We present evolution of elastic and transport properties due to diagenetic effects in realistic pore microstructure using computational rock physics tools. We generate compacted spherical random packs using Granular Dynamics simulation. The output from this process based simulation is gridded at fine scale resolution (5 microns) and used for diagenesis modeling. Different diagenetic methods are employed to recreate the effects of cementation on the initial compacted pack. The microstructures obtained after diagenetic modeling are used for estimating elastic and transport properties. A Finite Element based model is used for elastic properties, while Lattice Boltzmann method is used for estimating permeability. The bulk and shear moduli from the reconstructed microstructures are compared to clean sandstone samples and are found to be reasonably close. We present different diagenetic trends in the elastic, transport and cross-property domains.
The fundamental aim of rock physics is to relate geophysical observations to in-situ rock properties. Conventional rock physics models are based on either empirical relations from laboratory measurements or theoretical models based on idealized microstructures. These models have given important insights to understand physical properties and solve in-situ problems. However, these models are always over-simplified, with regard to the geometry they represent and, at times, with the physical interactions within the geometry. Moreover, the proper microstructural parameters for different properties are usually different, thereby constraining cross-property analyses in many cases. Conventional treatments of effective property estimations differ in the way they characterize microstructure (Kachanov and Sevastianov, 2005), thereby lacking commonality. With the availability of superior computing power, computational rock physics can now simulate different processes in a common but complex pore-geometry to estimate properties. There have been broadly three kinds of approaches to obtain common pore-geometry: 1) Stochastic methods (3D reconstruction) (Keehm, 2003, Bekri et al, 2000, Yeong and Torquato, 1998); 2) Imaging methods (scanned images, SEM images) (Keehm et al, 2003, Coles et al, 1998) and 3) Process-based methods (Bryan et al, 1995, Garcia et al., 2004, Guodong et al., 2004). We construct the pore-geometry by using a granular media simulation, followed by implementation of computational diagenetic methods. The microstructures of the solid and the void part of the rock jointly determine its transport, electric and mechanical properties (Guodong et al., 2005). Analysis of rock microstructures suggests that they change variously due to different geological factors like sedimentological factors (grain size distribution, mineralogy, sorting), stress conditions and diagenesis (quartz overgrowth, mineral alteration, pressure solution). Thus, the understanding of spatial trends in rock microstructures is necessary to understand the relevance of trends and “cross-trends” in transport and elastic properties. Spatial trends in microstructures arise due to processes occurring during deposition like sorting, rearrangements and deformation during compaction, cementation and other diagenetic processes (Garcia et. al., 2004). Physics-based granular dynamics simulation provides us with the capability to simulate these processes and thus gain more insights into the understanding of property trends. Other microstructure construction methods like imaging methods are costly and restrict resolutions of images; the stochastic methods do not provide a realistic background in terms of modeling sedimentation physics and often under-predict connectivity.