<![CDATA[Leaks in oil pumping systems are among the main causes of production losses and reduced operational efficiency in the upstream oil and gas industry. This study aims to identify dominant failure modes, monitor process stability, and predict pump leakage risk using an integrated approach that combines Failure Mode and Effect Analysis (FMEA), Statistical Process Control (SPC), and the Support Vector Machine (SVM) algorithm. The research was conducted at PT Pertamina EP Zone 1 Rantau Field using operational data from the year 2024. FMEA results show that leakage due to illegal tapping and corrosion are the most critical failures, with Risk Priority Numbers (RPN) of 216 and 180, respectively. SPC analysis using X̄-R control charts revealed weekly process fluctuations, indicating potential variability in operations. To predict leakage risk, an SVM model was trained using technical pump features such as pressure, temperature, vibration, and pipe joint age. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE), and model evaluation yielded an accuracy of 95.48%. The integration of these three methods has proven effective in supporting data-driven predictive maintenance strategies. It reduces unplanned downtime, minimizes production losses, and enhances the reliability of oil pumping systems.]]>
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