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Examining the Capability of Statistical Models to Mitigate Induced Seismicity during Hydraulic Fracturing of Shale Gas Reservoirs

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Original languageEnglish
Pages (from-to)690-701
Number of pages12
JournalBulletin of the Seismological Society of America
Volume108
Issue number2
Early online date27 Feb 2018
DOIs
DateAccepted/In press - 16 Jan 2018
DateE-pub ahead of print - 27 Feb 2018
DatePublished (current) - 1 Apr 2018

Abstract

Injection into the subsurface is carried out by industry for a variety of reasons, for example, storing wastewater, enhanced oil recovery, and hydraulic frac- ture stimulation. By increasing subsurface pressures, injection can trigger felt seismic- ity (i.e., sufficient magnitude to be felt at the surface) on pre-existing faults. As the number of cases of felt seismicity associated with hydraulic fracturing (HF) has increased, strategies for mitigating induced seismicity are required. However, most hydraulic stimulation activities do not induce felt seismicity. Therefore, a mitigation strategy is required that is capable of differentiating the normal case from abnormal cases that trigger larger events. In this article, we test the ability of statistical methods to estimate the largest event size during stimulation, applying these approaches to two datasets collected during hydraulic stimulation in the Horn River Shale, British Columbia, where HF was observed to reactivate faults. We apply these methods in a prospective manner, using the microseismicity recorded during the early phases of a stimulation stage to make forecasts about what will happen as the stage continues. We do so to put ourselves in the shoes of an operator or regulator, where decisions must be taken based on data as it is acquired, rather than a post hoc analysis once a stimulation stage has been completed. We find that the proposed methods can provide a reasonable forecast of the largest event to occur during each stage. This means that these methods can be used as the basis of a mitigation strategy for induced seismicity.

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    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via GSA at https://pubs.geoscienceworld.org/ssa/bssa/article/528918/Examining-the-Capability-of-Statistical-Models-to . Please refer to any applicable terms of use of the publisher.

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