Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

•A numerical simulation was carried out to generate mineral-trapping data due to CO2 injection in a sandstone reservoir.•An unsupervised machine learning tool called non-negative matrix factorization with k-means clustering was used to analyze the data.•We identified injection-, short-, mid-, and long-term reaction stages.•Calcite, dolomite, siderite, clinochlore, kaolinite, Na+, K+, Ca2+, Mg2+, and aq. CO2 play major role in mineral trapping.