This paper discusses a new workflow to stochastically estimate the performance of future production in coal seam gas (CSG) developments. Usually performance evaluations for CSG wells are conducted using either much-generalised statistical methods or numerical simulation. Both approaches have significant drawbacks; the former methods are quick but very often lack accuracy, while the latter is very accurate however also usually highly complex in set-up and computation. The presented workflow is a new approach to well performance prediction that combines speed and reasonable accuracy. The workflow generates a set of key performance indicators of existing wells derived from historic dynamic data (water and gas production rates, pressures, etc.), static data (initial coal and reservoir properties, etc.) and predicted data (simplified production forecasts). The wells are then grouped according to the similarity of their KPIs. The production profiles of the wells within the same group are combined to a type curve that is described by the most likely production profile and an associated uncertainty range. A data-driven expert system is used to identify and capture the correlations of the parameters such as geographic locations, well spacing, reservoir properties and the group membership (equivalent to type curve). This expert system can then be applied to any location in the field in order to determine the most likely group membership of a potential well. The classification of a new well to a group is hereby not necessarily unique; the expert system might classify a new well into several groups and assign a probability of occurrence for each of the groups. A Monte Carlo routine is then applied to forecast the performance of the new well locations honoring the respective probability of occurrence of each type curve.