Abstract

In this presentation, a novel rock fracability index is proposed that is based on a brittle mineral weight percentage, a brittleness index and a natural fracture intensity, and is shown to be a good indicator of whether a rock is prone to be fractured. When MS geophones cannot sufficiently cover hydraulic stimulation areas, the uncertainty of MS data increases, especially of MS events that are far from a sensor array should be considered. The rock mechanical model generated by a sequential Gaussian simulation is applied as a guide to tune signal filter criteria, identify a confidence level of a MS event, and remove or preserve some MS events. Although the unexpected fault will reduce the reliability of a generated RFI model, the accuracy of an estimated SRV is still significantly enhanced after the implementation of this approach.