Summary

The complexity of rock fabric (e.g., spatial distribution of pores and rock components) in organic-rich mudrocks has a significant impact on rock properties, such as electrical resistivity, and hence on estimates of hydrocarbon reserves. Actual spatial distributions of pores and rock components are not accounted for in conventional petrophysical models. For instance, clays are typically considered to be distributed in laminated, dispersed, or structural forms. However, clay distribution in organic-rich mudrocks might not fit any of these categories. Furthermore, conventional models do not incorporate conductive components—other than brine and clay minerals—in interpretation of resistivity measurements. We introduce a new resistivity-based workflow that quantitatively assimilates the type and the spatial distribution of all conductive components to improve reserves evaluation. To achieve this objective, we (a) introduce a directional connectivity parameter that quantifies the influence of clay, kerogen, pyrite, and brine network connectivity on electrical resistivity; (b) develop a new method that relates electrical resistivity to directional connectivity of all the conductive components; and (c) improve the estimates of water saturation in organic-rich mudrocks.

The new resistivity method incorporates directional connectivity of each conductive component of the rock. This parameter is a function of rock-fabric features such as directional tortuosity and constriction factor of each component. The aforementioned features are quantified using focused-ion-beam scanning-electron-microscope (FIB-SEM) 3D pore-scale images. We apply a semianalytical streamline model to estimate the network connectivity of the conductive components, which will be inputs to the new workflow. We successfully applied the new method to pore-scale rock samples from one organic-rich mudrock formation and several synthetic cases. The results of numerical simulations showed that the sensitivity of electrical conductivity to spatial distribution of conductive components can be significant, depending on their volumetric concentration and electrical properties. Finally, comparison of the new method against a conventional method [i.e., the Waxman-Smits model (Waxman and Smits 1968)] showed that water saturation can be improved by up to 35% when a quantitative and realistic spatial distribution of all the conductive components (e.g., pyrite, clay, and kerogen) is taken into account.

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