Big Data to Optimise Plant Operations

A close collaboration between researchers and a small Norwegian start-up leads to a new product, the ‘AlarmTracker’, ready for the control rooms of oil and gas platforms in 2019. The device supports the operator in making the right decisions in abnormal situations. Its objective is to secure a steady production, leading to an expected five percent increase of oil production.

Professor Morten Lind at Electrical Engineering, DTU talks about his focus on information technology for advanced control systems and how that is done in Danish Hydrocarbon Research and Technology Centre’s research. Meet him here in the movie.

DHRTC's hypothesis

This demo model focuses on developing models to help optimise plant operations. Initially, the aim is to support the optimisation the water injection (WI) systems and the produced water treatment (PWT) systems at Dan and Halfdan.

  • Big data analysis can improve the availability of water injection / produced water treatment over time
  • Reasons for downtime can be identified and mitigated by real-time decision support, as well as improved monitoring and control.
  • Development of specific cause-consequence models (MFM) to ensure online causal assessment and decision-making support
  • Validation and verification using the Aalborg University Esbjerg (AAU-E) PWT pilot plant
  • 'Classic’ big data analysis of the Dan water injection / PWT packs
  • Development of a ‘surrogate model’ in the MFM method
  • Development of relational software between MFM, FMECA, HAZOP and P&ID ‘languages’