GSTDTAP  > 气候变化
DOI10.1029/2018GL077609
Maximum Magnitude Forecast in Hydraulic Stimulation Based on Clustering and Size Distribution of Early Microseismicity
Moein, Mohammad Javad Afshari1; Tormann, Thessa2; Valley, Benoit3; Wiemer, Stefan2
2018-07-28
发表期刊GEOPHYSICAL RESEARCH LETTERS
ISSN0094-8276
EISSN1944-8007
出版年2018
卷号45期号:14页码:6907-6917
文章类型Article
语种英语
国家Switzerland
英文摘要

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.


Plain Language Summary Developing enhanced geothermal systems requires permeability enhancement by hydraulic stimulation, in which pressurized fluid is circulated between injection and production wells. This operation induces microseismicity that may be large enough to be felt by public and result in destructive events. Here we studied the possibility to forecast the maximum magnitude of induced events using the early seismicity patterns during hydraulic stimulation operations. Therefore, we analyzed the spatial clustering and size distribution of induced microseismicity observed during the stimulation of an enhanced geothermal system beneath Basel by comparison with synthetic data derived from fracture network models. We proposed a statistical model, originally developed in the context of discrete fracture network modeling, to represent the clustering and magnitude distribution of induced events. We calibrated the model using the scaling of early stimulation spatial patterns on Basel data set and successfully reproduced the rupture radius distribution, which was also capable of forecasting the maximum magnitude of induced events with increasing injection time and stimulated volume.


领域气候变化
收录类别SCI-E
WOS记录号WOS:000442582100018
WOS关键词LENGTH DISTRIBUTION ; FRACTURE NETWORK ; FAULT PATTERNS ; ROCK ; INJECTION ; MODELS ; SYSTEM
WOS类目Geosciences, Multidisciplinary
WOS研究方向Geology
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/28164
专题气候变化
作者单位1.Swiss Fed Inst Technol, Geol Inst, Dept Earth Sci, Zurich, Switzerland;
2.Swiss Fed Inst Technol, Swiss Seismol Serv SED, Dept Earth Sci, Zurich, Switzerland;
3.Univ Neuchatel, Ctr Hydrogeol & Geotherm, Neuchatel, Switzerland
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GB/T 7714
Moein, Mohammad Javad Afshari,Tormann, Thessa,Valley, Benoit,et al. Maximum Magnitude Forecast in Hydraulic Stimulation Based on Clustering and Size Distribution of Early Microseismicity[J]. GEOPHYSICAL RESEARCH LETTERS,2018,45(14):6907-6917.
APA Moein, Mohammad Javad Afshari,Tormann, Thessa,Valley, Benoit,&Wiemer, Stefan.(2018).Maximum Magnitude Forecast in Hydraulic Stimulation Based on Clustering and Size Distribution of Early Microseismicity.GEOPHYSICAL RESEARCH LETTERS,45(14),6907-6917.
MLA Moein, Mohammad Javad Afshari,et al."Maximum Magnitude Forecast in Hydraulic Stimulation Based on Clustering and Size Distribution of Early Microseismicity".GEOPHYSICAL RESEARCH LETTERS 45.14(2018):6907-6917.
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