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Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648767/ https://www.ncbi.nlm.nih.gov/pubmed/29051528 http://dx.doi.org/10.1038/s41598-017-13585-9 |
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author | Mignan, A. Broccardo, M. Wiemer, S. Giardini, D. |
author_facet | Mignan, A. Broccardo, M. Wiemer, S. Giardini, D. |
author_sort | Mignan, A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5648767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-56487672017-10-26 Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections Mignan, A. Broccardo, M. Wiemer, S. Giardini, D. Sci Rep Article 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. Nature Publishing Group UK 2017-10-19 /pmc/articles/PMC5648767/ /pubmed/29051528 http://dx.doi.org/10.1038/s41598-017-13585-9 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Mignan, A. Broccardo, M. Wiemer, S. Giardini, D. Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections |
title | Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections |
title_full | Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections |
title_fullStr | Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections |
title_full_unstemmed | Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections |
title_short | Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections |
title_sort | induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648767/ https://www.ncbi.nlm.nih.gov/pubmed/29051528 http://dx.doi.org/10.1038/s41598-017-13585-9 |
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