Implementation of Advanced Digital Technologies in Subsea Asset Life Extension – Ricky Thethi, Global Director, Woking

11th February 2020  ⋅  Session 7 – 10:45 – 12:45  ⋅  Kota Kinabalu, Malaysia

Many deepwater floating production assets are nearing the end of their original service life. Operators are looking to continue service of these assets to take advantage of existing or new production; or at least defer the date of decommissioning.

Risers are a critical dynamic component on the FPS and their life extension engineering process requires the current condition, past operating and future operating conditions to be well understood to determine remaining life and justify continued service. Key threats to riser systems include corrosion, structural defects and fatigue damage.

The application of advanced AI technologies is well suited for some of the computationally intensive integrity analysis needed in life extension engineering. This presentation will present the use of machine learning to develop an application using computer vision to automatically detect external anomalies from ROV/AUV video footage. Similarly, a response digital twin for risers will be presented where real time environmental and vessel motion measurements can be used to track the stress and fatigue life at critical areas of the system.

The development process, speed and accuracy of these applications will be demonstrated using case studies on various types of riser systems.

More information: SPE Workshop Malaysia

Implementation of Advanced Digital Technologies in Subsea Asset Life Extension – Ricky Thethi, Global Director, Woking

11th February 2020  ⋅  Session 7 – 10:45 – 12:45  ⋅  Kota Kinabalu, Malaysia

Many deepwater floating production assets are nearing the end of their original service life. Operators are looking to continue service of these assets to take advantage of existing or new production; or at least defer the date of decommissioning.

Risers are a critical dynamic component on the FPS and their life extension engineering process requires the current condition, past operating and future operating conditions to be well understood to determine remaining life and justify continued service. Key threats to riser systems include corrosion, structural defects and fatigue damage.

The application of advanced AI technologies is well suited for some of the computationally intensive integrity analysis needed in life extension engineering. This presentation will present the use of machine learning to develop an application using computer vision to automatically detect external anomalies from ROV/AUV video footage. Similarly, a response digital twin for risers will be presented where real time environmental and vessel motion measurements can be used to track the stress and fatigue life at critical areas of the system.

The development process, speed and accuracy of these applications will be demonstrated using case studies on various types of riser systems.

More information: SPE Workshop Malaysia