We perform statistical analyses on spatiotemporal patterns in the magnitude distribution of induced earthquakes in the Groningen natural gas field. The seismic catalogue contains 336 earthquakes with (local) magnitudes above, observed in the period between 1 January 1995 and 1 January 2022. An exploratory moving-window analysis of maximum-likelihood b-values in both time and space does not reveal any significant variation in time, but does reveal a spatial variation that exceeds the significance level. In search for improved understanding of the observed spatial variations in physical terms we test five physical reservoir properties as possible b-value predictors. The predictors include two static (spatial, time-independent) properties: The reservoir layer thickness, and the topographic gradient (a measure of the degree of faulting intensity in the reservoir); and three dynamic (spatiotemporal, time-dependent) properties: The pressure drop due to gas extraction, the resulting reservoir compaction, and a measure for the resulting induced stress. The latter property is the one that is currently used in the seismic source models that feed into the state-of-The-Art hazard and risk assessment. We assess the predictive capabilities of the five properties by statistical evaluation of both moving window analysis, and maximum-likelihood parameter estimation for a number of simple functional forms that express the b-value as a function of the predictor. We find significant linear trends of the b-value for both topographic gradient and induced stress, but even more pronouncedly for reservoir thickness. Also for the moving window analysis and the step function fit, the reservoir thickness provides the most significant results. We conclude that reservoir thickness is a strong predictor for spatial b-value variations in the Groningen field. We propose to develop a forecasting model for Groningen magnitude distributions conditioned on reservoir thickness, to be used alongside, or as a replacement, for the current models conditioned on induced stress.

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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 honoured and then reduced with the observational constraints. Ensembles of induced stress realizations are built by varying the Poisson's ratio in a poro-elastic model incorporating the 3-D 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 honoured 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 honouring 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.

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In February and March 2020 most European countries were confronted with the outbreak of the coronavirus disease 2019 (COVID-19). The Dutch Outbreak Management Team (OMT) and hospitals were in urgent need of forecasts that could help prepare for the increasing number of patients requiring ICU beds and that could also assess the effect of government measures to limit virus transmission. In the early stages of the outbreak, earth scientists from TNO and TU Delft both recognised the possibilities to apply Earth-science-based modelling expertise for these challenges. The two teams, working independently, both developed models to forecast the outbreak in different countries and to assess the effectiveness of social distancing measures@en