Comprehensive monitoring and prediction of seismicity within the Groningen gas field using large-scale field observations

This dissertation addresses the critical issue of induced seismicity within the Groningen gas field, one of the largest natural gas reservoirs globally. Since its discovery in the 1960s, the Groningen field has played a pivotal role in meet- ing the energy needs of the Netherlands. However, decades of sustained gas extraction have resulted in significant seismic activity in what was previously a tectonically inactive region. These induced earthquakes have raised press- ing public safety concerns, necessitating a comprehensive understanding of the underlying processes, alongside the development of robust methodologies for monitoring and predicting seismic events.

The aim of this research is to advance the monitoring, analysis, and pre- diction of seismicity by integrating statistical models with geomechanical in- sights and decision-making frameworks. Specifically, this study seeks to analyse the spatial and temporal patterns of seismic activity, predict future earthquake occurrences using spatio-temporal models, and optimise production strategies while mitigating seismic risk. Conducted within the framework of the DeepNL programme by the nwo (nwo), this work forms part of a broader effort to deepen understanding of subsurface dynamics influenced by anthropogenic activities.

A multidisciplinary methodological approach was employed to achieve the study objectives. The research relies on an extensive dataset comprising seis- mic event records provided by knmi (covering magnitudes ≥ 1.5 from 1995 to 2023), historical gas production data, pore pressure measurements from bore- holes, and NAM’s dynamic reservoir model output. Rigorous data cleaning and integration procedures ensured the datasets’ reliability, which formed the basis for subsequent statistical analyses.

The study begins with a detailed temporal and spatial analysis of seismic activity. Temporal trends were examined using Poisson point processes, which allowed for the identification of earthquake timing patterns. Moreover, adap- tive kernel estimation techniques were employed to detect spatial clustering of seismic events, particularly in regions experiencing significant pore pressure de- pletion. Building upon these initial analyses, spatio-temporal Cox processes were applied to model earthquake intensity more comprehensively. This ap- proach was enhanced by incorporating covariates derived from pore pressure estimates using polynomial kriging and Bayesian inference methods, thereby improving the predictive accuracy of seismic hazard assessments.