Evolution of hydraulic and elastic properties of reservoir rocks due to mineral precipitation in CO 2 geological storage

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Abstract

To investigate the evolution of the hydraulic and elastic properties of reservoir rocks associated with CO 2 mineralization in carbon capture and storage projects, we simulated the mineral precipitation process in a 3D model of natural sandstone, created from micro-CT scanned images, and a 3D model consisting of close-packed beads. Two different mineralization schemes were used: the first calculated pore filling by calcite by applying an advection-reaction equation to the fluid flow velocity field within the pore spaces of the rock models using a lattice Boltzmann simulation, and the second simulated pore filling by increasing the grain radius. We estimated the P-wave and S-wave velocities in the rock models using a dynamic wave propagation simulation and estimated the permeabilities using a lattice Boltzmann simulation. The simulation results showed that the elastic properties of the digital rocks were strongly influenced by mineralization around the grains, whereas their hydraulic properties were influenced by mineralization within the pore body. The elastic and hydraulic properties calculated from our rock models agree with those calculated using conventional rock physics theories, such as effective medium theory and the Kozeny-Carman equation, in terms of crack aspect ratio and tortuosity. Direct simulation using digital rocks enables us to explore the complex relations among the hydraulic and elastic parameters of rocks during CO 2 mineralization. The relationships between permeability and seismic velocity are strongly influenced by mineralization types as well as rock types; nevertheless, permeabilities have a definite relationship with the P-wave to S-wave velocity ratio (Vp/Vs). By applying the rock modeling approach of this study to target reservoirs, permeability evolution due to CO 2 mineralization could be evaluated on the basis of seismic velocities from geophysical monitoring data.

Original languageEnglish
Pages (from-to)84-95
Number of pages12
JournalComputers and Geosciences
Volume126
DOIs
Publication statusPublished - May 2019

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computers in Earth Sciences

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