Stimulated fractured reservoir DFN models calibrated with microseismic source mechanisms

Methods for constraining discrete fracture network (DFN) models have historically relied on two very different scales for data: large-scale sources from which attributes can be coarsely defined for volume elements of 1000s of cubic meters such as seismic data, or small-scale sources where attributes of individual fractures are measured on a meter scale such as wellbores. Populating reservoir models with wellbore data requires upscaling the measured parameters, and the use of the large scale data types is accompanied by assumptions that can have significant uncertainties. A source of data that fills the gap intermediate to the large and small scale fracture parameters is microseismic data. During reservoir stimulation or production, acquisition of microseismic data with a surface array of geophones laid out in multiple azimuths and offsets (e.g., a star-like pattern above the well, or shallowly buried geophones in a grid like pattern), provides a broad sampling of the focal sphere that can be used to invert microseismic events for the source mechanism. This paper presents examples of DFN models constrained with source mechanisms and the implications for reservoir modeling of these more-highly constrained fracture network models.