Developing an SCR Digital Twin Using Machine Learning
Machine learning is an enabling technology that can be used in the structural design, integrity management and life extension of riser systems, to better predict in-service response faster and with less input data streams. While in-field riser strain or motion monitoring provides direct indication of riser fatigue and strength performance, direct structural monitoring of these systems has been limited in offshore deployments and is not often used for every riser system on a production vessel. This is due to the high perceived capital or operational costs of direct subsea monitoring when deployed on every riser to reliably monitor over the entire service life. A lack of known structural response in service can result in qualitative inspection planning, conservative fatigue predictions and reduced asset utilization. Machine learning provides an alternative, low cost approach for life-of-field riser monitoring and predictive inspection planning.
1 Nov 2019
This is a recording of a live webinar held in November 2019