Data model improves risk management in the North Sea


A North Sea oil major set the goal of managing risk more effectively across multiple maintenance projects in proximity to each other on the same asset, then across multiple assets at the fleet level.

The inability to resolve anomalies effectively created an environment for accumulating risk and expense based on e.g. needlessly repeated activities such as removal and rebuilding of scaffolding that could have been left in place for use on two proximal jobs.

Solution and results

  • Took account and overview of the raw data available from the client and how it was structured
  • Employed a deep learning algorithm to extract and consolidate the data into a risk model by visually depicting and highlighting risk accumulation across the asset
  • Allowed anomalies to be contextualized along with risk factors, locations, and business-specific parameters
  • Resulted in value-enhancing decision support for planning and mobilization impact while de-risking operations and maintenance (O&M) activities, resulting in 10% savings across O&M budget

2018 LNGPT1 A07 049

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