Despite potential benefits; the effort required to implement balanced-complexity models; particularly at large scales; may deter their use. This paper presents techniques used in the design of balanced-complexity models. A Modelica-based approach is chosen to reduce implementation effort by interfacing exported Modelica models with application code by means of the generic interface FMI. The suggested approach is demonstrated by parameter estimation for a process of offshore oil production: a subsea well-manifold-pipeline production system.
Keywords: Modeling; process control; process models; process simulators; offshore oil and gas production; Modelica; subsea production; multiphase flow; balanced-complexity models; nonlinear model-predictive control; FMI
Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany
 F. Casella; F. Donida; and J. Akesson. Objectoriented modeling and optimal control: A case study in powerplant start-up. In Proc. 18th IFAC World Congress; Milano; Italy; volume 18; 2011.
 S. Elgs√¶ter; O. Slupphaug; and T. A. Johansen. Challenges in parameter estimation of models for offshore oil and gas production optimization. In International Petroleum Technology Conference; Dubai; 2007. doi: 10.2523/11728-MS.
 H. Elmqvist; H. Tummescheit; and M. Otter. Object-oriented modeling of thermo-fluid systems. Proceedings of 3rd Int. Modelica Conference; pages 269‚Äď286; 2003.
 B. A. Foss and T. S. Schei. Putting nonlinear model predictive control into use. In Assessment and Future Directions Nonlinear Model Predictive Control; LNCIS 358; pages 407‚Äď417. Springer Verlag; 2007.
 R. Franke; B. S. Babji; M. Antoine; and A. Isaksson. Model-based online applications in the abb dynamic optimization framework. In Modelica‚Äô 2008; Bielefeld; Germany; 2008.
 J. M. Godhavn; A. Pavlov; G. O. Kaasa; and N. L. Rolland. Drilling seeking automatic control solutions. In Proc. 18th IFAC World Congress; Milano; Italy; volume 18; Milano;Italy; 2011.
 L. Imsland; P. Kittilsen; and T. S. Schei. Modelbased optimizing control and estimation using modelica models. In Modelica‚Äô2008; Bielefeld; Germany; 2008.
 E. Jahanashahi and S. Skogestad. Simplified dynamical models for control of severe slugging in multiphase risers. In Proc. 18th IFAC World Congress; Milano; Italy; volume 18; Milano; Italy; 2011.
 K. Krueger; M. Rode; R. Franke; and B. A. Foss. Optimization of boiler start-up using a nonlinear boiler model and hard constraints. Energy; 29(12-15):2239‚Äď2251; 2004. doi: 10.1016/j.energy.2004.03.022.
 S. McArdle; D. Cameron; and K. Meyer. The life cycle simulator: From concept to commissioning... and beyond. In SPE Intelligent Energy Conference and Exhibition; pages 246‚Äď267; Utrecht; The Netherlands; 2010.
 P. Meum; P. T√łndel; J. M. Godhavn; and O. M. Aaamo. Optimization of smart well production through nonlinear model predictive control. In Intelligent Energy Conference and Exhibition; Amsterdam; The Netherlands; 2008.
 A. Mjaavatten; Robert Aasheim; Steinar Saelid; and Oddvar Gronning. Model for gas coning and rate-dependent gas/oil ratio in an oil-rim reservoir. SPE Reservoir Evaluation & Engineering; 11(5); 2008. doi: 10.2118/102390-PA.
 Z. K. Nagy; B. Mahn; R. Franke; and F. Allg¬®ower. Nonlinear model predicitive control of batch processes: an industrial case study. In Proc. 16th IFAC World Congress; Prague; Czech Republic; volume 16; 2005.
 R. van der Linden and A. Leemhuis. The use of model predictive control for asset production optimization: Application to a thin-rim oil field case. In SPE Annual Technical Conference and Exhibition; Florence; Italy; 2010.
 A. Willersrud; L. Imsland; S. O. Hauger; and P. Kittlsen. Short-term production optimization of offshore oil and gas production using nonlinear model predictive control. In Proc. 18th IFAC World Congress; Milano; Italy; volume 18; Milano; Italy; 2011.