Microseismic Without Dots - Probabilistic Interpretation and Integration of Microseismic Surveys
- Ulrich Zimmer (Shell)
- Document ID
- Unconventional Resources Technology Conference
- SPE/AAPG/SEG Unconventional Resources Technology Conference, 24-26 July, Austin, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2017. Unconventional Resources Technology Conference
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- 111 since 2007
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The interpretation of microseismic event distributions largely relies on the point distribution of the events in space and time. Although it is often attempted to minimize the individual location uncertainty of each event, the remaining uncertainty is ignored during the interpretation. The remaining location uncertainty leads to a random scatter that increases the apparent dimensions of the overall event distribution. Although in general it is not possible to determine the true location of an individual event, in cases where the uncertainty is truly random, the effect of the random scatter can be accounted for in the interpretation. One attempt would be to use event densities instead of individual events in the interpretation. But that does not account for different location uncertainties of the events and already assumes models that generate constant event densities. By replacing the event locations with their probability density functions, even non-linear uncertainty functions can be displayed. Once all information is represented by PDFs, Bayes theorem can be effectively used to integrate this information consistently. It also allows for the incorporation of subjective and objective information. The methodology can be applied to the estimation of realistic uncertainties for individual event locations as well as the interpretation of event distributions. In addition, the application of this methodology is not limited to the integration and interpretation of microseismic data but applicable to a wide range of information.
Microseismic monitoring has been used extensively to estimate the geometry and properties of hydraulically induced fracture networks. Almost always these estimates use the location of the microseismic events in space and time. Sometimes additional event attributes are used, e.g. magnitude, fault plane solution, moment tensor attributes, but the point location of the event almost always is a critical part in the interpretation (e.g. Cipolla et al., 2011).
It is well known that the accuracy of the event locations and its interpretation greatly depends on the quality and accuracy of the input parameters such as signal-to-noise ratio of the microseismic signal and the applied velocity model (e.g. Maxwell, 2014, Zimmer, 2011a, Zimmer, 2011b, Poliannikov, 2011, Poliannikov, 2015). In addition, other parameters such as the quality of the calibration data, the amount of information on the velocity model and the accuracy of the wellbore deviation surveys impact the achievable location accuracy. In most attempts of quantifying the location uncertainty only a small number of the sources of uncertainty are accounted for. And even if the uncertainty is quantified, the impact is usually linearized either explicitly or implicitly through tools like principal component analysis. This results in the uncertainty being quantified as an ellipsoid with the event location at its center.
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