Currently, New Zealand generates around 18% of its total electrical capacity using geothermal sources and could benefit significantly from simulation for optimization (Ministry of Business, Innovation & Employment, 2017). Current geothermal process and reservoir simulations are conducted separately with data manually parsed between the different simulators. Delays in the modelling process and the inability to efficiently model effects that reservoir changes over the assets lifetime have on the plant. Coupling both process and reservoir simulators would enable accurate prediction of both reservoir and plant issues. In this paper, a proof of concept is developed. The reservoir simulator AUTOUGH is coupled with the process simulator VMGSim using Python and PyTOUGH. A demonstration geothermal model was built using 2 simulated production and injection wells in which data is exchanged with an Organic Rankine Cycle (ORC) geothermal plant. The aim of this study is to demonstrate and compare the effects of coupled and un-coupled models have on a both the process and reservoir of the geothermal industry. Geothermal fluid mass, pressure and temperature data is passed between AUTOUGH and VMGSim where both the wellbore and plant is simulated. Brine injection data is passed back to AUTOUGH. This cycle is run until either a simulated plant failure occurs or the simulation is terminated. In an ORC plant, typical failures relate to temperature drops in the geothermal fluid that lead to the inability to vaporize the working fluid used to power the turbines. As a result plant changes are required to maintain production, which could reduce power generation or require drilling an additional production well. Coupling models adds additional benefits to the modelling process that support the optimization of both reservoir and surface related activity. Reservoir production rate in relation to reservoir model size determines the impact that coupled models on forecasting. Addition of fouling has shown significant changes to reinjection temperatures and when incorporated into a coupled model will increase the benefit of coupling.