Seismic slip‐pulse experiments simulate induced earthquake rupture in the Groningen gas field

We present a data-driven analysis to derive whether statistically significant spatial and/or temporal Gutenberg-Richter b-value variations exist within the induced earthquake catalogue of the Groningen gas field. We utilize the method developed by Kamer and Hiemer (2015; J. Geophys. Res. Solid Earth, 120, doi:10.1002/2014JB011510 ) which is based on optimal partitioning using Voronoi tessellation, penalized likelihood, and wisdom of the crowd philosophy. Our implementation derives both the magnitude of completeness and the b-values simultaneously. The magnitude of completeness is computed with the maximum curvature method with a correction applied to avoid bias due to catalogue incompleteness. Finally, following Marzocchi et al. (2020; Geophys. J. Int. 220, doi: 10.1093/gji/ggz541) the b-values computed are corrected for bin size and small sample sizes.In a first step we have limited the analysis to spatial variations in the b-values. A significant advantage of the approach taken is that it is feasible to also derive b-values in regions of very low data density. We will show that a statistically significant variation in b-values is obtained. Very low b-values (b<0.8) are observed in the central-northern part of the gas field. However, in the west near the production cluster Eemskanaal (EKL) and in the east near the city of Delfzijl significantly higher b-values (b>1.1) are observed. A Kolmogorov-Smirnov test of frequency-magnitude distributions for the two areas obtains a p-value of 1.5 10-13 and 2.3 10-12 for the EKL region and Delfzijl regions, respectively, rendering the difference more than statistically significant at the 99% confidence level.In a second step we extended the spatial analysis to a spatial-temporal analysis. The results of the analysis show that the Groningen earthquake database is too small to derive meaningful spatial results for the full Groningen gas field based on multiple random temporal nodes.&#160; We divided the dataset in two almost equal datasets: both containing roughly 50% of the data and of comparable spatial resolution. Spatial analysis of these two subsets of the catalogue shows a significant decrease of the b-values in the central and southern regions. Particularly in the western EKL region the b-value decreases from 1.2 to 0.92. The decrease is close to significant at the 90% confidence level. The northern region exhibits comparable low b-values in both periods. As the data in the first decade is primarily concentrated in the northern region, we have attempted to assess the spatial b-value here in the period prior to 2005. We find the high b-value area is significantly smaller and the minimum value is higher (b = 0.96 pre-2005 versus b = 0.88 post-2012). The difference is significant only at the interquartile level, but the model resolution is low.Based on our results, we could conclude a spatial and temporal variation in b-value is observed. However, despite our efforts to limit bias in the derivation, variations could still result from the presence of a truncation. Hence, we will extend the current analysis by a comparable analysis assuming a constant b-value and estimating the corner magnitude of a taper truncation.

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Relating spatial-temporal b-value patterns of induced earthquakes of the Groningen gas field to differential stress changes due to reservoir extraction

10.5194/egusphere-egu21-9913 ◽

2021 ◽

Author(s):

Nilgün Güdük ◽

Annemarie Muntendam-Bos ◽

Jan Dirk Jansen

Keyword(s):

Pore Pressure ◽

B Value ◽

Enhanced Geothermal Systems ◽

Differential Stress ◽

Gas Field ◽

Seismic Slip ◽

Stress Changes ◽

Pressure Depletion ◽

Groningen Gas Field ◽

Event Magnitude

The Gutenberg-Richter law describes the frequency-magnitude distribution of seismic events where its slope, the 'b-value', is commonly used to describe the relative occurrence of large and small events. Statistically significant b-value variations have been measured in laboratory experiments, mines, and various tectonic regimes (Wiemer & Wyss, 2002). An inversely proportional dependency of the b-value on the differential stress has been observed across different scales (Amitrano, 2003; Schorlemmer et al., 2005). Layland-Bachmann et al. (2012) have shown that this could explain the observed pattern of induced seismicity spatial-temporal b-value variations in Enhanced Geothermal Systems. In our study, we look for a similar relation applied to the Groningen gas field in the Netherlands.It is well known that the poroelastic changes in differential stress during gas extraction are influenced by the offset of the reservoir layer across the fault. Recently, Jansen et al. (2019) and Lehner (2019) proposed an analytical solution for stress changes on offset faults due to reservoir depletion. In a parallel study, we extended this solution to include the development of aseismic slip under slip weakening and the derivation of the onset of seismic slip. We utilize this formulation to derive the onset of seismic slip on theoretical faults of variable fault offset, dip, and reservoir thickness. Subsequently, we map our theoretical faults onto the pre-existing faults in the Groningen gas field, deriving fault segment-specific depletion levels at which the segment would become seismically active. We then simulate reservoir depletion conditions over time and assign an event magnitude to fault segments that move past their seismic activation depletion. To assign a magnitude, we use the observation that b-values are inversely proportional to differential stress, which is governed by the pore pressure depletion. Hence, we assume a simple inverse linear relation with pore pressure depletion. Each event magnitude is then randomly drawn from the probability density function of the Gutenberg-Richter distribution with the b-value assigned. We aim to compare the obtained catalogue and its b-value distribution both in time and space to the observed event-size distribution of the Groningen gas field as derived by Muntendam-Bos and G&#252;d&#252;k (EGU abstract 2021).

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Framework for a Ground-Motion Model for Induced Seismic Hazard and Risk Analysis in the Groningen Gas Field, The Netherlands

Earthquake Spectra ◽

10.1193/082916eqs138m ◽

2017 ◽

Vol 33 (2) ◽

pp. 481-498 ◽

Cited By ~ 46

Author(s):

Julian J. Bommer ◽

Peter J. Stafford ◽

Benjamin Edwards ◽

Bernard Dost ◽

Ewoud van Dedem ◽

...

Keyword(s):

Seismic Hazard ◽

Ground Motion ◽

Quantitative Estimation ◽

Epistemic Uncertainty ◽

Induced Earthquakes ◽

Gas Field ◽

Small Magnitude ◽

Ground Motion Model ◽

Hazard And Risk ◽

Groningen Gas Field

The potential for building damage and personal injury due to induced earthquakes in the Groningen gas field is being modeled in order to inform risk management decisions. To facilitate the quantitative estimation of the induced seismic hazard and risk, a ground motion prediction model has been developed for response spectral accelerations and duration due to these earthquakes that originate within the reservoir at 3 km depth. The model is consistent with the motions recorded from small-magnitude events and captures the epistemic uncertainty associated with extrapolation to larger magnitudes. In order to reflect the conditions in the field, the model first predicts accelerations at a rock horizon some 800 m below the surface and then convolves these motions with frequency-dependent nonlinear amplification factors assigned to zones across the study area. The variability of the ground motions is modeled in all of its constituent parts at the rock and surface levels.

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Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

Interpretation ◽

10.1190/int-2021-0137.1 ◽

2021 ◽

pp. 1-67

Author(s):

Stewart Smith ◽

Olesya Zimina ◽

Surender Manral ◽

Michael Nickel

Keyword(s):

Machine Learning ◽

Fault Detection ◽

Seismic Data ◽

Fault Prediction ◽

Machine Learning Techniques ◽

Gas Field ◽

Seismic Fault ◽

Learning Techniques ◽

Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

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Statistical physics models for aftershocks and induced seismicity

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2017.0397 ◽

2018 ◽

Vol 377 (2136) ◽

pp. 20170397 ◽

Cited By ~ 7

Author(s):

Molly Luginbuhl ◽

John B. Rundle ◽

Donald L. Turcotte

Keyword(s):

Induced Seismicity ◽

Statistical Physics ◽

Aftershock Sequence ◽

Induced Earthquakes ◽

Aftershock Activity ◽

Gas Field ◽

Natural Time ◽

Parkfield Earthquake ◽

Groningen Gas Field

A standard approach to quantifying the seismic hazard is the relative intensity (RI) method. It is assumed that the rate of seismicity is constant in time and the rate of occurrence of small earthquakes is extrapolated to large earthquakes using Gutenberg–Richter scaling. We introduce nowcasting to extend RI forecasting to time-dependent seismicity, for example, during an aftershock sequence. Nowcasting uses ‘natural time’; in seismicity natural time is the event count of small earthquakes. The event count for small earthquakes is extrapolated to larger earthquakes using Gutenberg–Richter scaling. We first review the concepts of natural time and nowcasting and then illustrate seismic nowcasting with three examples. We first consider the aftershock sequence of the 2004 Parkfield earthquake on the San Andreas fault in California. Some earthquakes have higher rates of aftershock activity than other earthquakes of the same magnitude. Our approach allows the determination of the rate in real time during the aftershock sequence. We also consider two examples of induced earthquakes. Large injections of waste water from petroleum extraction have generated high rates of induced seismicity in Oklahoma. The extraction of natural gas from the Groningen gas field in The Netherlands has also generated very damaging earthquakes. In order to reduce the seismic activity, rates of injection and withdrawal have been reduced in these two cases. We show how nowcasting can be used to assess the success of these efforts. This article is part of the theme issue ‘Statistical physics of fracture and earthquakes’.

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Geology of Groningen Gas Field, Netherlands: ABSTRACT

AAPG Bulletin ◽

10.1306/5d25c3bf-16c1-11d7-8645000102c1865d ◽

1968 ◽

Vol 52 ◽

Author(s):

A. J. Stauble, G. Milius

Keyword(s):

Gas Field ◽

Groningen Gas Field

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Probabilistic Moment Tensor Inversion for Hydrocarbon-Induced Seismicity in the Groningen Gas Field, the Netherlands, Part 2: Application

Bulletin of the Seismological Society of America ◽

10.1785/0120200076 ◽

2020 ◽

Vol 110 (5) ◽

pp. 2112-2123 ◽

Cited By ~ 3

Author(s):

Bernard Dost ◽

Annemijn van Stiphout ◽

Daniela Kühn ◽

Marloes Kortekaas ◽

Elmer Ruigrok ◽

...

Keyword(s):

Induced Seismicity ◽

Moment Tensor ◽

Moment Tensor Inversion ◽

Gas Field ◽

Double Couple ◽

Recent Developments ◽

Inversion Procedure ◽

Seismic Sections ◽

The Moment ◽

Groningen Gas Field

ABSTRACT Recent developments in the densification of the seismic network covering the Groningen gas field allow a more detailed study of the connection between induced seismicity and reactivated faults around the gas reservoir at 3 km depth. With the reduction of the average station distance from 20 km to 4–5 km, a probabilistic full-waveform moment tensor inversion procedure could be applied, resulting in both improved hypocenter location accuracy and full moment tensor solutions for events of M≥2.0 recorded in the period 2016–2019. Hypocenter locations as output from the moment tensor inversion are compared to locations from the application of other methods and are found similar within 250 m distance. Moment tensor results show that the double-couple (DC) solutions are in accordance with the known structure, namely normal faulting along 50°–70° dipping faults. Comparison with reprocessed 3D seismic sections, extended to a depth of 6–7 km, demonstrate that (a) most events occur along faults with a small throw and (b) reactivated faults in the reservoir often continue downward in the Carboniferous underburden. From non-DC contributions, the isotropic (ISO) component is dominant and shows consistent negative values, which is expected in a compacting medium. There is some indication that events connected to faults with a large throw (>70 m) exhibit the largest ISO component (40%–50%).

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