Predictive Modeling and Simulation of CO 2 Trapping Mechanisms: Insights into Efficiency and Long-Term Sequestration Strategies

Oluchi Ejehu (), Rouzbeh Moghanloo and Samuel Nashed
Additional contact information
Oluchi Ejehu: Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, University of Oklahoma, Norman, OK 73019, USA
Rouzbeh Moghanloo: Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, University of Oklahoma, Norman, OK 73019, USA
Samuel Nashed: Mewbourne School of Petroleum and Geological Engineering, Mewbourne College of Earth and Energy, University of Oklahoma, Norman, OK 73019, USA

Energies, 2025, vol. 18, issue 15, 1-24

Abstract: This study presents a comprehensive analysis of CO 2 trapping mechanisms in subsurface reservoirs by integrating numerical reservoir simulations, geochemical modeling, and machine learning techniques to enhance the design and evaluation of carbon capture and storage (CCS) strategies. A two-dimensional reservoir model was developed to simulate CO 2 injection dynamics under realistic geomechanical and geochemical conditions, incorporating four primary trapping mechanisms: residual, solubility, mineralization, and structural trapping. To improve computational efficiency without compromising accuracy, advanced machine learning models, including random forest, gradient boosting, and decision trees, were deployed as smart proxy models for rapid prediction of trapping behavior across multiple scenarios. Simulation outcomes highlight the critical role of hysteresis, aquifer dynamics, and producer well placement in enhancing CO 2 trapping efficiency and maintaining long-term storage stability. To support the credibility of the model, a qualitative validation framework was implemented by comparing simulation results with benchmarked field studies and peer-reviewed numerical models. These comparisons confirm that the modeled mechanisms and trends align with established CCS behavior in real-world systems. Overall, the study demonstrates the value of combining traditional reservoir engineering with data-driven approaches to optimize CCS performance, offering scalable, reliable, and secure solutions for long-term carbon sequestration.

Keywords: carbon capture and storage; carbon dioxide; smart proxy model; machine learning; decision tree; random forest; Peng–Robinson equation of state (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/15/4071/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/15/4071/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:15:p:4071-:d:1714595

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().