Microseismic Event Detection and Characterization Using Sparse Surface Networks

Abstract

In certain shale reservoirs in western Canada and elsewhere in North America, microseismicity associated with hydraulic fracturing can be detected and characterized by sparse surface networks of high-quality three-component instruments. Here we provide case-study examples of sparse surface networks, initially deployed for induced seismic monitoring, that generate rich microseismic data sets in the Duvernay and Montney shale plays. In particular, we show that in environments with favorable surface noise levels, high in-situ stress regime and low anelastic attenuation, such networks can detect microseismic events down to magnitude -1.0 within the hydrofracture-stimulated volume, along with induced seismicity on proximate faults. We utilize three-component detection of compressional and shear waves along with waveform template matching event detection techniques to lower the magnitude of completeness and maximize the recorded event catalog. By locating events using 3D velocity data, grid-search and relative location techniques, the precision and clustering of solutions is optimized for first-pass evaluation of stimulated volume boundaries and delineation of activated geological structures. Furthermore, use of high-quality instruments, including broadband seismometers, allows for unsaturated estimates of event magnitude across the full range of detections, leading to unbiased analysis of magnitude-frequency distributions (b-values) and cumulative radiated seismic energy. For larger magnitude events with sufficient signal to noise ratio, displacement spectral fitting is used to compute source parameters. Larger magnitude events also allow for individual or composite fault plane solutions to be derived and used to perform principal stress axes inversion as well as determine fracture plane orientations. Although surface noise typically interferes with detection of events below magnitude -1.0, spatiotemporal correlations between events above -1.0 and hydrofracture parameters (stage time/location, treatment pressure, slurry rate, proppant volume, etc) can provide operators with metrics for assessing the effectiveness of each frac stage. Consequently, sparse surface networks of high-quality seismic instruments can generate rich and informative data sets that make them a scalable, practical and cost-effective hydraulic fracture imaging option.

AAPG Datapages/Search and Discovery Article #90259 ©2016 AAPG Annual Convention and Exhibition, Calgary, Alberta, Canada, June 19-22, 2016