Sustainable CO2 Storage Assessment in Saline Aquifers Using a Hybrid ANN and Numerical Simulation Model Across Different Trapping Mechanisms

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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 storage assessment, providing a reliable tool for long-term carbon sequestration strategies.