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

Overview of attention for article published in Scientific Reports, October 2017
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Title
Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections
Published in
Scientific Reports, October 2017
DOI 10.1038/s41598-017-13585-9
Pubmed ID
Authors

A. Mignan, M. Broccardo, S. Wiemer, D. Giardini

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 31%
Student > Ph. D. Student 22 27%
Student > Master 5 6%
Other 4 5%
Professor 4 5%
Other 8 10%
Unknown 14 17%
Readers by discipline Count As %
Earth and Planetary Sciences 37 45%
Engineering 15 18%
Energy 3 4%
Business, Management and Accounting 1 1%
Psychology 1 1%
Other 3 4%
Unknown 23 28%