Published March 2024 | Supplemental Material
Journal Article Open

Induced Seismicity Forecasting with Uncertainty Quantification: Application to the Groningen Gas Field

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Shell (Netherlands)

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.

Copyright and License

© Seismological Society of America

Acknowledgement

The authors express their sincere gratitude to the National Science Foundation (NSF) for their financial support of this research project through the Industry‐University Collaborative Research Center Geomechanics and Mitigation of Geohazards (Award Number 1822214). In addition, the authors are grateful for the valuable input and discussion provided by Elías Rafn Heimisson, Kyungjae Im, and Jan Van Elk. The authors are also grateful to the anonymous reviewer for their valuable insights and to David Dempsey for his specific and thoughtful comments and suggestions which improved this paper substantially.

Data Availability

We have used the seismicity catalog from the Royal Dutch Meteorological Survey (KNMI; www.knmi.nl, last accessed May 2022). We have used previously published resources to find the stress distribution (Smith et al., 20192022Meyer et al., 2022). To forecast for up to 2030, we have used the “cold winter” scenario suggested in NAM (2013). Our codes for Algorithm 1 for some simple examples introduced in Appendix A5, available in the supplemental material to this article, are available on GitHub (https://github.com/hojjatks/UQ-Seismicity-Forecasting, last accessed September 2023). The supplemental material for this article includes a brief formulation of the spatial stress calculation (Appendix A1, available in the supplemental material to this article), analysis of sensitivity of uncertainties to different stress calculations (Appendix A2, available in the supplemental material to this article), a description of the observed and the synthetic catalog (Appendix A3, available in the supplemental material to this article), discretization of equation (1) (Appendix A4, available in the supplemental material to this article), some illustrative examples of our uncertainty quantification (UQ) methodology and impact of the data on the UQ bounds (Appendix A5), Lemma 1 for finding confidence interval bound (Appendix A6), the sensitivity of the total confidence interval to different combinations of epistemic and aleatoric uncertainty levels (Appendix A7), and the likelihood of the model parameters (Appendix A8).

Supplemental Material

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Additional details

Created:
October 23, 2024
Modified:
January 7, 2025