Locating microseismic events using borehole data

Abstract

ABSTRACTConstraining microseismic hypocentres in and around hydrocarbon reservoirs and their overburdens is essential for the monitoring of deformation related to hydraulic fracturing, production and injection and the assessment of reservoir security for CO2 and wastewater storage. Microseismic monitoring in hydrocarbon reservoirs can be achieved via a variety of surface and subsurface acquisition geometries. In this study we use data from a single, subsurface, vertical array of sensors. We test an existing technique that uses a 1D velocity model to constrain locations by minimizing differential S‐to‐P arrival times for individual sensors. We show that small errors in either arrival time picks or the velocity model can lead to large errors in depth, especially near velocity model discontinuities where events tend to cluster. To address this issue we develop two methods that use all available arrival times simultaneously in the inversion, thus maximizing the number of potential constraints from to N, where N is the number of phase picks. The first approach minimizes all available arrival time pairs whilst the second approach, the equal distance time (EDT) method defines the hypocentre as the point where the maximum number of arrival time surfaces intersect. We test and compare the new location procedures with locations using differential S‐to‐P times at each individual sensor on a microseismic data set recorded by a vertical array of sensors at the Ekofisk reservoir in the North Sea. Specifically, we test each procedure's sensitivity to perturbations in measured arrival times and the velocity model using Monte Carlo analysis. In general, location uncertainties increase with increasing raypath length. We show that errors in velocity model estimates are the most significant source of uncertainty in source location with these experiments. Our tests show that hypocentres determined by the new procedures are less sensitive to erroneous measurements and velocity model uncertainties thus reducing the potential for misinterpretation of the results.