Transfer learning for geological carbon storage forecasting using neural operator

Abstract

Geological carbon storage (GCS) is critical for sequestering CO₂ deep underground. GCS projects may face environmental challenges, such as leakage risks, adverse pressure buildup, and groundwater contamination. Numerical simulators play a vital role in accurate forecasting but can be computationally expensive. In this work, we leveraged an updated Fourier Neural Operator (FNO) which includes data sparsity management, to learn to rapidly forecast pressure and CO₂ phase saturation distributions in a geological carbon storage (GCS) reservoir. Compared to commercial reservoir simulators, FNO-based forecasting offers accurate prediction while reducing the computational time by a factor of 40, enabling high volume of forecasting in less time. Additionally, we applied transfer learning (TL) to further reduce the data and computational requirements of the FNO-based forecasting across a wide array of scenarios. Specifically, we demonstrated the usefulness of TL in accurately predicting the pressure and CO₂ saturation distributions for uncertain and variable geological and operational conditions. The results of this study indicate that the improved FNO workflow reduces the computational time by approximately 97 %, and the relative mean error for predicting both CO₂ saturation and pressure distributions is <1 %. Generally, the use of TL effectively transfers knowledge from a pre-existing model to other related tasks. TL significantly reduces the required training data by 78 % while maintaining a relative mean error below 5 %. Although, the results in this work can be further improved, this study demonstrates the potential of integrating FNO and TL to reduce computational time and data requirements for CO₂ forecasts during GCS projects, providing a more efficient and faster approach.