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Support Vector Machines (SVMs) for regression provide a robust approach to modeling data, especially in the presence of outliers. By allowing users to set a threshold value (ε), SVMs prioritize the impact of data points based on their residuals, ensuring that extreme values are accounted for in the model. The flexibility of adjusting the threshold ε allows users to control the complexity of the model, making SVMs versatile tools for regression analysis across datasets with varying degrees of complexity and outlier presence. This study investigates the application of SVMs for regression using a comprehensive dataset from the oil and gas sector. The study first filters variables and looks at correlations. Then it tests the regression model's assumptions and talks about what that means for the model's reliability and how well it can predict the future