This paper discusses the use of a real-time monitoring system to track wave driven fatigue damage at critical locations in a subsea well during a 290-day drilling operation in the Mediterranean Sea. Real-time monitoring was employed due to up-front finite element (FE) analysis predicting fatigue damage rates of sufficient magnitude that the required safety factor would not be met.
LMRP/BOP motions were recorded using two subsea monitoring units (each containing accelerometers and angular rate sensors). These were installed on the LMRP and above the lower flex joint. The accelerometer signals were used to deduce the static inclination of the well and the angular rate signals the dynamic inclination due to vessel motion and wave loading on the riser. Data was transmitted in real-time to a central processing unit on board the vessel. This converted the motion data into fatigue damage accumulation using non-linear transfer functions derived from the FE model. The damage was displayed in a simplified format for use in decision-making.
The recorded data revealed the motions of the LMRP/BOP and hence the damage rates in the well to be considerably less than predicted by the FE analysis. The damage rates derived from the monitoring system therefore served as a continuously updating demonstration that an acceptable margin of safety against suffering a fatigue failure was being maintained thus permitting drilling to proceed safely.
A subsequent benchmarking study which additionally involved processing of recorded vessel motion data, current data and seastate data was undertaken to identify possible reasons for the differences between the predicted and measured LMRP/BOP motions. This showed that conservative assumptions made on the soil stiffness (due to limited availability of data), differences between the predicted and measured riser natural periods and differences between the predicted and measured vessel motions all contributed to the LMRP/BOP motions and hence the damage rates predicted by the up-front FE analysis being larger than measured in the field. The systematic identification of the factors which contributed to the differences between the predicted and measured response is described in this paper. Areas where further work would be of value to help improve the reliability of up-front, analytically predicted fatigue damage rates are also discussed.