Stress‐Based and Convolutional Forecasting of Injection‐Induced Seismicity: Application to the Otaniemi Geothermal Reservoir Stimulation

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AbstractInduced seismicity observed during Enhanced Geothermal Stimulation at Otaniemi, Finland is modeled using both statistical and physical approaches. The physical model produces simulations closest to the observations when assuming rate-and-state friction for shear failure with diffusivity matching the pressure build-up at the well-head at onset of injections. Rate-and-state friction implies a time-dependent earthquake nucleation process which is found to be essential in reproducing the spatial pattern of seismicity. This implies that permeability inferred from the expansion of the seismicity triggering front (Shapiro et al., 1997, can be biased. We suggest a heuristic method to account for this bias that is independent of the earthquake magnitude detection threshold. Our modeling suggests that the Omori law decay during injection shut-ins results mainly from stress relaxation by pore pressure diffusion. During successive stimulations, seismicity should only be induced where the previous maximum of Coulomb stress changes is exceeded. This effect, commonly referred to as the Kaiser effect, is not clearly visible in the data from Otaniemi. The different injection locations at the various stimulation stages may have resulted in sufficiently different effective stress distributions that the effect was muted. We describe a statistical model whereby seismicity rate is estimated from convolution of the injection history with a kernel which approximates earthquake triggering by fluid diffusion. The statistical method has superior computational efficiency to the physical model and fits the observations as well as the physical model. This approach is applicable provided the Kaiser effect is not strong, as was the case in Otaniemi.
Original languageEnglish
Article numbere2022JB024960
Pages (from-to)1-27
Number of pages27
JournalJournal of Geophysical Research: Solid Earth
Issue number4
Early online date25 Mar 2023
Publication statusPublished (in print/issue) - 4 Apr 2023

Bibliographical note

Funding Information:
This study was supported by the National Science Foundation via the Industry‐University Collaborative Research Center Geomechanics and Mitigation of Geohazards (award #1822214) and the National Science Foundation Graduate Research Fellowship (award #DGE‐1745301). The authors are grateful to Dr. David Dempsey and the associate editor for their insightful and thorough reviews. The authors would like to thank our colleagues and collaborators for numerous discussions and their comments on the manuscript, in particular Grzegorz Kwiatek, Mateo Acosta, Kyungjae Im, Krittanon Sirorattanakul, Maxim Vraine, Guanli Wang, Thomas Ader, and Tero Saarno.

Publisher Copyright:
© 2023. The Authors.


  • induced seismicity
  • seismicity forecasting
  • geothermal energy


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