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Abstract
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Accurately estimating the Rate of Penetration (ROP) in drilling operations remains a significant challenge due to the limitations of traditional approaches, which are often characterized by low accuracy and reliance on empirical equations with assumed coefficients. These methods, while optimized for specific fields, often fail to generalize across different geological contexts. To address these gaps, this study proposes an innovative machine learning-driven framework for ROP prediction, employing advanced algorithms such as Least Squares Support Vector Machines (LSSVM), Artificial Neural Networks (ANN), and Random Forest (RF). To further enhance model performance, metaheuristic optimization strategies such as the Crow Search Algorithm (CSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) are integrated. Among the tested models, the LSSVM-CSA framework achieved the best results, with a remarkable R-squared (R2) value of 92.55, a Root Mean Square Error (RMSE) of 2.98. These results underscore the superior accuracy, robustness, and adaptability of the proposed methodology. By learning from the unique characteristics of each field during training, the models provide enhanced predictive capabilities and operational flexibility. The dataset, sourced from the Fahliyan Formation in southern Iran, demonstrates the practical applicability of the approach in real-world drilling operations. This study addresses the limitations of traditional methods, highlights the benefits of integrating machine learning with metaheuristic optimization, and provides actionable insights for advancing drilling efficiency, minimizing operational costs, and enabling data-driven decision-making in the petroleum industry.
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