CO2 injection-based enhanced methane recovery from carbonate gas reservoirs via deep learning

CO2 injection is a promising technology for enhancing gas recovery (CO2-EGR) that concomitantly reduces carbon emissions and aids the energy transition, although it has not yet been applied commercially at the field scale. We develop an innovative workflow using raw data to provide an effective approach in evaluating CH4 recovery during CO2-EGR. A well-calibrated three-dimensional geological model is generated and validated using actual field data-achieving a robust alignment between history and simulation. We visualize the spread of the CO2 plume and quantitatively evaluate the dynamic productivity to the single gas well. We use three deep learning algorithms to predict the time histories of CO2 rate and CH4 recovery and provide feedback on production wells across various injection systems. The results indicate that CO2 injection can enhance CH4 recovery in water-bearing gas reservoirs-CH4 recovery increases with injection rate escalating. Specifically, the increased injection rate diminishes CO2 breakthrough time while concurrently expanding the swept area. The increased injection rate reduces CO2 breakthrough time and increases the swept area. Deep learning algorithms exhibit superior predictive performance, with the gated recurrent unit model being the most reliable and fastest among the three algorithms, particularly when accommodating injection and production time series, as evidenced by its smallest values for evaluation metrics. This study provides an efficient method for predicting the dynamic productivity before and after CO2 injection, which exhibits a speedup that is 3-4 orders of magnitudes higher than traditional numerical simulation. Such models show promise in advancing the practical application of CO2-EGR technology in gas reservoir development.