Effect of supercritical CO₂ on carbonates: Savonnières sample case study

Abstract

CO2 geosequestration is an efficient way to reduce greenhouse gas emissions into the atmosphere. Carbonate rock formations are one of the possible targets for CO2 sequestration due to their relative abundance and ability to serve as a natural trapping reservoir. The injected supercritical CO2 can change properties of the reservoir rocks such as porosity, permeability, tortuosity, and specific surface area due to dissolution and precipitation processes. This, in turn, affects the reservoir characteristics, i.e., their elastic properties, storage capacity, stability, etc. The tremendous progresses made recently in both microcomputed X‐ray tomography and high‐performance computing make numerical simulation of physical processes on actual rock microstructures feasible. However, carbonate rocks with their extremely complex microstructure and the presence of microporosity that is below the resolution of microcomputed X‐ray tomography scanners require novel, quite specific image processing and numerical simulation approaches. In the current work, we studied the effects of supercritical CO2 injection on microstructure and elastic properties of a Savonnières limestone. We used microtomographic images of two Savonnières samples, i.e., one in its natural state and one after injection and residence of supercritical CO2. A statistical analysis of the microtomographic images showed that the injection of supercritical CO2 led to an increase in porosity and changes of the microstructure, i.e., increase of the average volume of individual pores and decrease in the total number of pores. The CO2 injection/residence also led to an increase in the mean radii of pore throats, an increase in the length of pore network segments, and made the orientation distribution of mesopores more isotropic. Numerical simulations showed that elastic moduli for the sample subjected to supercritical CO2 injection/residence are lower than those for the intact sample.

Pressure‐Dependent Elastic and Transport Properties of Porous and Permeable Rocks: Microstructural Control

“…The measured porosity, permeability, and their pressure dependence compare favorably with the literature data reported for Savonnieres limestone (e.g., Shulakova et al, ) and Berea sandstone (e.g., Pimienta, Borgomano, et al, ). P wave velocities in Berea sandstone as a function of pressure have been reported in the literature (e.g., Mavko & Vanorio, ; Tao et al, ).…”

“…The Savonnieres is a shelly oolitic limestone, upper Jurassic in age, originating from the northeast of France (Shulakova et al, ). It is made of fine, closely woven grains with many vesicles and a calcite content of about ~ 98%.…”

“…A decrease in mechanical strength associated with this dissolution has led to the collapse of the outer shell and creation of microcracks under burial stress (Figures b and c). Although the three images of this rock in Figure are different, these facies are repeated randomly in the rock sample so that the macroscopic (>cm) properties of the sample are homogeneous (Shulakova et al, ). The Savonnieres limestone is characterized by a porosity of ~26% and a gas permeability of ~95 mD (i.e., 0.9 · 10 −13 m 2 ).…”

Evolution of permeability and microstructure of tight carbonates due to numerical simulation of calcite dissolution

“…We plot the hydraulic diameter distributions in Figure a along with the time evolution of the geometric mean and standard deviation (Figures b and c). Over the course of dissolution, there is an inversion of the pore diameter about ~25 μm, which we interpret as conversion narrow pore throats into wider pores, and is consistent with experimental observations [ Shulakova et al , ]. Despite the stagnancy of the porosity and specific surface area of the connected pore geometry (Figures a and b) and unintuitive effective porosity behavior (Figure b), the hydraulic diameter best mimics—at least qualitatively—the permeability evolution.…”

Quantifying Rock Weakening Due to Decreasing Calcite Mineral Content by Numerical Simulations

“…Moreover, the same rock sample can be examined under varying testing conditions or mineral composition, which can be virtually varied [ 48 ]. Present numerical models employ calculation techniques to determine static [ 49 , 50 , 51 ] and dynamic moduli [ 52 ] mainly for reservoir rocks such as sandstones [ 53 , 54 ] and carbonates [ 55 , 56 ], but also for shales [ 57 ]. However, there is still a discrepancy between the achieved numerical estimates of mechanical properties derived from micro-CT images and laboratory data, where, regardless of the numerical approach, the simulated moduli are systematically higher [ 58 , 59 ].…”