Drilling risers are slender structures used during offshore operations by drilling companies to provide access from a surface vessel to the top section of the well (wellhead) at the sea floor. The purpose is to allow for drilling or completion of wells or to perform workover of the wells at a later stage. The lowermost section of the riser is usually a blowout preventer (BOP) that is to contain hydrocarbons in the case of an unexpected release. However, BOPs are very heavy structures that cause significant structural fatigue of wellheads when exposed to loads from the riser. Different technologies have been used to reduce the loads applied to the wellheads. The study focuses on a few much used technologies including the Reactive Flex-Joint (RFJ) which has been in commercial use for approximately half a year. The RFJ is effectively a muscle placed on top of the BOP counteracting the loads applied by the riser. The technology has been carefully studied earlier, but new data allows for more in-depth understanding of its complex mechanical response. Global riser analysis models (numerical models) are much used to assess structural integrity of wellhead and risers. There are in the literature, however, few examples of such models being independently validated by measurements. Thus far it has not been common to place sensors on riser systems to measure loads directly due to relatively high cost of sensor systems. Furthermore, systematic evaluation of data from sensors is not widely reported. Most models used for assessment of riser loads are based on finite element methods or other types of discretization of the fundamental partial differential equations. An alternative approach however are machine learning algorithms that rather make use of measured data from operations offshore. Such methods could in principle be more accurate and faster when used within certain limits.
Project leader: Per Thomas Moe
Institution: FMC KONGSBERG SUBSEA AS