Predicting Geologic CO₂ Storage and Plume Evolution from Sparsely Available Well Data Using Barlow Twins

Carbon Capture and Storage (CCS) is an important practice for reducing greenhouse gas emissions and combating climate change. However, accurately monitoring carbon storage operations using simulations can be challenging due to data availability, subsurface complexity, uncertainty, and computational cost. Machine learning can help to address these challenges by providing cheaper data-driven approaches. For instance, Continuous Conditional Generative Adversarial Networks (CCGAN) can be used to predict CO₂ plume propagation with sparsely available data. This model enables fast prediction with reasonable accuracy and a substantial reduction in computational cost when compared to numerical simulations. Another approach, Barlow Twins (BT), provides better results than other deep learning-based approaches and comparable results to traditional methods for linear subspace and nonlinear manifold problems. In this work, we compare the accuracy of predictions of CO₂ plume propagation based on data from three well locations using a BT-based approach to those obtained with the CCGAN. Our findings suggest that BT-based approaches could be a viable option for data-driven simulation of CO₂ plume propagation in the subsurface when data is limited.