High water cut has been an issue in the Delaware basin for many years now. Volume of produced water continue to increase, resulting in adverse environmental impacts and higher reservoir-management costs. To address these problems, a data-driven workflow has been developed to pre-emptively identify the high water-cut wells. The workflow uses unsupervised pseudo-rock typing followed by supervised classification trained on well logs from 17 wells in the Delaware basin. The workflow requires a suite of 5 well logs from a 500-ft depth interval surrounding the kick-off points of these wells, which includes 200 ft above and 300 ft below the KOP. First, the well logs are clustered into 5 pseudo-rock types using multi-level clustering. Using statistical features extracted from these 5 pseudo-rock types, 3 supervised classifiers, namely K-nearest neighbor, support vector machine, and logistic regression, are trained and tested to detect the high water-cut wells. Over 100 cross validations, the 3 classifiers perform at a median Matthew’s Correlation Coefficient (MCC) of 0.90. The kurtosis of the neutron porosity log response of the pseudo-rock type A0, interpreted as a shale lithology, is the most The submitted paper is currently under review. Dr. Sid Misra is the lead investigator on this topic. informative/relevant signature associated with high water cut. Next, the presence of pseudo-rock type A1, interpreted as high-permeability lithology, is an informative signature of low water-cut wells. The kurtosis of the density porosity log response of the pseudo-rock type B0, interpreted as carbonate lithology, and the presence of pseudo-rock type B1, interpreted as a tight sandstone lithology, serve as informative signatures for differentiating high water cut wells from low water cut wells.