Отправлено: 18.08.17 10:40. Заголовок: A New Resistivity-Based Model for Improved Hydrocarbon Saturation Assessment in Clay-Rich Formations

A New Resistivity-Based Model for Improved Hydrocarbon Saturation Assessment in Clay-Rich Formations Using Quantitative Clay Network Geometry and Rock Fabric
AuthorsArtur Posenato Garcia (The University of Texas at Austin) | Archana Jagadisan (The University of Texas at Austin) | Ameneh Rostami (The University of Texas at Austin) | Zoya Heidari (The University of Texas at Austin) Document IDSPWLA-2017-GPublisherSociety of Petrophysicists and Well-Log AnalystsSource SPWLA 58th Annual Logging Symposium, 17-21 June, Oklahoma City, Oklahoma, USA Publication Date2017

ABSTRACT
The importance of the 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, Simandoux, and Indonesia do not take into account spatial distribution and connectivity of clay network. Spatial distribution of 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 such formations. In this paper, we introduce a new resistivity-based model which quantitatively takes into account the actual clay-network geometry and distribution and type of clay minerals. Reliable incorporation of spatial distribution of 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 directional tortuosity and constriction factor of each rock component.

The aforementioned rock fabric features are quantitatively evaluated from the three-dimensional (3D) pore-scale images. We scan core samples from clay-rich formations using a high-resolution micro- Computed Tomography (CT) scanner. Then, we perform trainable segmentation on each set of two-dimensional (2D) raw images to identify different rock components and pores.

The 2D segmented images are then converted into a 3D volume. We apply a semi-analytical streamline model to estimate the network connectivity and tortuosity of the conductive components from the 3D binary images, which will be inputs to the introduced model.

We successfully applied the introduced model in several synthetic rock samples as well as in actual clay-rich rock samples including a shaly formation and a mudrock. The electrical conductivity, estimated from numerical simulations, was in agreement with the resistivity estimates from the new model.

Comparison of the results against conventional methods showed that saturation estimates were improved by up to 50% in more than 60% of the samples after quantitatively taking into account spatial distribution of 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.

https://www.onepetro.org/conference-paper/SPWLA-2017-G