Natural Fracture Characterization by Source Mechanism Estimation and Semi-Stochastic Generation of Discrete Fracture Networks Using Microseismic and Core Data

Abstract

The overall goal of this study is to generate discrete fracture networks using microseismic and core data from a natural fractured reservoir that has been hydraulically stimulated. To improve fracture characterization, a methodology based on source mechanisms estimations is developed with the aim to distinguish the two natural fracture sets present in the reservoir. Source mechanisms estimation is a geophysical processing technique that can provide orientation and rupture mode of seismic events. An intermediate step, moment tensor inversion, is however needed. The main challenge is that one element of the moment tensor is completely undetermined by the limited azimuthal acquisition coverage; thus, some kind of assumption needs to be considered to complete the missing element. In this work, it is assumed that the microseisms occur mainly as consequence of the natural fractures reactivation, thus source dip and strike are known. For the discrete fracture generation, a semi-stochastic technique is proposed, which combines information from the source mechanisms estimations, the microseismic report and the core analysis report.

The two main contributions of this work are that a methodology to improve natural fracture characterization is proposed, which incorporates micro seismic data to distinguish the fracture sets known to be present, and that a semi-stochastic technique to generate discrete fracture networks, which combines microseismic information and core data, is proposed and implemented as well.