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Maximum magnitude forecast in hydraulic stimulation based on clustering and size distribution of early microseismicity

Afshari Moein, M. and Tormann, T. and Valley, B. and Wiemer, S. (2018):
Maximum magnitude forecast in hydraulic stimulation based on clustering and size distribution of early microseismicity.
In: Geophysical Research Letters, AGU, pp. 6907-6917, DOI: 10.1029/2018GL077609, [Online-Edition: https://doi.org/10.1029/2018GL077609],
[Article]

Abstract

We interpreted the spatial clustering and size distribution of induced microseismicity observed during the stimulation of an enhanced geothermal system beneath Basel by comparison with scale‐invariant synthetic data derived from discrete fracture network models. We evaluated microseimic specific influential factors including the effect of hypocentral location uncertainties, existence of a fractured zone and repeating events on the observed spatial organization. Using a dual power‐law model originally developed in the context of discrete fracture network modeling, we developed theoretically the relationships among spatial clustering and magnitude distributions. We applied this model to the Basel data set and showed that the spatial clustering characteristics presented stationary properties during the hydraulic stimulation. Based on this observation, we proposed a statistical seismicity model calibrated on the scaling of early stimulation spatial patterns that is capable of forecasting the maximum magnitude of induced events with increasing injection time and stimulated volume.

Item Type: Article
Erschienen: 2018
Creators: Afshari Moein, M. and Tormann, T. and Valley, B. and Wiemer, S.
Title: Maximum magnitude forecast in hydraulic stimulation based on clustering and size distribution of early microseismicity
Language: English
Abstract:

We interpreted the spatial clustering and size distribution of induced microseismicity observed during the stimulation of an enhanced geothermal system beneath Basel by comparison with scale‐invariant synthetic data derived from discrete fracture network models. We evaluated microseimic specific influential factors including the effect of hypocentral location uncertainties, existence of a fractured zone and repeating events on the observed spatial organization. Using a dual power‐law model originally developed in the context of discrete fracture network modeling, we developed theoretically the relationships among spatial clustering and magnitude distributions. We applied this model to the Basel data set and showed that the spatial clustering characteristics presented stationary properties during the hydraulic stimulation. Based on this observation, we proposed a statistical seismicity model calibrated on the scaling of early stimulation spatial patterns that is capable of forecasting the maximum magnitude of induced events with increasing injection time and stimulated volume.

Journal or Publication Title: Geophysical Research Letters
Publisher: AGU
Divisions: 11 Department of Materials and Earth Sciences
11 Department of Materials and Earth Sciences > Earth Science
11 Department of Materials and Earth Sciences > Earth Science > Geothermal Science and Technology
Date Deposited: 04 Oct 2018 12:06
DOI: 10.1029/2018GL077609
Official URL: https://doi.org/10.1029/2018GL077609
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