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The induced seismicity in the Groningen gas field, The Netherlands, presents contrasted spatio-temporal patterns between the central area and the south west area. Understanding the origin of this contrast requires a thorough assessment of two factors: (1) the stress development on the Groningen faults, and (2) the frictional response of the faults to induced stresses. Both factors have large uncertainties that must be honored and then reduced thanks to observational constraints. The present contribution builds upon the previous work of Candela et al. (2019), which was focused on the frictional response, and extends it by taking into account uncertainties in the stress development and by evaluating the relative effect of both factors. Ensembles of induced stress realizations are built by varying the Poisson’s ratio in a poro-elastic model incorporating the 3D complexities of the geometries of the Groningen gas reservoir and its faults, and the historical pore pressure distribution. The a-priori uncertainties in the frictional response are mapped by varying the parameters of a seismicity model based on rate-and-state friction. The uncertainties of each component of this complex physics-based model are honored through an efficient data assimilation algorithm. By assimilating the seismicity data with an Ensemble-Smoother, the prior uncertainties of each model parameter are effectively reduced, and the posterior seismicity rate predictions are consistent with the observations. Our integrated workflow allows us to disentangle the contributions of the main two factors controlling the induced seismicity at Groningen, induced stress development and fault frictional response. Posterior distributions of the model parameters of each modelling component are contrasted between the central and south west area at Groningen. We find that, even after honoring the spatial heterogeneity in stress development across the Groningen gas field, the spatial variability of the observed induced seismicity rate still requires spatial heterogeneity in the fault frictional response. This work is enabled by the unprecedented deployment of an Ensemble-Smoother combined with physics-based modelling over a complex case of reservoir induced seismicity.

Over 190 gas fields have been exploited in The Netherlands and only 15-20% have been associated with induced seismicity. We assess the geomechanical characteristics of stress changes on faults due to gas depletion for 180 producing gas fields in the Netherlands. We confirm findings from earlier generic studies that inter-reservoir offset faults require less reservoir depletion to reach failure compared to bounding and small offset faults. However, the stress changes on the offset faults alone are not sufficient to explain the observed seismicity. We find that the presence of the visco-elastic Zechstein formation probably has a crucial influence on the in-situ stress field in the Dutch subsurface and significantly impacts the fault stability of the gas reservoirs in the Netherlands. By accounting for this influence, our results show remarkable consistency with the observed (non)occurrence of induced seismicity in the Dutch gas fields. A more detailed study taking into account the detailed geological information of reservoir and fault geometry available at the operators and the slip weakening behavior of the frictional strength is required to further refine the predictive power of our analysis.

We implement a Coulomb rate‐and‐state approach to explore the nonlinear relation between stressing rate and seismicity rate in the Groningen gas field. Coulomb stress rates are calculated, taking into account the 3‐D structural complexity of the field and including the poroelastic effect of the differential compaction due to fault offsets. The spatiotemporal evolution of the Groningen seismicity must be attributed to a combination of both (i) spatial variability in the induced stressing rate history and (ii) spatial heterogeneities in the rate‐and‐state model parameters. Focusing on two subareas of the Groningen field where the observed event rates are very contrasted even though the modeled seismicity rates are of similar magnitudes, we show that the rate‐and‐state model parameters are spatially heterogeneous. For these two subareas, the very low background seismicity rate of the Groningen gas field can explain the long delay in the seismicity response relative to the onset of reservoir depletion. The characteristic periods of stress perturbations, due to gas production fluctuations, are much shorter than the inferred intrinsic time delay of the earthquake nucleation process. In this regime the modeled seismicity rate is in phase with the stress changes. However, since the start of production and for two subareas of our analysis, the Groningen fault system is unsteady and it is gradually becoming more sensitive to the stressing rate.

The Wadden Sea is a shallow tidal sea in the north of the Netherlands where gas production is ongoing since 1986. Due to the sensitive nature of this area, gas extraction induced subsidence must remain within the "effective subsidence capacity" for the two tidal basins (Pinkegat and Zoutkamperlaag) affected. We present a probabilistic method to monitor the "effective subsidence capacity" and ensure that subsidence is below the long term (18.6 years) volumetric rate for relative sea level rise that can be accommodated by the tidal basins without environmental harm. The role of sedimentation volume rate, relative sea level rise and subsidence volume rate due to gas depletion are taken into account including their uncertainties. The probability of exceeding the acceptable subsidence limit for the period 2012 to 2050 is 2.8% for the tidal basin called Zoutkamperlaag and 1% for the tidal basin of Pinkegat for climate scenarios that fit the current relative sea level rise observations on the Dutch coast. The values are shown to be dominated by the effect of relative sea level rise, and not due to subsidence induced by gas depletion in the Wadden Sea. To current knowledge no harm is done to nature. Copyright 2015 ARMA, American Rock Mechanics Association.

In the northern parts of The Netherlands, the exploitation of gas fields has induced small earthquakes. These induced earthquakes have occasionally caused damage to buildings. A workflow was developed in order to evaluate the seismic risk of the exploitation of hydrocarbon fields. A Bayesian analysis was performed to investigate the correlation between the reservoir properties and production specifics of the fields and the occurrence of induced earthquakes. Based on geomechanical and reservoir production key parameters, a probability of that induced seismicity will occur can be assigned to an individual hydrocarbon field. Probable ground motions for average surface conditions were obtained from a probabilistic seismic hazard analysis. Next, the impact of local soil conditions was taken into account by analysing the site response of shallow soil profiles. Finally, the relation of ground motions and probability of damage was investigated, using data on damage during past induced erathquakes. In this extended abstract a concise overview of this workflow is presented.© 2010, Society of Petroleum Engineers.

In an attempt to derive more information on the parameters driving compaction, this paper explores the feasibility of a method utilizing data on compaction-induced subsidence. We commence by using a Bayesian inversion scheme to infer the reservoir compaction from subsidence observations. The method's strength is that it incorporates all the spatial and temporal correlations imposed by the geology and reservoir data. Subsequently, the contributions of the driving parameters are unravelled. We apply the approach to a synthetic model of an upscaled gas field in the northern Netherlands. We find that the inversion procedure leads to coupling between the driving parameters, as it does not discriminate between the individual contributions to the compaction. The provisional assessment of the parameter values shows that, in order to identify adequate estimate ranges for the driving parameters, a proper parameter estimation procedure (Markov Chain Monte Carlo, data assimilation) is necessary. © Springer Science+Business Media B.V. 2008.