scholarly journals Using PS-InSAR observations to detect aseismic fault slip in the seismically active Groningen gas field

2021 ◽  
Author(s):  
Marius Wouters ◽  
Ramon F. Hanssen ◽  
Rob Govers
Keyword(s):  
Fault Slip ◽  
Gas Field ◽  
Groningen Gas Field

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

2017 ◽  
Vol 33 (2) ◽  
pp. 481-498 ◽  
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.

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

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.

scholarly journals Statistical physics models for aftershocks and induced seismicity

2018 ◽  
Vol 377 (2136) ◽  
pp. 20170397 ◽  
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’.

Geology of Groningen Gas Field, Netherlands: ABSTRACT

AAPG Bulletin ◽  
1968 ◽  
Vol 52 ◽  
Author(s):  
A. J. Stauble, G. Milius
Keyword(s):  
Gas Field ◽  
Groningen Gas Field

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

2020 ◽  
Vol 110 (5) ◽  
pp. 2112-2123 ◽  
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%).

scholarly journals Coda-Wave Based Monitoring of Pore-Pressure Depletion-driven Compaction of Slochteren Sandstone Samples from the Groningen Gas Field

2020 ◽  
Author(s):  
Reuben Zotz-wilson ◽  
Nikoletta Filippidou ◽  
Arjan Linden ◽  
Berend Antonie Verberne ◽  
Auke Barnhoorn
Keyword(s):  
Pore Pressure ◽  
Coda Wave ◽  
Gas Field ◽  
Pressure Depletion ◽  
Groningen Gas Field

scholarly journals An integrated shear-wave velocity model for the Groningen gas field, The Netherlands

2017 ◽  
Vol 15 (9) ◽  
pp. 3555-3580 ◽  
Author(s):  
Pauline P. Kruiver ◽  
Ewoud van Dedem ◽  
Remco Romijn ◽  
Ger de Lange ◽  
Mandy Korff ◽  
...  
Keyword(s):  
The Netherlands ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Model ◽  
Gas Field ◽  
Groningen Gas Field
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