An integrated study on the sensitivity and uncertainty associated with the evaluation of stimulated reservoir volume (SRV)

This paper combines the microseismic monitoring, hydraulic fracturing simulation and SRV fitting techniques to investigate the inter-relationship of data uncertainties and to provide a possible methodology for guiding the stimulation treatment during reservoir development. The hydraulic fracturing process is simulated to obtain a realistic set of microseismic events that are generated from hydraulic fracture propagation and the pore pressure increase in the matrix. In order to examine the impacts of the uncertainties on the estimated locations of the microseismic events, different levels of noise are added to the first arrival times during the inversion process. Because the microseismic event locations inverted from the first arrival times with noise are very scattered, a denoising procedure based on probability theory is applied to identify and exclude the outliers in the MS event clouds. The discrete bin, 3D Delaunay triangulation and ellipsoid fitting methods are used to compute SRV from the inverted microseismic events with noise. Comparisons are made to examine the sensitivities of the different SRV fitting methods with respect to data noise. The results show that the two discrete bin algorithms are relatively stable, while the MVEE and 3D Delaunay triangulation algorithms are not very robust and can be easily affected by the noise in the observed data.