The effectiveness of hydraulic fracture stimulation in low-permeability reservoirs was evaluated by mapping microseismic events related to rock fracturing. The geometry of stage by stage event point sets were used to infer fracture orientation, particularly in the case where events line up along an azimuth, or have a planar distribution in three dimensions. Locations of microseismic events may have a higher degree of uncertainty when there is a low signal-to-noise ratio (either due to low magnitude or to propagation effects). Low signal-to-noise events are not as accurately located in the reservoir, or may fall below the detectability limit, so that the extent of fracture stimulated reservoir may be underestimated. In the Bakken Formation of the Williston Basin, we combined geologic analysis with process-based and stochastic fracture modeling to build multiple possible discrete fracture network (DFN) model realizations. We then integrated the geologic model with production data and numerical simulation to evaluate the impact on estimated ultimate recovery (EUR). We tested assumptions used to create the DFN model to determine their impact on dynamic calibration of the simulation model, and their impact on predictions of EUR. Comparison of simulation results, using fracture flow properties generated from two different calibrated DFN scenarios, showed a 16% difference in amount of oil ultimately produced from the well. The amount of produced water was strongly impacted by the geometry of the DFN model. The character of the DFN significantly impacts the relative amounts of fluids produced. Monitoring water cut with production can validate the appropriate DFN scenario, and provide critical information for the optimal method for well production. The results indicated that simulation of enhanced permeability using induced microseismicity to constrain a fracture flow property model is an effective way to evaluate the performance of reservoirs stimulated by hydraulic fracture treatments.
Assessment of uncertainty in discrete fracture network modeling using probabilistic distribution method