Application of machine learning in the determination of rock brittleness for CO2 geosequestration

The underground storage of carbon dioxide (CO₂), also called CO₂ geosequestration, represents one of the most promising options for reducing greenhouse gases in the atmosphere. However, fluid-rock interactions in reservoir and cap rocks before and during CO₂ geosequestration alter their mineralogical composition, and consequently, their brittleness index which is paramount in determining the suitability of formations for CO₂ geosequestration. Therefore, it is important to monitor the brittleness of reservoir and cap rocks, to ascertain their integrity for CO₂ storage. In this study, an algorithm was developed to generate numerical simulation datasets for a more reliable machine learning model development, and an artificial neural network (ANN) model was developed to evaluate the brittleness index of rocks using data from numerical simulations of CO₂ geosequestration in sandstone and carbonate reservoirs, overlain by shale caprock. The model was developed using Python programming language. The model developed in this study predicted the brittleness index of rocks with an R² value greater than 99 %, and mean absolute percentage error (MAPE) <0.6 % on the training, validation, and testing datasets. Hence, the model predicts the brittleness index of rocks with high accuracy. The findings of the study revealed that the geochemical composition of formation fluids is related to the brittleness index of rocks. In terms of feature importance in predicting the brittleness index of rocks, the concentrations of SiO₂ (aq), SO₄², K⁺, Ca²⁺, and O₂ (aq) have a stronger impact on the brittleness of rocks considered in this study.