Influence of Caprock Morphology on Solubility Trapping during CO2 Geological Sequestration. (25th June 2022)

• Record Type: Journal Article
• Authors: Punnam, Pradeep Reddy; Krishnamurthy, Balaji; Surasani, Vikranth Kumar
• Other Names: Sracek Ondra Academic Editor.
• Is Part Of: Geofluids. Volume 2022(2022)
• Publication Date: 2022-06-25
• Subjects: Hydrogeology -- Periodicals; Sedimentary basins -- Periodicals; Fluids -- Migration -- Periodicals; Groundwater flow -- Periodicals; Geothermal resources -- Periodicals; Fluid dynamics -- Periodicals; Earth -- Crust -- Periodicals; 551.49
• DOI: 10.1155/2022/8016575
• Languages: English
• ISSNs: 1468-8115

Abstract

Abstract : Carbon capture and sequestration (CCS) technology is one of the indispensable alternatives to reduce carbon dioxide (CO2 ) emissions. In this technology, carbon capture and transport grid will send CO2 to the storage facilities that are using various storage techniques. Geologic carbon sequestration (GCS) is one such storage technique where CO2 is injected into a deep geological subsurface formation. The injected CO2 is permanently stored in the formation due to structural, residual, solubility, and mineral trapping phenomena. Among different trapping mechanisms, solubility trapping plays a significant role in the safe operation of GCS. In this work, the study is conducted to elucidate the influence of top surface caprock morphology on the solubility trapping mechanism. The simulation results show that the naturally available heterogeneous formations with anticline and without anticline structure influence the solubility fingering phenomena and solubility entrapment percentage over a geological time scale. The lateral migration and sweeping efficiency results of both the synthetic domains for the injected CO2 have shown the importance of caprock morphology on solubility trapping and selection of injection rate. Quantification of solubility trapping in two morphological structures revealed that the synthetic domain without anticline morphology had shown higher solubility trapping. In the future, the simulation data using Artificial Neural Networks can be applied to predict the structural and solubility trapping of geological formations. This analysis further helps incorporating the interaction of CO2 with porous media leading to a mineral trapping mechanism.