History Matching an Unconventional Reservoir with a Complex Fracture Network

Abstract Multistage hydraulic fracturing of a horizontal well in an unconventional reservoir tends to induce a complex fracture network (CFN) which is challenging to characterize by conventional methods. In this work, we develop a fracture characterization workflow to estimate the geometric configuration and fracture properties of a CFN by assimilating microseismic event data and production data, sequentially. A novel stochastic fractal model, that is consistent with rock physics and outcrop observations, is developed in order to generate realizations of the complex fracture network. In the first stage of the two-stage assisted history matching workflow, we estimate the parameters of the stochastic fractal model (fracture intensity, average fracture length, orientation and fracture distribution) by using a genetic algorithm to history match data for the locations of microseismic events. In the second stage, the production data from the shale reservoir are assimilated by the ES-MDA algorithm to estimate the stimulated reservoir volume (SRV) and its average permeability, fracture permeability, aperture and porosity. In the unconventional shale gas reservoir simulator used as the forward model, large-scale fractures are modeled via the embedded discrete fracture model (EDFM) and a dual-porosity, dual-permeability (DP-DK) model is used for modeling the SRV and small scale fractures. The simulator includes Knudsen diffusion and the Langmuir adsorption/desorption model. For validation, we consider a synthetic shale gas reservoir with a horizontal well that has been stimulated by multistage hydraulic fracturing. A particular realization of the variables that describe the reservoir model is used to generate observed data for microseismic events and production rates. The parameters to be adjusted to match the observed microseismic events are the expected values of the length, orientation and intensity of the distribution of the natural fractures and the fractal pattern. Results show that we obtain good estimates of the expected value of natural fracture length, orientation, intensity and fracture distribution by history matching observations of locations of microseismic events. These estimates provide an updated stochastic fractal model for the configuration of CFN. The history-matched fractal model is used to generate an ensemble of fracture distributions consistent with microseismic data as candidate fracture configurations when estimating fracture properties by matching production data. We obtain much better history matches, future performance predictions, estimates of stimulated reservoir volume and its average permeability and estimates of fracture permeability, porosity and aperture when we match both microseismic and production data than we only match production data. When both seismic and production data are matched for synthetic cases and parameters are properly scaled, the true values of parameters and reservoir performance predictions are within the P25-P75 confidence intervals calculated from the ensemble of history matched models in virtually all cases. In practice, the proper characterization of the CFN and reservoir properties should be useful for placing new wells and designing fracture treatments.