Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data

Abstract

The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Because natural fractures are highly heterogeneous and of subseismic scale, integrating petrophysical data (i.e., cores and well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I have developed an integrated and stochastic approach for discrete fracture network modeling with field data experimentation. In the method, I first perform a seismic attribute analysis to highlight the discontinuity in the seismic data. Then, I extrapolate the well-log data that include localized but high-confidence information. By using the fracture intensity model including seismic and well logs, I build the final natural fracture model that can be used as a background model for the subsequent geomechanical analysis such as simulation of hydraulic fractures propagation. As a result, our workflow combining multiscale data in a stochastic approach constructs a reliable natural fracture model. I validate the constructed fracture distribution by its good agreement with the well-log data.