reduce costs, and achieve operational excellence, more importantly it can also optimize the operations and reduce GHG emissions. The digital twin concept helps in creating a digital replica of the physical assets and the associated processes. This technology can transform the way the oil and gas industry operate, enabling to achieve the operations excellence. As every field produces enormous amount of data which comes from sensors and field automation, the same can be leveraged through a digital twin that can support right from the conceptual design and various other phases of a field development capital project till operations and maintenance up to the retirement of the field and its associated assets. This paper looks in detail at the various approaches wherein the operating companies with their digital information and management system at organization level, can augment such Enterprise level digital twin methodologies. It also addresses the various process of Statement of Requirements (SOR), Data sources analysis, Modelling of the digital twins, Scenario analysis, and Implementation and continuous monitoring and improvement. This paper utilizes the various lessons learned and real time scenarios correlated with specific applications in the upstream oil and gas facilities. The results perceived, noticeably identify the importance for adopting the approach in a structured way rather than starting at the random stages while arraying the Digital twin journey. It further scrutinizes the digital twin solution architecture which can fit within the current organization wide enterprise domain, and also the latent and resultant advantages of implementation within premises or in a cloud environment. Another key aspect in this strategy was to develop the requirement standard for the Digital twin at enterprise level for existing assets and new development fields. In addition, it highlights the application specific scenarios which can be considered prototypical in implementation of this technology in the oil and gas industry having the potential to improve operational and sustainability performance and minimize environmental impact. Lastly, it concludes with the accompanying challenges to ensure that the digital twin solution is cyber secure and compliant while integrating with existing / new control systems such as DCS/ICSS and ERP to provide a comprehensive view of the physical entity's performance and to support effective decision-making. The practice of digital twin technology in the oil and gas industry is a relatively innovative area of research and development, and there is still much to be learned about how to effectively implement this technology and leverage it to achieve sustainability goals. While there is some existing literature on the use of digital twin technology, much of this paper has focused on the digital twin solution in a comprehensive platform leveraging machine learning algorithms that can connect with a plenty of data sources to provide real-time visibility into asset performance, enabling them to optimize operations, reduce emissions, and better manage environmental risks.