1887

Abstract

Summary

Precise microseismic event location is necessary to correctly assess the hydraulic stimulation effectiveness. In order to obtain proper event locations reliable location uncertainty analysis have to be performed. Location uncertainty computed with use of the Bayesian inversion is efficiently characterised by probability density function (pdf). We present the Bayesian approach for location of microseismic events recorded by a single vertical receiver array during hydraulic fracturing in northern Poland. We compare a cumulative probability density function for the location of all events with a standard representation of events as maximum likelihood points (‘dots in the box’). The discussion is extended by considering seismic moment and moment magnitude as the weighting parameters for pdf visualization. Bayesian approach is more intuitive and easier to directly transfer into reservoir engineering parameters, like the Stimulated Reservoir Volume, or to be used in Discrete Fracture Network modelling. The obtained cumulative location pdf clearly points out highly fractured zones and shows directly the scale and the distribution of the location uncertainty. Particularly in case of the studied acquisition geometry the shape of the pdf distribution cannot be explained in standard way in terms of horizontal and vertical errors.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201600909
2016-05-31
2020-08-04
Loading full text...

Full text loading...

References

  1. Chambers, K.
    [2014] Computing location uncertainty for imaged sources, Fifth EAGE Passive Seismic Workshop, Extended Abstracts, PSP19.
    [Google Scholar]
  2. Eisner, L., Duncan, P., Heigl, W. M. and Keller, W. R.
    [2009] Uncertainties in passive seismic monitoring, The Leading Edge, 28(6), 648–655.
    [Google Scholar]
  3. Grechka, V., De La Pena, A., Schisselé-Rebel, E., Auger, E. and Roux, P-F.
    [2015] Relative location of microseismicity, Geophysics, 80(6), WC1–WC9.
    [Google Scholar]
  4. Mayerhofer, M. J., Lolon, E. P., WarpinskiN. R., CipollaC. L., Walser, D. and and Rightmire, C. M.
    [2010] What is stimulated reservoir volume?, SPE Production and Operations, 25, 89–98.
    [Google Scholar]
  5. Qin, G., Chen., R., Gong, B. and Xu, B.
    [2012] Data-driven Monte Carlo Simulations in Estimating the Stimulated Reservoir Volume (SRV) by Hydraulic Fracturing Treatments, 74th EAGE Conference & Exhibition incorporating SPE EUROPEC, Extended Abstracts, H013.
    [Google Scholar]
  6. Tarantola, A.
    [2005] Inverse problem theory and methods for model parameter estimation, Society of Industrial Applied Mathematics, Philadelphia.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201600909
Loading
/content/papers/10.3997/2214-4609.201600909
Loading

Data & Media loading...

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