<![CDATA[Manual and conventional processing of geothermal well production data is computationally inefficient and requires several hours to days to generate productivity assessments, particularly when dealing with large-scale and non-linear operational datasets. The complexity of geothermal production parameters this paper such as wellhead pressure (WHP), enthalpy, steam flow, brine flow, total flow, and generated power this paper creates challenges for accurate and timely productivity classification at the well level. This study utilizes 74,912 daily production records collected from January 2018 to June 2024, comprising 13 operational and production-related attributes. The objective is to identify the most effective machine learning algorithm for classifying geothermal well productivity levels to support faster and more reliable operational decision-making. A comparative machine learning classification approach was conducted using K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), and Gradient Boosting. Model evaluation was performed using three train–test split ratios: 70:30, 80:20, and 90:10. Two modelling scenarios were implemented: with and without Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The results indicate that the K-NN model achieved the highest classification performance, reaching 94.22% accuracy using the 90:10 split ratio without SMOTE. Gradient Boosting demonstrated stable performance across all ratios, with its best accuracy of 91.39% at the 70:30 split without SMOTE. In contrast, SVM produced the lowest performance, with a maximum accuracy of 79.78% at the 90:10 ratio without SMOTE. The application of SMOTE improved minority class recall, particularly for SVM, but generally reduced overall model accuracy for K-NN and Gradient Boosting. These findings demonstrate that classical machine learning algorithms, particularly K-NN, provide an efficient and accurate solution for geothermal well productivity classification. The proposed approach significantly reduces processing time compared to conventional analytical methods and supports data-driven decision-making in geothermal production forecasting and development planning.]]>
