Abstract
This paper focuses on the application of a digital twin at a large compression facility in the Middle East. The facility is currently under development, with the first of several compressor trains scheduled to begin operation in 2024.
The objectives of the digital twin application are early anomaly detection and improved reliability and maintenance through pre-empting failures and process upsets. It aims to revolutionize maintenance and operations by leveraging advanced data analytics and predictive modeling. The digital twin also supports virtual training, along with remote monitoring of the compression train -- enhancing safety and improving the productivity of onsite personnel.
By implementing a phased approach, the project ensures a seamless integration of core field data into a 3D virtual environment, supporting intelligent decision-making for operations and maintenance (O&M) activities in preparation for start-up. The main use cases of the digital twin include:
Virtual training of operations and maintenance personnel on the compressor train while it is physically being built and commissioned at the facility.
The ability to run diagnostics using predictive and prescriptive analytics from anywhere in the network (i.e., in a virtual environment or via augmented reality in the physical environment). The aim is to provide advanced notification of potential breakdowns and present personnel with the next steps for corrective action to mitigate risk and/or delay a process upset.
Enhance training, diagnostics, and troubleshooting via remote collaboration with offsite experts in the customer's organization or with the compression train original equipment manufacturer (OEM) while in operation.
Simulation and dynamic modeling for failure prediction and exploration of "what if" scenarios during operation.
Overall, the digital twin application is expected to deliver substantial improvements in reliability, efficiency, and safety.
