Addressing the Pressing Needs of Offshore Ultra-Deepwater Floating Facilities and Risers: Near Real-time Management System for Deepwater Drilling Risers

摘要

Subsea drilling operations are moving into harsher, deeper and less familiar offshore environments. To avoid disruptive events due to uncertainties associated with these environments, a Riser Lifecycle Management System (RLMS) has been developed for near real-time condition monitoring and fatigue estimation of drilling risers. In the digital age of the Industrial Internet, decisioning platforms that monitor, assess and make recommendations, like RLMS for UDW equipment, are critical to manage risk. Unlike conventional techniques such as strain gauges for direct strain/stress measurement, the RLMS measures the vibrations of the riser string using accelerometers at selected joints along the drilling riser. The system then transmits the vibration data via acoustic telemetry in near real-time to a topside data acquisition system on the drilling vessel where fatigue life estimates for all riser joints are made using machine learning techniques. Preliminary laboratory and sub-scale rig testing have been conducted to test key subsystems and validate the system functionality and sensor signal fidelity under simulated environments and have demonstrated good results. The RLMS is an integrated platform of both hardware and software tools. The RLMS hardware includes subsea sensing modules each with acoustic telemetry, which enables wireless data communication with a topside data acquisition system on the drilling vessel. A modular approach was used for designing the subsea platform. The platform consists of an acoustic modem and transducer, rechargeable batteries, tri-axial accelerometers and gyroscopes, and a micro-processor for data acquisition and processing. The topside system includes software algorithms for data processing, riser fatigue analysis, and visualization and alerts for enhanced operational decision-making by drilling contractors and operators. The fatigue estimation algorithm takes as inputs the riser configuration (geometry, material properties, and modal data), the associated digital Radio Frequency Identification (RFID) data from each riser joint, and the measured accelerometer data, and in turn generates transfer functions that calculate the ocean current profile. Specifically, an artificial neural network (ANN) model, combined with an optimization algorithm, is used to develop the transfer functions. The inputs to the neural network model are current intensities and the outputs are acceleration features at locations along the riser string where the motion sensors are attached. An optimization algorithm is used to match predicted acceleration from the neural network model with measured acceleration features in order to back-calculate the current intensities. The current intensities are then input into SHEAR7 for estimation of fatigue damage rates. The next phase of the research program will include a field trial to test the integrated RLMS functionality on a drilling rig for near real-time visibility into drilling risers and riser life assessment.