The Nanoscale Basis of CO₂ Trapping for Geologic Storage

Abstract

Carbon capture and storage (CCS) is likely to be a critical technology to achieve large reductions in global carbon emissions over the next century. Research on the subsurface storage of CO2 is aimed at reducing uncertainties in the efficacy of CO2 storage in sedimentary rock formations. Three key parameters that have a nanoscale basis and that contribute uncertainty to predictions of CO2 trapping are the vertical permeability kv of seals, the residual CO2 saturation Sg,r in reservoir rocks, and the reactive surface area ar of silicate minerals. This review summarizes recent progress and identifies outstanding research needs in these areas. Available data suggest that the permeability of shale and mudstone seals is heavily dependent on clay fraction and can be extremely low even in the presence of fractures. Investigations of residual CO2 trapping indicate that CO2-induced alteration in the wettability of mineral surfaces may significantly influence Sg,r. Ultimately, the rate and extent of CO2 conversion to mineral phases are uncertain due to a poor understanding of the kinetics of slow reactions between minerals and fluids. Rapidly improving characterization techniques using X-rays and neutrons, and computing capability for simulating chemical interactions, provide promise for important advances.

Sealing Shales versus Brittle Shales: A Sharp Threshold in the Material Properties and Energy Technology Uses of Fine-Grained Sedimentary Rocks

“…In particular, the log k v values of fine-grained rocks are well-known to be much more sensitive to X clay than to ϕ or other variables. 15 Existing data reveal that k v decreases by 6 orders of magnitude as X clay increases from 0 to 35% and by an additional 1.5 orders of magnitude as X clay increases from 35 to 70% ( Figure 3B). The possible threshold at X clay ∼ 1 / 3 in Figure 3B is even more sharply pronounced in measurements of the permeability of homogeneous quartz/clay mixtures, 16 but it is absent in fieldscale reconstructions of fault permeability versus fault clay content, 17,18 a difference attributed to the scale dependence of permeability in heterogeneous porous media.…”

“…Porosity and permeability are key variables in basin modeling, 2 in geomechanical predictions of caprock failure in CCS, 20 and in predictions of the sealing properties of faults, 18 but most extant models assume that ϕ and k v are invariant with X clay . 15 The few models that correctly recognize the impact of X clay on k v do not account for the existence of a possible threshold at X clay ∼ 1 / 3 . 2,18 The transition at X clay ∼ 1 / 3 in the unconfined compressive strength of shale and mudstone ( Figure 3A) and, perhaps, in other material properties (Figures 3B,C) is reflected in a remarkably sharp mineralogical demarcation between finegrained sedimentary rocks that are exploited for hydrocarbons and those that are used or considered for use in CCS or radioactive waste storage.…”

Equilibrium partial pressure of CO2 in Callovian–Oxfordian argillite as a function of relative humidity: Experiments and modelling

“…This proposed experiment should also provide interesting results regarding the alteration of clayey cap rock by CO 2 in the context of geological carbon sequestration (e.g. Bourg et al, 2015).…”

Evaluation of accessible mineral surface areas for improved prediction of mineral reaction rates in porous media

“…Currently there is no protocol for determining mineral reactive surface areas and they are often estimated using one of many specific surface area (SSA) or effective surface area (ESA) approximations , Bourg et al, 2015, Beckingham et al, 2016. SSA approximations include geometric surface areas calculated assuming geometry and average grain diameter, image perimeter based surface area, and laboratory measured BET surface area as summarized in Beckingham et al (2016).…”

“…These are typically calculated by applying a scaling factor (SF) to a specific surface area (Beckingham et al, 2016). This range of approaches to surface area estimation results in multiple orders of magnitude variation in RSA values (Bourg et al, 2015, Beckingham et al, 2016. In reactive transport simulations, variations in mineral reactive surfaces areas result not only in discrepancies in mineral reaction rates, but porosity and reactive plume evolution as well (Gaus et al, 2008, Atchley et al, 2014.…”