The digitalization of production and processing systems has significant potential to greatly benefit the Oil & Gas industry, as well as for Carbon Capture Utilization and Storage (CCUS) and for green hydrogen (H2) production using offshore renewable energy. Digital Twins, in particular, play a critical role in not only designing new applications but also enhancing existing machinery. This integration of advanced technologies empowers the industry to achieve unprecedented levels of efficiency, controllability, and predictive maintenance, ensuring a more competitive and sustainable future.
This paper focuses on developing an embedded Digital Twin for an electric subsea valve actuator, which was modeled using state-space equations based on its physical counterpart's components. Its state-space matrices are continually updated through controller output signals and recalculating system parameters, including mechanical and volumetric efficiencies and the production valve signature, based on data from sensors. To achieve continuous updates, an online and recursive system identification technique employing a Kalman Filter is used to update the Digital Twin's parameters.
Throughout the experiment, a MATLAB script subjected the Digital Twin model to tests utilizing data from the physical counterpart. Both scenarios, with and without online and recursive parameter identification, were explored. Remarkably, the Digital Twin adeptly adapted its parameters, mirroring the behavior of its physical counterpart. The findings conclusively exhibited that the embedded Digital Twin enables condition monitoring of the physical counterpart, bridging sensor gaps and compensating for sensor faults. Such proactive maintenance capabilities enhance equipment reliability significantly. This feature is used in this study to increase the fault-tolerance of an electric Subsea Valve Actuator, providing a digital redundancy to critical components such as position and torque sensors.
