Sustainable CO 2 Storage Assessment in Saline Aquifers Using a Hybrid ANN and Numerical Simulation Model Across Different Trapping Mechanisms

Mazen Hamed and Ezeddin Shirif ()
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Mazen Hamed: Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada
Ezeddin Shirif: Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada

Sustainability, 2025, vol. 17, issue 7, 1-32

Abstract: The study represents an innovative method to utilize the strong computational power of CMG-GEM, a numerical reservoir simulator coupled with artificial neural networks (ANNs) to predict carbon storage capacity in saline aquifers. The key parameters in geological storage formations are identified by generating a diverse dataset from CMG-GEM simulation runs by varying the different geological and operational parameters. Robust data analysis was performed to understand the effects of these parameters and access the different CO 2 trapping mechanisms. One of the significant novelties of this model is its ability to incorporate additional inputs not previously considered in similar studies. This enhancement allows the model to predict all CO 2 trapping mechanisms, rather than being limited to just one or two, providing a more holistic and accurate assessment of carbon sequestration potential. The generated dataset was used in MATLAB to develop an ANN model for CO 2 storage prediction across various trapping mechanisms. Rigorous testing and validation are performed to optimize the model, resulting in an accuracy of 98% using the best algorithm, which reflects the model’s reliability in evaluating the CO 2 storage. Therefore, the number of simulation runs was significantly reduced, which saves great amounts of computational power and simulation running time. The integration of machine learning and numerical simulations in this study represents a significant advancement in sustainable CO 2 storage assessment, providing a reliable tool for long-term carbon sequestration strategies.

Keywords: carbon storage; carbon sequestration; ANN; MATLAB; reservoir simulation; CMG-GEM; saline aquifers; CO 2 trapping; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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