科研之友
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摘要
The importance of clay-network conductivity in resistivity-based saturation assessment has been well recognized over the years. The existing shaly sand models are oversimplified by assuming that the clays are present in the rock predominantly as laminated, dispersed, or structural. This assumption, however, is not reliable in many clay-rich formations because, in nature, clay minerals can have complex spatial distributions. Furthermore, the conventional shaly sand resistivity models, such as Waxman-Smits, dual-water, and Simandoux, do not take into account spatial distribution and connectivity of the clay network. Spatial distribution of the clay network can significantly affect resistivity of clay-rich formations and oversimplifying this distribution can lead to huge uncertainties in estimates of water saturation. In this paper, we introduce a new resistivity-based model that quantitatively takes into account the actual clay-network geometry and distribution and type of clay minerals. Reliable incorporation of spatial distribution of the clay network (i.e., not limited to extreme cases of dispersed, layered, and structural) improves reserves evaluation in clay-rich formations with complex clay network structure.
The new resistivity model incorporates directional pore-network connectivity of each conductive component of the rock that forms a percolating network. The directional connectivity is calculated as a function of the volume fractions and rock-fabric features, such as the directional tortuosity and constriction factor of each rock component. The aforementioned rock-fabric features are quantitatively evaluated from three-dimensional (3D) pore-scale images. We scan core samples from clay-rich formations using a high-resolution microcomputed tomography (CT) scanner. We apply a semianalytical streamline model to estimate the network connectivity and tortuosity of the conductive components from the 3D segmented images, which will be inputs to the introduced model.
We successfully applied the introduced model to several synthetic rock samples as well as to actual clay-rich rock samples, including a shaly sand formation and a mudrock. The electrical conductivity estimates from numerical simulations were in agreement with those estimated from the new model. Comparison of the results against conventional methods showed that saturation estimates were relatively improved by at least 50% in more than 50% of the samples after quantitatively taking into account spatial distribution of the clay network. The outcomes of this paper are promising for successful application of the introduced model for improved in-situ assessment of hydrocarbon saturation through assimilating the impacts of rock fabric and spatial distribution of clay networks on electrical resistivity measurements.
- 出版日期2018-6
