Quantitative interpretation (QI) is routinely applied to conventional reservoirs, with steps such as rock physics modelling (RPM), fluid substitution and inversion into elastic properties being firmly established in the geophysical workflow deployed in the successful development of hydrocarbon reserves (see for example, Simm and Bacon, 2014). For unconventional reservoirs, QI is equally valuable, but has not seen the same take up, perhaps due to lower development and drilling costs associated with land based assets. However, QI for unconventional reservoirs is a cause that we are championing here.
Arguably, the principal parameter in characterizing an unconventional reservoir is the total organic carbon (TOC) concentration (Vernik, 2016), with its quantification a key to screening reserves for potential development. A QI workflow for low permeability shale reservoirs can be exploited to maximize returns and reduce the risk of poor drilling decisions. For such a workflow, we require a good log based TOC determination, followed by rock physics modelling, kerogen substitution and synthetic gather computation, from which characteristics of changes in the signal can be understood. Recasting log points into AI vs SI space then aids interpretation of a final inversion.