|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Summary In this paper we present a method for identifying intervals in shale oil reservoirs that contain moveable hydrocarbons with a novel geochemical productivity index (PI), . This index merges three important rock properties that must always be considered for sound shale oil reservoir characterization: vitrinite reflectance (), oil–saturation index (OSI), and free–water porosity (). Integrating this index with other petrophysical properties and geomechanical parameters defines intervals with high moveable oil content. Shale oil is both a source rock and an unconventional reservoir rock. Hence, it is critical to know both its organic–matter (OM) maturity and its oil/water flow capacity. The introduced considered these features simultaneously; maturity was evaluated by discretizing from 0 to 1, depending on whether the rock was immature or not; free oil flow capacity modeled the normalizing between 0 and 1 on the basis of results from the Rock-Eval VI pyrolysis (REP) obtained in the laboratory or by electric logs; and water flow capacity was estimated from , obtained using a nuclear–magnetic–resonance (NMR) log, which was transformed into an index between 0 and 1. Flow oil capacity was defined as the amount of moveable oil that exceeded the sorption capacity of the source rock. Using the is explained with real data from a vertical well that penetrates several stacked shale oil reservoirs. However, the same approach can be used in any other type of wellbore architecture (i.e., deviated, horizontal, geosteered). Initially, a correlation between vertical depth and was developed. This resulted in a continuous OM–maturity curve along the well section. Next, OSI was simulated by using a bin porosity from an NMR log, where was between 33 and 80 milliseconds and was correlated with OSI data from REP. As a result, a good match between the simulated and the real OSI data was achieved. Similar to OSI, was also calculated from the NMR log, but it used a bin porosity when was greater than 80 milliseconds. These three parameters were then transformed to fractional indices, which were combined into a unique index, . When the index was greater than 0.66, there was a good chance that the three conditions mentioned above would be met. For the example well considered in this study, it was found that almost 30% of the total vertical section had good moveable oil potential. This corresponded to 10 intervals in the well. The key novelty of this paper is that we have developed a continuous curve of an index that is easy to use and is powerful for identifying intervals with moveable hydrocarbon potential. This is true even in those intervals without laboratory data because of the continuity of the curve, in addition to the integrated criteria that are usually applied independently. The Igp index is a simple–to–use approach. However, because it is a new method, an explorationist should validate it against real oil production information.