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Sci Rep. 2017 Oct 19;7(1):13607. doi: 10.1038/s41598-017-13585-9.

Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections.

Author information

1
Swiss Federal Institute of Technology Zurich, Institute of Geophysics, Zurich, Switzerland. arnaud.mignan@sed.ethz.ch.
2
Swiss Competence Center for Energy Research - Supply of Electricity, Zurich, Switzerland. arnaud.mignan@sed.ethz.ch.
3
Swiss Seismological Service, Zurich, Switzerland. arnaud.mignan@sed.ethz.ch.
4
Swiss Competence Center for Energy Research - Supply of Electricity, Zurich, Switzerland.
5
Swiss Seismological Service, Zurich, Switzerland.
6
Swiss Federal Institute of Technology Zurich, Institute of Geophysics, Zurich, Switzerland.

Abstract

The rise in the frequency of anthropogenic earthquakes due to deep fluid injections is posing serious economic, societal, and legal challenges to many geo-energy and waste-disposal projects. Existing tools to assess such problems are still inherently heuristic and mostly based on expert elicitation (so-called clinical judgment). We propose, as a complementary approach, an adaptive traffic light system (ATLS) that is function of a statistical model of induced seismicity. It offers an actuarial judgement of the risk, which is based on a mapping between earthquake magnitude and risk. Using data from six underground reservoir stimulation experiments, mostly from Enhanced Geothermal Systems, we illustrate how such a data-driven adaptive forecasting system could guarantee a risk-based safety target. The proposed model, which includes a linear relationship between seismicity rate and flow rate, as well as a normal diffusion process for post-injection, is first confirmed to be representative of the data. Being integrable, the model yields a closed-form ATLS solution that is both transparent and robust. Although simulations verify that the safety target is consistently ensured when the ATLS is applied, the model from which simulations are generated is validated on a limited dataset, hence still requiring further tests in additional fluid injection environments.

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