Digital twin technology for enhanced upstream capability in oil and gas

Abstract

Digital twins are receiving considerable attention as a cutting-edge technology that will transform the oil and gas industry. Powered by a digital thread that connects data across the product lifecycle, a digital twin virtually mirrors or emulates processes, assets, and projects in real-time to generate highly valuable insights. Promises of value creation, delivering optimized production, increased reliability, improved safety, and enhanced foresight, are now driving oil and gas operators to realize their potential. Despite these claimed and expected benefits, realized value is often hard to quantify and explicitly link to digital twin technology. And in addition, consistency in definition and availability of a reference architecture is lacking, resulting in a gap of a standard approach in implementing this technology. This thesis attempts to investigate and summarize the digital twin's enabling technologies (e.g., model-based systems engineering, network infrastructure, the Internet of Things (IoT), and automation) and provide insight into industry digital twin applications in use today. Modeling a simple production facility demonstrates that digital twins have the potential to improve the prediction and mitigation of facility failures leading to overall higher availability and improved financial outlooks for projects. The simulation results of a highly robust and integrated digital twin used on an offshore, deepwater facility showed an improved NPV of $211 million over 27 years. With enhanced upstream capabilities enabled by a digital twin, considerations to reducing daily physical inspection requirements are made more feasible. However, as costs for offshore personnel decrease, the cost of software development and maintenance will increase sharply. Oil and gas assets are more enabled to be monitored and controlled remotely through this increased rigor and oversight provided by the digital twin platform. From the perspective of a three-component digital twin framework consisting of modeling and analytics, enablement technology, and data, a digital twin can provide value from a virtual proxy to a fully autonomous system.