Abstract
This study leverages machine learning techniques to predict and optimize operational expenditures (OPEX) in Indonesia's oil and gas industry. By analyzing historical data from Work Plan and Budget (WP&B) reports from 2017, the research identifies key factors influencing OPEX, such as production location, oil characteristics, and development stages. The Random Forest model demonstrated the highest predictive accuracy with an R-squared value of 0.92 and Mean Squared Error (MSE) of 4.5. The findings highlight significant cost-saving opportunities, particularly in Kalimantan and Papua. These insights support strategic planning and decision-making, emphasizing the transformative potential of machine learning in enhancing operational efficiency and sustainability in the oil and gas sector.
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