MR14A-02:
Experimental Diagenesis and 3D Printing of Evolving Carbonate Microstructures

Monday, 15 December 2014: 4:15 PM
Tiziana Vanorio, SRPL - Stanford Rock Physics Laboratory, Stanford University, Geophysics Department, Stanford, CA, United States
Abstract:
Understanding how rock microstructures and, in turn, the spatial distribution of the properties of the rock skeleton (porosity, permeability, and elastic properties) evolve because of time-variant, thermo-chemo-mechanical processes is fundamental to decipher changes in the earth’s crust due to rock-fluid interactions using remote geophysical monitoring methods. Laboratory experiments undoubtedly play a vital role in understanding the underlying basic rules that are needed to inform both simulations and modeling. Nevertheless, capturing coupled chemo-mechanical processes experimentally is a very challenging problem because as pore space deforms chemo-mechanically, the fluid reacts and flows through a deforming pore space. The result is that as much as we strive to achieve controlled conditions in laboratory experiments, it is extremely difficult to control for all of the possible responses of the highly heterogeneous pore network. To overcome such a limitation, we often resort to the fabrication of rock samples in the laboratory. Nevertheless, analogs are not rocks. This level of complexity requires an approach that advances beyond the limitations of each method, be it experimental or computational. I present an approach that takes advantage of the favorable aspects of experimental diagenesis, multi-scale imaging techniques (from pore scale to 3D rock volumes) and 3D printed models of varying carbonate microstructures. This approach allows us to study the evolution of natural pore network geometries from diagenesis experiments, use the basic rules of the evolving microstructures to drive the digital change of the pore network of the printed models in a well-controlled fashion as much possible in the analog experiments, and then iteratively measure the properties of the printed models at the scale of the laboratory. This integration can help make sense of the trackless evolution of properties in apparently scattered datasets such as those characterizing carbonate rocks.