The development of unconventional reservoirs has received tremendous attention from energy companies in recent years. Due to the low permeability nature of these resources, a hydraulic fracturing is often applied to stimulate the near-well region to enable economic production. The injection pressure, as it propagates, creates fractures that generate microseismic events. The monitoring of such events has become an important tool to better understand hydraulic fracture geometry, to estimate stimulated reservoir volume, to refine fracture treatment, and to optimize long-term field development. In the estimation of Stimulated Reservoir Volume (SRV) from microseismic data, recent literature highlights the importance of using time and uncertainty to achieve a more accurate estimation, as well as the influence of more complex geometries in understanding the microseismic event cloud. However, the current methods do not take any of these factors into consideration. In this work, we propose two different approaches to estimate the SRV that integrate spatial correlation together with time to obtain more accurate volume estimations. The first method is called alpha-shapes which is a generalization of the well-known shrink-wrap algorithm. The second approach is the density-based region reconstruction which considers the density of the microseismic samples in the space to reconstruct the SRV. The density-based approach uses radial basis function with Gaussian kernels to account for uncertainty in microseismic events. In addition to these two methods, we also developed a sketch-based tool to assist the users in filtering microseismic events that are visibly wrong. We molded these two approaches to allow for direct user changes to the final volume through sketch-based tools, and thus giving the expert the ability to guide the SRV estimation and to create "what-if" scenarios for a better understanding of the microseismic data. We also integrated the developed tools in this work with an interactive tabletop multitouch display to create a collaborative work environment for the experts.


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