Subsidence can be induced by hydrocarbon production, due to the decrease in pore pressure in the reservoir which causes the reservoir to compact. The subsidence at any point on the surface is a result of the compaction over a large area of the reservoir. The properties of the reservoir and thus the compaction are uncertain. Therefore, an inversion is needed to constrain the knowledge about compaction in the reservoir with the use of subsidence data. We applied a previously developed linearized subsidence inversion method to the Roswinkel gas field. This field is situated in the northeastern part of The Netherlands. The Roswinkel field has been in production between 1980 and 2005. It is a complicated anticlinal structure with many faults in two major directions, dividing the reservoir in up to 30 reservoir compartments. Prior geomechanical modelling of the Roswinkel field revealed deviations in the measured subsidence from the predicted ideal elliptical shape of the subsidence bowl, possibly indicating partly undepleted compartments in this reservoir. The prior knowledge of the reservoir was quantified using Monte Carlo simulations. The degree of compartmentalization was varied by perturbing the fault transmissibilities. The prior knowledge, contained in the simulation models, includes the expected compaction field, the standard deviations, and the spatial and temporal correlations between the model elements. Our inversion study on Roswinkel demonstrates our ability to constrain the prior uncertainty of the reservoir model. The inversion exercise gave a clear adaptation of the prior compaction field from a smooth, extended field to a sharply bounded field with internal structure. This means that identification of gas compartments and fault properties by inversion of subsidence measurements is feasible. The prior knowledge is the critical part in the inversion exercise; the most critical steps seem to be the geological and the geodetic analysis. For the latter, new data like space-geodetic observations might help improve the analysis. However, we expect the largest improvement to come from integrating inversion steps, implying that all the different data are taken into account simultaneously.