The Roswinkel gas field in the northeastern part of the Netherlands has been in production between 1980 and 2005. Located at about 2100 m depth, it is a severely faulted anticlinal structure, constituting up to 30 reservoir compartments. Due to the complicated nature of this field, there are large uncertainties in both the fault transmissibilities and the strength of the connected aquifer. Consequently, there is a possibility of undepleted compartments in the reservoir.

Pressure depletion due to gas production results in compaction of the reservoir sandstones leading to surface subsidence. The gas production in Roswinkel has induced subsidence of approximately 17 cm above the centre of the field. The subsidence at any point on the surface is a result of compaction over a large area within the reservoir.

We estimated the compaction in the reservoir using subsidence data in order to reduce the uncertainties in fault transmissibility and aquifer connection. We employed a previously developed Bayesian inversion method in which prior knowledge is combined with observations. The prior knowledge on the compaction and the associated uncertainties were generated by Monte Carlo simulations of the reservoir in which the fault transmissibilities were varied. In the mean time the geological reality was maintained and the production history was honored. The average prior compaction field is a relatively smooth field extending well into the aquifer, with a typical uncertainty of 40%.

Our inversion results show a reservoir in which certain large faults are dividing the reservoir in compartments containing different pressure histories. Furthermore, the aquifer activity appeared to be much weaker than the average prior knowledge suggested. The posterior uncertainty of the compaction levels was reduced to about 10%. Our study demonstrates that a carefully executed inversion exercise can considerably reduce uncertainties, thus giving scope for the identification of undepleted compartments in the reservoir.

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.