1887
Volume 65, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Like most other industrial activities that affect the subsurface, hydraulic fracturing carries the risk of reactivating pre‐existing faults and thereby causing induced seismicity. In some regions, regulators have responded to this risk by imposing traffic light scheme‐type regulations, where fracture stimulation programs must be amended or shut down if events larger than a given magnitude are induced. Some sites may be monitored with downhole arrays and/or dense near‐surface arrays, capable of detecting very small microseismic events. However, such monitoring arrangements will not be logistically or economically feasible at all sites. Instead, operators are using small, sparse arrays of surface seismometers to meet their monitoring obligations.

The challenge we address in this paper is to maximise the detection thresholds of such small, sparse, surface arrays so that they are capable of robustly identifying small‐magnitude events whose signal‐to‐noise ratios may be close to 1. To do this, we develop a beamforming‐and‐stacking approach, computing running short‐term/long‐term average functions for each component of each recorded trace (), time‐shifting these functions by the expected travel times for a given location, and performing a stack.

We assess the effectiveness of this approach with a case study using data from a small surface array that recorded a multi‐well, multi‐stage hydraulic fracture stimulation in Oklahoma over a period of 8 days. As a comparison, we initially used a conventional event‐detection algorithm to identify events, finding a total of 17 events. In contrast, the beamforming‐and‐stacking approach identified a total of 155 events during this period (including the 17 events detected by the conventional method). The events that were not detected by the conventional algorithm had low‐signal‐to‐noise ratios to the extent that, in some cases, they would be unlikely to be identified even by manual analysis of the seismograms. We conclude that this approach is capable of improving the detection thresholds of small, sparse arrays and thus can be used to maximise the information generated when deployed to monitor industrial sites.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12498
2017-03-22
2020-07-10
Loading full text...

Full text loading...

References

  1. AllenR.V.1978. Automatic earthquake recognition and timing from single traces. Bulletin of the Seismological Society of America68, 1521–1532.
    [Google Scholar]
  2. BaptieB.2012. Earthquake Monitoring 2011/2012. British Geological Survey Commissioned Report OR/12/092.
  3. BC Oil and Gas Commission
    BC Oil and Gas Commission . 2012. Investigation of Observed Seismicity in the Horn River Basin. Accessed from http://www.bcogc.ca/node/8046/download on 23 July 2015.
  4. BC Oil and Gas Commission
    BC Oil and Gas Commission . 2014. Investigation of Observed Seismicity in the Montney Trend. Accessed from https://www.bcogc.ca/node/12291/download on 23 July 2015.
  5. ChambersK., KendallJ.‐M., Brandsberg‐DahlS. and RuedaJ.2010a. Testing the ability of surface arrays to monitor microseismic activity. Geophysical Prospecting58, 821–830.
    [Google Scholar]
  6. ChambersK., KendallJ.‐M. and BarkvedO.2010b. Investigation of induced microseismicity at Valhall using the Life of Field Seismic array. The Leading Edge29, 290–295.
    [Google Scholar]
  7. ClarkeH., EisnerL., StylesP. and TurnerP.2014. Felt seismicity associated with shale gas hydraulic fracturing: the first documented example in Europe. Geophysical Research Letters41, 8308–8314.
    [Google Scholar]
  8. DaroldA., HollandA.A., ChenC. and YoungbloodA.2014. Preliminary analysis of seismicity near Eagleton 1‐29, Carter County, July 2014. Oklahoma Geological Society Open File Report, OF2‐2014.
  9. DuncanP.M. and EisnerL.2010. Reservoir characterization using surface microseismic monitoring. Geophysics75, A139–A146.
    [Google Scholar]
  10. EisnerL., ZhangY., DuncanP., MuellerM.C., ThorntonM.P. and GeiD.2011. Effective VTI anisotropy for consistent monitoring of microseismic events. The Leading Edge30, 772–776.
    [Google Scholar]
  11. GrechkaV.2015. Tilted TI models in surface microseismic monitoring. Geophysics80, WC11–WC23.
    [Google Scholar]
  12. GreenC.A., StylesP. and BaptieB.J.2012. Preese Hall shale gas fracturing, review and recommendations for induced seismic mitigation. Department of Energy and Climate Change. Accessed from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/15745/5075-preese-hall-shale-gas-fracturing-review.pdf on 23 July 2015.
  13. GrigoliF., CescaS., AmorosoO., EmoloA., ZolloA. and DahmT.2014. Automated seismic event location by waveform coherence analysis. Geophysical Journal International196, 1742–1753.
    [Google Scholar]
  14. GuestW.S. and KendallJ.‐M.1993. Modelling waveforms in anisotropic inhomogeneous media using ray and Maslov asymptotic theory: applications to exploration seismology. Canadian Journal of Exploration Geophysics29, 78–92.
    [Google Scholar]
  15. HorlestonA., StorkA., VerdonJ., BairdA., WookeyJ. and KendallM.2013. Seismic monitoring of drilling operations in Balcombe, West Sussex. University of Bristol. Accessed from http://www1.gly.bris.ac.uk/BUMPS/PDFS/BristolBalcombeReport2013.pdf on 23 July 2015.
  16. FomelS.2004. On anelliptic approximations for qP velocities in VTI media. Geophysical Prospecting52, 247–259.
    [Google Scholar]
  17. FribergP.A., Besana‐OstmanG.M. and DrickerI.2014. Characterisation of an earthquake sequence triggered by hydraulic fracturing in Harrison County, Ohio. Seismological Research Letters85, 1295–1307.
    [Google Scholar]
  18. KendallJ.‐M., FisherQ.J., Covey CrumpS., MaddockJ., CarterA., HallS.A.et al. 2007. Seismic anisotropy as an indicator of reservoir quality in siliciclastic rocks. In: Structurally Complex Reservoirs (eds S.J.Jolley , D.Barr , J.J.Walsh and R.J.Knipe ). Geological Society of London Special Publications292, 123–136.
    [Google Scholar]
  19. LarsenS. and SchultzC.A.1995. ELAS3D: 2D/3D elastic finite‐difference wave propagation code. Technical Report UCRL‐MA‐121792, 1–19.
  20. LomaxA., SatrianoC. and VassalloM.2012. Automatic picker developments and optimization: FilterPicker—A robust, broadband picker for real‐time seismic monitoring and earthquake early warning. Seismological Research Letters83, 531–540.
    [Google Scholar]
  21. MaxwellS.C., RutledgeJ., JonesR. and FehlerM.2010. Petroleum reservoir characterization using downhole microseismic monitoring. Geophysics75, 75A129–75A137.
    [Google Scholar]
  22. National Research Council
    National Research Council . 2013. Induced Seismicity Potential in Energy Technologies. Washington, DC: The National Academies Press.
    [Google Scholar]
  23. OttemöllerL. and SargeantS.2013. A local magnitude scale ML for the United Kingdom. Bulletin of the Seismological Society of America103, 2884–2893.
    [Google Scholar]
  24. SambridgeM.1999a. Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space. Geophysical Journal International138, 479–494.
    [Google Scholar]
  25. SambridgeM.1999b. Geophysical inversion with a neighbourhood algorithm—II. Appraising the ensemble. Geophysical Journal International138, 727–746.
    [Google Scholar]
  26. SatrianoC., EliaL., MartinoC., LancieriM., ZolloA. and IannacconeG.2011. PRESTo, the earthquake early‐warning system for southern Italy: concepts, capabilities and future perspectives. Soil Dynamics and Earthquake Engineering31, 137–153.
    [Google Scholar]
  27. SchulzR., MeiS., PanaD., SternV., GuY.J., KimA.et al. 2015. The Cardston earthquake swarm and hydraulic fracturing of the Exshaw Formation (Alberta Bakken play). Bulletin of the Seismological Society of America105, 2871–2884.
    [Google Scholar]
  28. SkoumalR.J., BrudzinskiM.R. and CurrieB.S.2015. Induced earthquakes during hydraulic fracturing in Poland Township, Ohio. Bulletin of the Seismological Society of America105, 189–197.
    [Google Scholar]
  29. StorkA.L., VerdonJ.P. and KendallJ.‐M.2014. The robustness of seismic moment and magnitudes estimated using spectral analysis. Geophysical Prospecting62, 862–878.
    [Google Scholar]
  30. UtheimT., HavskovJ., OzyaziciogluM., RodriguezJ. and TalaveraE.2014. RTQUAKE, A real‐time earthquake detection system integrated with SEISAN. Seismological Research Letters85, 735–742.
    [Google Scholar]
  31. VerdonJ.P. and KendallJ.‐M.2015. Written Evidence submitted to the Commons Select Environmental Audit Committee Inquiry on the Environmental Risks of Fracking. FRA0022. Accessed from http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/environmental-audit-committee/environmental-risks-of-fracking/written/17012.pdf on 23 July 2015.
  32. WangZ. and KrupnickA.2013. A retrospective review of shale gas development in the United States: What lead to the boom? Resources for the Future DP12–13.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12498
Loading
/content/journals/10.1111/1365-2478.12498
Loading

Data & Media loading...

  • Article Type: Research Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error