Evaluation of caprock integrity for underground storage of CO2 in depleted oil and gas reservoirs using machine learning approach.

Aminaho, Efenwengbe Nicholas; Sanaee, Reza; Faisal, Nadimul

Abstract

Carbon (IV) oxide (CO2) geosequestration represents one of the most promising options for reducing atmospheric emissions of CO2. Capturing carbon dioxide is proposed as one solution to global climate change caused by anthropogenic gases in the atmosphere. CO2 can be stored in aquifer or depleted oil and gas reservoirs. Caprock integrity is assessed based on petrophysics and geomechanics. Theories of rock failure is vital for caprock integrity (factor of safety estimation). Caprock integrity is monitored using Machine Learning. The aim of this study is to evaluate caprock integrity under cyclic stress loadings and identify best reservoirs for CO2 storage. The authors will investigate the impact of pressure on caprock integrity during CO2 injection and evaluate the extent machine learning models can predict caprock integrity.

Citation

AMINAHO, E.N., SANAEE, R. and FAISAL, N. 2022. Evaluation of caprock integrity for underground storage of CO2 in depleted oil and gas reservoirs using machine learning approach. To be presented at 9th Annual student energy congress 2022 (ASEC 2022), 7-9 March 2022, Zagreb, Croatia.

• Presentation Conference Type: Presentation / Talk
• Conference Name: 9th Annual student Energy congress 2022 (ASEC 2022)
• Start Date: Mar 7, 2022
• End Date: Mar 9, 2022
• Deposit Date: Mar 3, 2022
• Peer Reviewed: Peer Reviewed
• Keywords: Carbo dioxide; Geosequestration; Reducing atmospheric emissions; Global climate change; Caprock integrity
• Public URL: https://rgu-repository.worktribe.com/output/1608739
• Additional Information: This presentation was submitted as part of the SPE Europe student paper contest at the 9th Annual student energy congress 2022 (ASEC 2022), Zagreb, Croatia.