Benchmarking maintenance costs for oil storage tanks has conventionally depended on historical averages and expert assessments, leading to irregular cost planning and challenges in creating defined baselines.  This paper presents a data-driven methodology that incorporates statistical analysis into the benchmarking process to provide a more objective and scalable cost estimating framework.  Empirical data from twenty-one oil storage tanks, differing in capacity and service age, were utilized to assess multiple regression models—linear, exponential, logarithmic, and polynomial—to determine the most effective functional relationship among tank capacity, service age, and maintenance cost.  Outlier identification, heteroskedasticity assessment, and residual normality evaluations were performed to guarantee model robustness.  The third-order polynomial regression demonstrated the greatest explanatory ability (R² = 0.846, Adjusted R² = 0.816), signifying a robust non-linear correlation between capacity and maintenance expense.  The findings indicated that smaller tanks often have elevated cost ratios owing to scale inefficiencies, whereas excessively big tanks incur greater costs due to structural intricacies.  The suggested approach facilitates the establishment of a "maintenance cost rule of thumb" that may act as a dependable reference for budgeting, planning, and performance evaluation.  The use of statistical analysis improves transparency, repeatability, and decision-making precision in asset integrity management methods for oil storage facilities