The Netherlands is subject to anthropogenic and natural subsidence with rates for which the relative contribution of each process to total subsidence (i.e. natural plus anthropogenic) is still unclear. Such information is important for stakeholders to support decision making on subsidence mitigation and it should be substantiated with independent scientific studies. We present the outline of a hybrid Artificial Intelligence (AI) and knowledge-based physical model (geomechanical models) workflow to disentangle different subsidence forcing. We use static (structural model) and time-dependent data for both surface (geodetic measurements: GPS, levelling and InSAR) and subsurface (phreatic groundwater level, reservoir pressure). We show preliminary results of the approach for an area covering a gas field in the Friesland coastal plain and an area in the peat-rich central Rhine-Meuse delta plain. @en

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.@en

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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The coastal plains of the Netherlands are subject to anthropogenic and natural subsidence with rates which are an order of magnitude higher than sea-level rise. Because one-third of the Netherlands lies below mean sea level, subsidence may threaten the country’s subsistence with major socio-economic consequences. Subsidence is a normal natural process but is overprinted and accelerated by anthropogenic activities which drives deep or shallow processes. Deep processes are caused by the extraction of hydrocarbons and salt, whereas shallow processes are primarily caused by lowering of phreatic groundwater levels. At present, the relative contribution of each process to total subsidence (i.e. natural plus anthropogenic) is unclear. Such information is important for stakeholders to support decision making on subsidence mitigation and it should be substantiated with independent scientific studies. We present the outline of a hybrid Artificial Intelligence (AI) big data and model workflow to disentangle different subsidence forcing and we report on preliminary results for an area covering a gas field in the peat-rich Friesland coastal plain. The proposed workflow is a hybrid approach between a knowledge-based physical model and machine-learning techniques. The big input data comprises a suite of static (structural model) and time-dependent subsurface data (phreatic groundwater level, reservoir pressure), and geodetic measurements. Geomechanical models provide the connection between the drivers (groundwater levels and reservoir pressures) and the surface movement.@en