We have applied Bayesian inference for simultaneous inversion of multiple microseismic data to obtain event locations along with the subsurface velocity model. The traditional method of using a predetermined velocity model for event location may be subject to large uncertainties, particularly if the prior velocity model is poor. Our study indicated that microseismic data can help to construct the velocity model, which is usually a major source of uncertainty in microseismic event locations. The simultaneous inversion eliminates the requirement for an accurate predetermined velocity model in microseismic event location estimation. We estimate the posterior probability density of the velocity model and microseismic event locations with the maximum a posteriori estimation, and the posterior covariance approximation under the Gaussian assumption. This provides an efficient and effective way to quantify the uncertainty of the microseismic location estimation and capture the correlation between the velocity model and microseismic event locations. We have developed successful applications on both synthetic examples and real data from the Newberry enhanced geothermal system. Comparisons with location results based on a traditional predetermined velocity model method demonstrated that we can construct a reliable effective velocity model using only microseismic data and determine microseismic event locations without prior knowledge of the velocity model.

We have studied microseismic data acquired from a geophone array deployed in the horizontal section of a well drilled in the Marcellus Shale near Susquehanna County, Pennsylvania. Head waves were used to improve event location accuracy as a substitution for the traditional P-wave polarization method. We identified that resonances due to poor geophone-toborehole coupling hinder arrival-time picking and contaminate the microseismic data spectrum. The traditional method had substantially greater uncertainty in our data due to the large uncertainty in P-wave polarization direction estimation. We also identified the existence of prominent head waves in some of the data. These head waves are refractions from the interface between the Marcellus Shale and the underlying Onondaga Formation. The source location accuracy of the microseismic events can be significantly improved by using the P-, S-wave direct arrival times and the head wave arrival times. Based on the improvement, we have developed a new acquisition geometry and strategy that uses head waves to improve event location accuracy and reduce acquisition cost in situations such as the one encountered in our study.

In this paper, we show that the location of microseismic events can be significantly improved by incorporating information on head wave arrival time. The traditional method of using direct arrival times and P-wave polarizations leads to increased error due to the large uncertainty in polarization. We integrated head wave arrival time to P-and S-wave arrival time to achieve better resolution in microseismic event location. To this end, we developed a Bayesian inference framework for joint event location and velocity model calibration. The developed method was applied for both microseismic event as well as perforation shot location in a project in Marcellus shale. Comparison with location results provided by contractor shows that the developed method can effectively improve the accuracy of microseismic event location. Based on the improvement, we propose a new acquisition geometry and strategy to reduce microseismic monitoring cost and improve event location accuracy.

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