Probabilistic forecasting of hydraulic fracturing-induced seismicity using an injection-rate driven ETAS Model

Mancini, Simone; Werner, Maximilian Jonas; Segou, Margarita; Baptie, Brian. 2021 Probabilistic forecasting of hydraulic fracturing-induced seismicity using an injection-rate driven ETAS Model. Seismological Research Letters, 92 (6). 3471-3481. https://doi.org/10.1785/0220200454

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Official URL: http://dx.doi.org/10.1785/0220200454

Abstract/Summary

The development of robust forecasts of human‐induced seismicity is highly desirable to mitigate the effects of disturbing or damaging earthquakes. We assess the performance of a well‐established statistical model, the epidemic‐type aftershock sequence (ETAS) model, with a catalog of ∼93,000 microearthquakes observed at the Preston New Road (PNR, United Kingdom) unconventional shale gas site during, and after hydraulic fracturing of the PNR‐1z and PNR‐2 wells. Because ETAS was developed for slower loading rate tectonic seismicity, to account for seismicity caused by pressurized fluid, we also generate three modified ETAS with background rates proportional to injection rates. We find that (1) the standard ETAS captures low seismicity between and after injections but is outperformed by the modified model during high‐seismicity periods, and (2) the injection‐rate driven ETAS substantially improves when the forecast is calibrated on sleeve‐specific pumping data. We finally forecast out‐of‐sample the PNR‐2 seismicity using the average response to injection observed at PNR‐1z, achieving better predictive skills than the in‐sample standard ETAS. The insights from this study contribute toward producing informative seismicity forecasts for real‐time decision making and risk mitigation techniques during unconventional shale gas development.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1785/0220200454
ISSN:	0895-0695
Date made live:	26 May 2021 14:34 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/530399

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1785/0220200454

ISSN:

0895-0695

Date made live:

26 May 2021 14:34 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/530399