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Our solution required:

EOS Modelling

By careful data selection and Equation of State (EOS) modelling we selected representative fluid compositions for each producing reservoir. We modelled test separator and production separator train configurations.

The producing wellstream compositions were calculated as a function of bottom hole pressures and these wellstreams were passed through the process train model to produce Export condensate gas ratios (CGRs).


The Export condensate production was modelled by using the BHP output of the history matched simulation model to determine the Export CGRs of the wells determined by the separator train modelling. The predicted export rates matched the fiscal meter liquid export rates over time to 0.06 percent by mass. There was no need for tuning or regression; we believe this to be a world first on mixed condensate systems. The method can also be applied in predictive mode for future liquid production and accommodate changing processing conditions. It gives greater accuracy of liquid rates of a compositional model while retaining the advantages of full field simulation accuracy..

The accurate predictions on a well-by-well basis also lead to a dramatic improvement in liquids allocation. The method is sufficiently accurate to be used in place of measured welltest results if required.

Armada Hub Condensate Production and Condensate Allocation

The Problem

The modelling and allocation of condensate production in a mature condensate hub such as Armada is complicated due to

  • variable partner interests
  • changing reservoir/process conditions due to depletion and higher pressure
  • satellite fields being brought online
  • Rate dependency on welltest CGRs and difficulty in getting reproducibility in lower condensate yield tests
  • Significant errors in converting from meter to export rates
  • Simulators handle export volumes poorly


We developed a new approach to modelling liquids production from condensate fields by accurately modelling fluid properties sub surface and through surface processing. This approach provides a flexible, fast and cheap method of calibrating an Integrated Asset Model, and monitoring allocation results. In certain cases of multi-field gas condensate systems the expense and complexity of developing an Integrated Asset Model is not warranted, and this approach provides an attractive alternative for generating commingled processed liquid production data while honouring Eclipse 100 model output.