Assessment of CO2 storage potential in reservoirs with residual gas using deep learning

Abstract

[Formula: see text] 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 [Formula: see text] storage efficiency as a screening criterion for selecting a water-bearing zone of a depleted gas reservoir to store [Formula: see text]. Using compositional reservoir simulations, we have evaluated the impact of residual methane on the injectivity, operational pressure, and long-term [Formula: see text] trapping efficiency during injection and postinjection stage in a reservoir model representative of the so-called “HC sand” gas reservoir in the High Island 24L field located in the offshore Texas State Waters. Results suggest that the presence of residual hydrocarbon gas negatively affects [Formula: see text] residual and dissolution trapping because it enhances the injectivity and pressure management arising from the increased mobility of [Formula: see text] plume in the vicinity of the injection zone due to its mixing with the resident residual hydrocarbon gas. We further investigate the application of artificial neural network (ANN)-based proxy models for fast-track modeling of [Formula: see text] storage in geologic structures associated with depleted gas reservoirs, aiming at the prediction of [Formula: see text] trapping efficiency. We then use the developed ANN model to perform Monte Carlo simulations for quantifying the uncertainty of geologic and reservoir parameters on [Formula: see text] trapping efficiency in these formations. It becomes evident that the 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 [Formula: see text] storage.