Induced Seismicity Forecasting with Uncertainty Quantification: Application to the Groningen Gas Field
Abstract
Reservoir operations related to natural gas extraction, fluid disposal, carbon diox-
ide storage, or geothermal energy production, are capable of inducing seismicity. Mod-
eling tools have been developed that allow for quantitative forecasting of seismicity based
on operations data, but the computational cost of such models and the difficulty in rep-
resenting various sources of uncertainties make uncertainty quantification challenging.
We address this issue in the context of an integrated modeling framework, which com-
bines reservoir modeling, geomechanical modeling, and stress-based earthquake forecast-
ing. We use the Groningen gas field as a case example of application. The modeling frame-
work is computationally efficient thanks to a 2-D finite-element reservoir model which
assumes vertical flow equilibrium, and the use of semi-analytical 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. The model parameters and their un-
certainties are estimated using either a Poisson or a Gaussian likelihood. We investigate
the effect of the likelihood choice on the forecast performance and we estimate uncer-
tainties in the predicted number of earthquakes as well as in the expected magnitudes.
We 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
non-tapered Gutenberg-Richter distributions to evaluate the most probable maximum
magnitude over time and account for uncertainties in its estimation. We show that the
framework yields realistic estimates of the seismicity model uncertainties and is appli-
cable for operational forecasting or to design induced seismicity monitoring. It could also
serve as a basis for probabilistic traffic-light systems.
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