--> Abstract: Uncertainty in Surface Microseismic Monitoring, by Mueller, Michael C. and Thornton, Michael; #90166 (2013)
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Uncertainty in Surface Microseismic Monitoring

Mueller, Previous HitMichaelNext Hit C.1 and Thornton, Previous HitMichaelTop
1[email protected]

Uncertainty in a migration based approach to surface microseismic monitoring occurs in two ways: uncertainty in the validity in detected event and uncertainty in the estimated position of the event. Synthetic modeling and comparison to case studies show that sign-to-noise-ratio (SNR) is a key indicator of both types of the uncertainties.

Reliability, in terms of the ability to detect the complete set of events is a nearly binary function of SNR. Events with SNR above a threshold of 2-3 are readily detected, while events with SNR below the threshold are missed. Positional uncertainties likewise are driven by SNR. While vertical positional uncertainty is more sensitive to noise, both horizontal and vertical uncertainties decrease rapidly with increasing SNR.

While SNR can be used to infer the relative likelihood that given event is real, false-positives will occur. Discriminating the true positive from the false will require additional information beyond SNR.

While synthetic modeling is useful in assessing the performance characteristic of the imaging method, a number of simplifying assumptions were made that differ from actual application of the method. First, our model assumed that travel-times were known exactly. In practice, velocity and static corrections must be estimated from calibration shots (sources at known locations in the subsurface). While travel time errors are most likely to decrease the SNR after migration, long period errors in travel times could cause spurious focusing and add uncertainty. Secondly, the model assumed the additive noise was Gaussian. While this is a reasonable first approximation, it does not take into account coherent noises, which are ubiquitous in surface microseismic monitoring. Appropriate preprocessing can reduce the impact of coherent noise, but residual coherent noise will trigger false-alarms. Moreover, the number of false-alarms rejected in the event localization step will likely not be so high, as coherency in the noise will imply some additional consistency among triggers not seen in the model.

 

AAPG Search and Discovery Article #90166©2013 AAPG International Conference & Exhibition, Cartagena, Colombia, 8-11 September 2013