Assessment of CO₂ Storage Potential in Reservoirs with Residual Gas Using Deep Learning

CO₂ injection into the underlying water leg of depleted hydrocarbon reservoirs is a desirable option for carbon storage as demonstrated by existing industrial-scale storage projects in these geologic environments. This study sheds light on the effect of residual methane on the CO₂ storage efficiency as a screening criterion for selecting a water-bearing zone of a depleted gas reservoir to store CO₂. Employing compositional reservoir simulations, we evaluated the impact of residual methane on the injectivity, operational pressure, and long-term CO₂ trapping efficiency during injection and post-injection stage in a reservoir model representative of the so-called “HC sand” gas reservoir in High Island 24L field located in the offshore Texas State Waters. Results suggest that the presence of residual hydrocarbon gas negatively affects CO₂ residual and dissolution trapping, while it enhances the injectivity and pressure management arising from the increased mobility of CO₂ plume in the vicinity of the injection zone due to its mixing with the resident residual hydrocarbon gas.We further investigated the application of artificial neural network (ANN)-based proxy models for fast-track modeling of CO₂ storage in geologic structures associated with depleted gas reservoirs, aiming at the prediction of CO₂ trapping efficiency. We then employed the developed ANN model to perform Monte Carlo simulations for quantifying the uncertainty of geologic and reservoir parameters on CO₂ trapping efficiency in these formations. It becomes evident that residual hydrocarbon saturation is a key screening criterion for the storage site selection. The developed data-driven model can offer a robust and fast tool for screening the water-bearing zone of the depleted gas reservoirs by evaluating the efficiency of CO₂ storage.