OFFSHORE DRILLING
Develop a full understanding of riser system health Digital elements of an asset integrity program described By Kenneth Bhalla
T
he drilling riser on a mobile offshore drilling unit (MODU) features a conduit between the drilling rig and well system that serves a critical role within the system. Typically comprised of a series of connected 75-feet to 90-feet long riser joints, the riser is the main conduit to drill through, connecting to the top of the blow-out preventer (BOP), which is latched to the wellhead and casing system on the seafloor. It is essential to develop a full understanding of riser system health to ensure that all riser damage and associated risks are captured and quantified accurately based on past loading history and any future loading states. Annual inspection rotation Drilling riser joints are typically inspected at five-year intervals. This is usually performed by rotating 20% of the riser joints onshore every year to be dis-assembled and inspected. Many costly and time-consuming boat trips from the MODU to onshore facilities are required for this procedure, in addition to trucking the riser to the inspection facility once onshore. Approximately 20 riser joints from each riser system are transported by boat and one riser per truck to the inspection facility each year, making the logistics of performing a drilling riser joint inspection complex and costly. However, since the riser system experiences significant wear, erosion, corrosion, fatigue damage and seal damage during operations, this process has long been considered essential for tracking deterioration over time. Digital elements Invoking digital elements of a condition-based maintenance and monitoring program are ultimately meant to reduce both operational
8 • DECEMBER 2019 OIL&GAS ENGINEERING
expenditures and capital expenditures, and to minimize and manage risk with accurate information pertaining to system health. The elements listed below form an Internet of Things (IoT) eco-system used to manage asset health: A physics-based digital twin of the asset drives the process. Sensor technology is designed and specified to understand: • The physical loading of the asset • The response of the asset. Data cleaning is used to characterize the asset loading and response measured by the sensors. Inspection techniques are used to create a living digital signature of the asset. Data science techniques such as artificial intelligence, machine learning, and deep learning are used with the sensor data and physics-based digital twin to characterize future response states from physical loading. The future response states can be verified by inspection. These elements or resources form the basis of a process that leads to: • Cost savings • Risk reduction through understanding of key performance indicators • Increased performance with additional data analytics. The process data resides on a dedicated information management system, which supports the user with inventory management data. Reliability, consistency, accuracy A condition-based maintenance system which leverages monitoring of riser systems with a laser-based inspection system, has been implemented with a new standard process, which is American Bureau of Shipping (ABS) approved.
