Determination of Lithofacies and Elastic Behavior Modeling in Columbian River Basalt Group (CRBG) Formations

Efficient handling of climate change issues in order to mitigate its negative impact of the flora and fauna of the earth, or on the pace of industrialization, is a big challenge in every disposition around the world.  Amongst the many options available, geological storage of CO₂ in the basalt formations is proving to be a promising one due to its large and pervasive occurrence, to facilitate stable carbonation of the sequestered CO₂, and with ready access to the basalt deposits for operational requirements. Laboratory testing and a few field   implementations showed that carbon dioxide injected in basalts would form stable carbonate minerals, keeping the substance in place for thousands of years.

This work applies the machine learning applications aimed at the classification of different facies in basalts, particularly flow tops and flow interiors, towards the selection of a sequestration site based on their relevant petrophysical characteristics.

After the facies were identified, several rock physics models were run with an outlook of predicting the elastic properties of basalt. Based on our results, we found the Differential Effective Medium (DEM) model enables the most accurate prediction with the least error as compared to Self-Consistent Approximation and Kuster-Toksӧz model. This finding provides a foundation for using the DEM model to create an initial reservoir matrix, which can be applied to simulate geomechanical changes upon CO₂ injection in Basalt. Additionally, facies classification aids in delineating zone boundaries within basalt flows, allowing for the selection of optimal injection sites based on their petrophysical properties.