Abstract

This paper presents the development of a tightly-coupled Integrated Asset Model (IAM) to capture the surface-subsurface interactions of 5 gas condensate reservoirs producing through a common surface facilities network. The objective of the exercise is to develop a tool that is built to combine existing compositional simulation models and the surface network model in a single platform/environment that could be used in production optimization, de-bottlenecking, field development and flow assurance.

The success criteria included providing a solution that can ensure:

  • Adequate representation of the different fluids of the 5 reservois

  • Full adherence to existing network constraints and field development guidelines

  • Providing optimal network configuration based on the use of automatic production optimization procedures.

  • Flexibility to add complex network elements and decision logic

The most powerful and unique feature that enabled all of the above was the effective use of procedures and built-in functions in Nexus that offer the capability to incorporate operational considerations into the solution.

The first step involved developing a common fluid components basis to be used for the surface and subsurface models. This was achieved through adopting a suitable lumping scheme that involved minimal adjustment to the existing equations of state (EOS) and no compromise of the fluid description or the quality of the history matches

Next step involved converting all the five reservoir models and their surface network model into the same simulation environment. The final step involved developing the procedures that carried out production optimization and proposed the optimal settings to fully utilize the available compression capacity.

Finally, the surface elements of the IAM were calibrated to the results of production capacity tests. The calibration involved matching the observed gas production rates, tubing head pressures and flowline pressures. This step is required to validate the constrained model performance and prediction.

The results showed the magnitude of interaction between the reservoirs and clearly identified the system bottlenecks. The model can be used to propose the best tie-in location of future wells in addition to providing first-pass flow assurance indications by highlighting elements of the network at risk of erosion throughout the field's life and under different network configurations.

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