中文版 | English
Title

Investigating Injection Pressure as a Predictor to Enhance Real-Time Forecasting of Fluid-Induced Seismicity: A Bayesian Model Comparison

Author
Corresponding AuthorFeng, Yu
Publication Years
2023-03-01
DOI
Source Title
ISSN
0895-0695
EISSN
1938-2057
Volume94Issue:2A
Abstract
Fluid-induced seismicity is now a growing concern in the spotlight and managing its risks entails a probabilistic forecast model suited to real-time applications, which com-monly relies on the operational parameter of injection rate in a nonhomogeneous Poisson process. However, due to potential injectivity change, gas kicks, and other proc-esses, injection rate may not provide as robust a proxy for the forcing process as injec-tion pressure, which embodies fluid-rock interactions. Hence, we present a Bayesian approach to prospective model comparison with parameter uncertainties considered. We tested nine geothermal stimulation case studies to comprehensively demonstrate that injection pressure is indeed the main physical predictor of induced seismicity rel-ative to injection rate, and when combined with the latter as predictors, can give the best-performing model and robustly enhance real-time probabilistic forecasting of induced seismicity. We also discussed the implications of our results for seismic risk management and potential directions for further model improvement.
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
National Natural Science Foundation of China[U2039202] ; Shenzhen Science and Technology Program[RCBS20210609103200001]
WOS Research Area
Geochemistry & Geophysics
WOS Subject
Geochemistry & Geophysics
WOS Accession No
WOS:000990328200001
Publisher
ESI Research Field
GEOSCIENCES
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/420652
DepartmentAcademy for Advanced Interdisciplinary Studies
理学院_地球与空间科学系
前沿与交叉科学研究院_风险分析预测与管控研究院
Affiliation
1.Southern Univ Sci & Technol, Inst Risk Anal Predict & Management, Acad Adv Interdisciplinary Studies, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China
First Author AffilicationAcademy for Advanced Interdisciplinary Studies
Corresponding Author AffilicationAcademy for Advanced Interdisciplinary Studies
First Author's First AffilicationAcademy for Advanced Interdisciplinary Studies
Recommended Citation
GB/T 7714
Feng, Yu,Mignan, Arnaud,Sornette, Didier,et al. Investigating Injection Pressure as a Predictor to Enhance Real-Time Forecasting of Fluid-Induced Seismicity: A Bayesian Model Comparison[J]. SEISMOLOGICAL RESEARCH LETTERS,2023,94(2A).
APA
Feng, Yu,Mignan, Arnaud,Sornette, Didier,&Gao, Ke.(2023).Investigating Injection Pressure as a Predictor to Enhance Real-Time Forecasting of Fluid-Induced Seismicity: A Bayesian Model Comparison.SEISMOLOGICAL RESEARCH LETTERS,94(2A).
MLA
Feng, Yu,et al."Investigating Injection Pressure as a Predictor to Enhance Real-Time Forecasting of Fluid-Induced Seismicity: A Bayesian Model Comparison".SEISMOLOGICAL RESEARCH LETTERS 94.2A(2023).
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