Capturing The Complexity Of Geologic Carbon Sequestration In Complex Basalt Formations

Global carbon dioxide (CO2) emissions from energy production declined each year between 2013 and 2016, but this trend ended abruptly in 2017 when CO2 emissions from energy reached 32.6 gigatons (WEO, 2019). This 1.6% increase marked a new record high for energy-based CO2 emissions and further increases are projected well into the future unless comprehensive CO2 management strategies are adopted by the world’s leading energy producers. Moreover, decades of steadily increasing anthropogenic CO2 emissions have resulted in steadily increasing atmospheric CO2 concentrations, which reached 400 ppm in May 2013 and are now above 410 ppm at the Mauna Loa Observatory (SIO, 2019).

Because it is widely accepted that rising atmospheric CO2 concentrations are responsible for increasing land and sea temperature, perhaps the grand challenge of modern society is to find technological, social, and political frameworks for stabilizing anthropogenic CO2 emissions.

Within the portfolio of CO2 stabilization strategies, one promising technological solution is carbon capture and sequestration (CCS), which is the process of capturing CO2 at point-source generators (i.e., power plants, factories, etc.) and isolating it from the atmosphere by disposal into deep geological (>800 m) formations. The efficacy of CCS is dependent on the target reservoir, which is measured by its total storage capacity, as well as its ability to accept CO2 injections and prevent it from leaking.

Traditionally, CCS research has focused on sedimentary geologic formations, such as depleted oil/gas reservoirs, deep saline reservoirs, and unmineable coal seams (Benson & Cole, 2008). These sedimentary systems are generally characterized by highly conductive reservoir rocks overlain by much lower conductivity formations, the latter of which effectively traps CO2 in the disposal reservoir and prevents it from escaping.

While CCS in sedimentary rocks has proven to be effective for CO2 storage, such formations do not exist everywhere. As a result, researchers are now looking into non-traditional geologic formations for CCS. One promising rock type is flood basalt, which forms when molten lava flows across the land surface, cools and then hardens. When this process occurs repeatedly over large geographic regions, the result in a layered assemblage of individual basalt flows (IMAGE) that are called large igneous provinces, e.g., Deccan Traps (India), Siberian Traps (Russia), and Columbia River Basalt Group (USA).

The motivation for CCS in basalt formations is their mineral composition, which releases calcium, magnesium, and iron ions when exposed to mixtures of CO2 and water. As these ions enter the water-CO2 mixture, they react with bicarbonate ions to form carbonate minerals, e.g., calcite. This process permanently immobilizes carbon that originated in the CO2. To further motivate this CCS in basalt formations, laboratory and field experiments show that carbon mineralization occurs rapidly. In fact, the CarbFix pilot test in Iceland demonstrated 95% mineralization in less than two years (Matter et al., 2016).

While these laboratory- and pilot-scale experiments show promising results, the transition to industrial scale implementation is fraught with challenges. For example, flood basalt formations are characterized by pervasive fracture networks, which are potential pathways for CO2 to escape the reservoir before mineralization. In addition, technological limitations preclude our ability to fully map these fracture networks at depths required for CCS (>800 m). This uncertainty is further compounded because the conductivity of basalt fracture networks exhibits an extraordinary variability (Jayne & Pollyea, 2018). The consequences of this uncertainty are that risk assessment models of CCS in basalt formations are prone to considerable error. In our most recent paper, we turn this uncertainty into an asset by implementing stochastic (statistics-based) simulation methods to learn the extent to which flood basalt formations can accept CO2 injections while remaining mechanically stable.

This study is based on the Wallula Basalt Sequestration Pilot Project in south east Washington State, USA. This pilot project was developed by the U.S. Department of Energy to evaluate the potential for basalt sequestration technology. Between 2007 and 2013, the Wallula site was subject to extensive site characterization, which culminated in a small-scale test injection that showed widespread carbon mineralization in post-injection core samples (McGrail et al., 2017). In order to test industrial-scale CCS operations in flood basalt formations, we combined site characterization data from the Wallula borehole with geostatistical analysis of permeability on the Columbia River Plateau to develop a numerical modeling experiment comprising 50 equally probable, heterogeneous reservoirs.

We ran each CO2 injection model for 20 years at the same injection pressure and found that the injection potential from a single injection well ranges from 0.1 to 2 million metric tons (MMT) CO2 per year with a mean value of ~0.8 MMT per year. For reference, a 1,000 MW gas-fired powerplant generates ~1.6 MMT CO2 per year, which suggests that flood basalt formations exhibit excellent injection potential for industrial-scale CCS operations. However, these results also indicate that there is potential for poorly performing injection wells. As a result, our modeling study illustrates the importance of detailed reservoir characterization because reservoir capacity is one of the most important factors when considering the efficacy of CCS.

This study also found that there is a thermal anomaly at the leading edge of the CO2 plume, where temperature increases up to 4oC (Fig. 2). This heating is caused by CO2 dissolving in water, which is an exothermic reaction (releases heat). Because 4oC is a substantial increase in reservoir temperatures, our study strongly implies that temperature could be used as a monitoring tool during CCS injection operations.

Fig 2. Model results from a single simulation after 20 years of a CO2 injection. Here we are showing the changein temperature associated with a CO2 injection. The increase in temperature matches up with the edgeof the CO2. As you can see, the shape of the CO2 plume is not concentric, this is due to the heterogeneous nature of the Columbia River Basalts. Image courtesy Richard Jayne.

In summary, the numerical methods presented in Jayne et al. (2019) yield a wide range of quantitative data that provide important information about reservoir storage capacity and characteristic fluid system behavior. This information can then be harnessed to develop quantitative frameworks for minimizing operational risks and developing site-specific plans for monitoring, measuring, and verifying CO2 isolation. While this study is specific to the Columbia River Basalt Group, our methods can be utilized to bound the uncertainty associated with CCS in other geologic formations.

These findings are described in the article entitled Geologic CO2 sequestration and permeability uncertainty in a highly heterogeneous reservoir, recently published in the International Journal of Greenhouse Gas Control.


  1. Benson, Sally M., and David R. Cole. “CO2 sequestration in deep sedimentary formations.” Elements 4.5 (2008): 325-331.
  2. Jayne, Richard S., and Ryan M. Pollyea. “Permeability correlation structure of the Columbia River Plateau and implications for fluid system architecture in continental large igneous provinces.” Geology 46.8 (2018): 715-718.
  3. Jayne, Richard S., Hao Wu, and Ryan M. Pollyea. “Geologic CO2 sequestration and permeability uncertainty in a highly heterogeneous reservoir.” International Journal of Greenhouse Gas Control 83 (2019): 128-139.
    Matter, Juerg M., et al. “Rapid carbon mineralization for permanent disposal of anthropogenic carbon dioxide emissions.” Science 352.6291 (2016): 1312-1314.
  4. McGrail, Bernard Pete, et al. “Wallula Basalt Pilot Demonstration Project: Post-injection Results and Conclusions.” Energy Procedia 114 (2017): 5783-5790.
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