Data Driven Virtual Sensors for Riser Prognostic Integrity Management – Shankar Sundararaman, Principal Engineer, Houston

30th April – 3rd May 2018  ⋅  NRG Park, Houston, Texas

In-field riser strain or motion monitoring provides direct indication of riser fatigue and strength performance for prognostic integrity management. However, direct riser monitoring in the field has been limited in offshore developments to date with respect to its use as a standard on every floating platform and on every riser system attached to the 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. Furthermore, monitoring devices are typically installed at practical locations, which may not be at fatigue critical areas. A lack of known response in service can result in qualitative inspection planning, conservative fatigue predictions and reduced asset utilization.

Data-driven virtual sensors provide an innovative solution for life of field riser monitoring and predictive inspection planning. The virtual sensor is a machine learning model that calculates stresses in the time domain at the fatigue hot spots and at high stress locations along the riser. The sensor is driven by platform motions and environmental loads that are typically measured in the field. It allows calculation of fatigue in near real-time using time synchronous data. The virtual sensor system can be integrated with a topside or centralized data platform.

The vision for the virtual sensor is to train a machine learning model initially with finite element global riser analysis data and subsequently with full scale field data from the riser in service. Data aggregation from a term and deployment limited riser monitoring system is employed to capture system-specific response. This hybrid strategy results in low cost virtual sensors for life of field riser monitoring.

This paper describes the motivation, methodology, validation and applications for riser virtual sensors. A framework for the development of a riser system machine learning model is described. Global response data is obtained from a vertical top tensioned riser operating in a water depth of 5,000 ft. Fatigue predictions are compared against the fatigue damage calculated from finite element analysis data.

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