Geological and engineering insights from transfer learning with fourier neural operators: A case study of CO2 storage forecasting in disparate saline aquifers

The rapid advancement of machine learning techniques, particularly Fourier Neural Operators (FNO), offers a promising approach to predicting CO₂ saturation and pressure distributions in geological carbon storage. This study explores the application of FNO combined with transfer learning (FNO+TL) to enhance computational efficiency and accuracy in forecasting CO₂ storage under diverse geological and operational conditions. We trained FNO models on datasets from the SACROC (153 samples) geological model and applied TL to predict outcomes for the Illinois Basin - Decatur Project (IBDP). Our findings highlight the substantial computational savings without significant compromise in performance of the FNO+TL models compared to FNO, using 10 and 20 samples for pressure and saturation predictions respectively. The FNO+TL model achieved an average Mean Absolute Error (MAE) of 0.11 for CO2 saturation and 8.7 psia for pressure predictions, compared to 0.79 and 2.4 psia respectively for FNO. While saturation predictions were less precise, the model effectively captured the overall CO₂ migration trends. Notably, transfer learning significantly reduced computational costs, decreasing training time by 62.5 % and storage, RAM requirements by 90 % and 68 %, respectively. Despite some limitations in saturation prediction accuracy, the FNO+TL approach demonstrates potential for efficient and reliable CO₂ storage forecasting. This study highlights the potential of FNOs and transfer learning for efficient and accurate forecasting of CO₂ storage behavior and management of carbon sequestration projects.