Geothermal reservoirs are a valuable source of sustainable energy. The economic viability of harnessing geothermal energy is dependent on the size of the energy resource. However, determining the energy potential of a geothermal reservoir is generally complicated and uncertain due to a low availability of data. The estimation of the energy resource is often done by calculating the stored heat capacity of the reservoir, using temperature measurements and information on the rock properties. However, these estimations of the geothermal energy resource are approximate and uncertain, relying on a poorly defined recovery factor. Advances in numerical modelling and uncertainty quantification allow us to give a better estimate of the energy resource of a geothermal field. In this paper we illustrate this method using a synthetic model of a geothermal system. By allowing for uncertainty in the model parameters (rock permeability, and the magnitude and location of the deep upflow sources) we generate 2000 sample models based on the geological model of the system. These sample models are each run to steady state using the Waiwera geothermal simulator. The models are then conditioned on the location and temperature of the base of the clay cap using approximate Bayesian computation (ABC). Finally, an innovative method is applied to estimate and run maximum potential production scenarios for each of the conditioned sample models. The forecasts from these scenarios combine to give an assessment of the geothermal resource including uncertainty. The resource assessment uses the same data that are available for a traditional stored heat calculation but also includes reservoir physics, wellbore physics and realistic energy extraction scenarios to provide a more accurate forecast. Our method can be applied in less than a month using widely available computational resources.

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