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Abstract Gas extraction from the Groningen gas reservoir, located in the northeastern Netherlands, has led to a drop in pressure and drove compaction and induced seismicity. Stress-based models have shown success in forecasting induced seismicity in this particular context and elsewhere, but they generally assume that earthquake clustering is negligible. To assess earthquake clustering at Groningen, we generate an enhanced seismicity catalog using a deep-learning-based workflow. We identify and locate 1369 events between 2015 and 2022, including 660 newly detected events not previously identified by the standard catalog from the Royal Netherlands Meteorological Institute. Using the nearest-neighbor distance approach, we find that 72% of events are background independent events, whereas the remaining 28% belong to clusters. The 55% of the clustered events are swarm-like, whereas the rest are aftershock-like. Among the swarms include five newly identified sequences propagating at high velocities between 3 and 50 km/day along directions that do not follow mapped faults or existing structures and frequently exhibit a sharp turn in the middle of the sequence. The swarms occurred around the time of the maximum compaction rate between November 2016 and May 2017 in the Zechstein layer, above the anhydrite caprock, and well-above the directly induced earthquakes that occur within the reservoir and caprock. We suggest that these swarms are related to the aseismic deformation within the salt formation rather than fluids. This study suggests that the propagating swarms do not always signify fluid migration. 

Abstract Reservoir operations for gas extraction, fluid disposal, carbon dioxide storage, or geothermal energy production are capable of inducing seismicity. Modeling tools exist for seismicity forecasting using operational data, but the computational costs and uncertainty quantification (UQ) pose challenges. We address this issue in the context of seismicity induced by gas production from the Groningen gas field using an integrated modeling framework, which combines reservoir modeling, geomechanical modeling, and stress-based earthquake forecasting. The framework is computationally efficient thanks to a 2D finite-element reservoir model, which assumes vertical flow equilibrium, and the use of semianalytical solutions to calculate poroelastic stress changes and predict seismicity rate. The earthquake nucleation model is based on rate-and-state friction and allows for an initial strength excess so that the faults are not assumed initially critically stressed. We estimate uncertainties in the predicted number of earthquakes and magnitudes. To reduce the computational costs, we assume that the stress model is true, but our UQ algorithm is general enough that the uncertainties in reservoir and stress models could be incorporated. We explore how the selection of either a Poisson or a Gaussian likelihood influences the forecast. We also use a synthetic catalog to estimate the improved forecasting performance that would have resulted from a better seismicity detection threshold. Finally, we use tapered and nontapered Gutenberg–Richter distributions to evaluate the most probable maximum magnitude over time and account for uncertainties in its estimation. Although we did not formally account for uncertainties in the stress model, we tested several alternative stress models, and found negligible impact on the predicted temporal evolution of seismicity and forecast uncertainties. Our study shows that the proposed approach yields realistic estimates of the uncertainties of temporal seismicity and is applicable for operational forecasting or induced seismicity monitoring. It can also be used in probabilistic traffic light systems. 

Abstract A variety of geo‐energy operations involve extraction or injections of fluids, including hydrocarbon production or storage, hydrogen storage, CO₂sequestration, and geothermal energy production. The surface deformation resulting from such operations can be a source of information on reservoir geomechanical properties as we show in this study. We analyze the time‐dependent surface deformation in the Groningen region in northeastern Netherlands using a comprehensive geodetic data set, which includes InSAR (Radarsat2, TerraSAR‐X, Sentinel‐1), GNSS, and optical leveling spanning several decades. We resort to an Independent Component Analysis (ICA) to isolate deformation signals of various origins. The signals related to gas production from the Groningen gas field and from seasonal storage at Norg Underground Gas Storage are clearly revealed. Surface deformation associated to the Groningen reservoir show decadal subsidence, with spatially variable subsidence rates dictated by local compressibility. The ICA reveals distinct seasonal fluctuations at Norg, closely mirroring the variations of gas storage. By comparing the observed long‐term subsidence within the Groningen reservoir and seasonal oscillations at Norg from a linear poroelastic compaction model, we quantify the fraction of inelastic deformation of the reservoir in space and time and constrain the reservoir compressibility. In Groningen, increased compressibility indicates inelastic compaction that has built over time and might account for as much as 20% of the total compaction cumulated until 2021, while Norg shows no signs of inelastic deformation and a constant compressibility. This study provides a methodology to monitor and calibrate models of the subsurface deformation induced by geo‐energy operations or aquifer management. 

Abstract Deterministic earthquake prediction remains elusive, but time‐dependent probabilistic seismicity forecasting seems within reach thanks to the development of physics‐based models relating seismicity to stress changes. Difficulties include constraining the earthquake nucleation model and fault initial stress state. Here, we analyze induced earthquakes from the Groningen gas field, where production is strongly seasonal, and seismicity began 3 decades after production started. We use the seismicity response to stress variations to constrain the earthquake nucleation process and calibrate models for time‐dependent forecasting of induced earthquakes. Remarkable agreements of modeled and observed seismicity are obtained when we consider (a) the initial strength excess, (b) the finite duration of earthquake nucleation, and (c) the seasonal variations of gas production. We propose a novel metric to quantify the nucleation model's ability to capture the damped amplitude and the phase of the seismicity response to short‐timescale (seasonal) stress variations which allows further tightening the model's parameters.