Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models
Abstract Predicting the rate of penetration (ROP) is critical for optimizing drilling performance, yet it remains a complex task due to the interplay of multiple geological and operational parameters. This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolutio...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-025-02018-3 |
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| author | Guoli Huang Sarah Kanaan Hamzah Pinank Patel T. Ramachandran Aman Shankhyan A. Karthikeyan Dhirendra Nath Thatoi Deepak Gupta S. AbdulAmeer Mariem Alwan Zahraa Saad Abdulali Mahmood Jasem Jawad Hiba Mushtaq Mohammad Mahtab Alam Hojjat Abbasi |
| author_facet | Guoli Huang Sarah Kanaan Hamzah Pinank Patel T. Ramachandran Aman Shankhyan A. Karthikeyan Dhirendra Nath Thatoi Deepak Gupta S. AbdulAmeer Mariem Alwan Zahraa Saad Abdulali Mahmood Jasem Jawad Hiba Mushtaq Mohammad Mahtab Alam Hojjat Abbasi |
| author_sort | Guoli Huang |
| collection | DOAJ |
| description | Abstract Predicting the rate of penetration (ROP) is critical for optimizing drilling performance, yet it remains a complex task due to the interplay of multiple geological and operational parameters. This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolution dataset from drilling operations in southeast Iraq. Among the models tested, the Random Forest algorithm demonstrated outstanding performance, achieving an R2 of 0.955, a Mean Squared Error (MSE) of 0.119, and an Average Absolute Relative Error (AARE%) of 7.683, highlighting its reliability and robustness in predicting ROP. Sensitivity analysis and SHAP (Shapley Additive Explanations) also identified fracture pressure, kinematic viscosity, and rotary speed (RPM) as the most influential parameters affecting ROP. While alternative methods like Decision Tree and AdaBoost showed signs of overfitting, the results emphasize the Random Forest model’s superiority in balancing accuracy and generalizability. This research underscores the potential of advanced machine learning techniques in enhancing drilling performance, offering significant implications for real-world applications. |
| format | Article |
| id | doaj-art-ba460f22dc2c41fc9e1e2d7013cd8b61 |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-ba460f22dc2c41fc9e1e2d7013cd8b612025-08-20T03:45:47ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-06-0115712210.1007/s13202-025-02018-3Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning modelsGuoli Huang0Sarah Kanaan Hamzah1Pinank Patel2T. Ramachandran3Aman Shankhyan4A. Karthikeyan5Dhirendra Nath Thatoi6Deepak Gupta7S. AbdulAmeer8Mariem Alwan9Zahraa Saad Abdulali10Mahmood Jasem Jawad11Hiba Mushtaq12Mohammad Mahtab Alam13Hojjat Abbasi14College of Energy and Environment Engineering, Yan’an UniversityDepartment of Construction and Project Management, College of Engineering, Alnoor UniversityDepartment of Mechanical Engineering, Faculty of Engineering and Technology, Marwadi University Research Center, Marwadi UniversityDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University)Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Mechanical Engineering, Sathyabama Institute of Science and TechnologyDepartment of Mechanical Engineering, Siksha ‘O’ Anusandhan (Deemed to be University)Department of Mechanical Engineering, Graphic Era Hill UniversityDepartment of Automobile Engineering, College of Engineering, Al-Musayab, University of BabylonCollege of Health and Medical Technology, National University of Science and TechnologyPharmacy College, Al-Farahidi UniversityDepartment of Pharmacy, Al-Zahrawi University CollegeGilgamesh Ahliya UniversityDepartment of Basic Medical Sciences, College of Applied Medical Science, King Khalid UniversityChemistry Department, Herat UniversityAbstract Predicting the rate of penetration (ROP) is critical for optimizing drilling performance, yet it remains a complex task due to the interplay of multiple geological and operational parameters. This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolution dataset from drilling operations in southeast Iraq. Among the models tested, the Random Forest algorithm demonstrated outstanding performance, achieving an R2 of 0.955, a Mean Squared Error (MSE) of 0.119, and an Average Absolute Relative Error (AARE%) of 7.683, highlighting its reliability and robustness in predicting ROP. Sensitivity analysis and SHAP (Shapley Additive Explanations) also identified fracture pressure, kinematic viscosity, and rotary speed (RPM) as the most influential parameters affecting ROP. While alternative methods like Decision Tree and AdaBoost showed signs of overfitting, the results emphasize the Random Forest model’s superiority in balancing accuracy and generalizability. This research underscores the potential of advanced machine learning techniques in enhancing drilling performance, offering significant implications for real-world applications.https://doi.org/10.1007/s13202-025-02018-3Rate of penetrationMachine learningDrillingRandom ForestSHAP |
| spellingShingle | Guoli Huang Sarah Kanaan Hamzah Pinank Patel T. Ramachandran Aman Shankhyan A. Karthikeyan Dhirendra Nath Thatoi Deepak Gupta S. AbdulAmeer Mariem Alwan Zahraa Saad Abdulali Mahmood Jasem Jawad Hiba Mushtaq Mohammad Mahtab Alam Hojjat Abbasi Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models Journal of Petroleum Exploration and Production Technology Rate of penetration Machine learning Drilling Random Forest SHAP |
| title | Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models |
| title_full | Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models |
| title_fullStr | Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models |
| title_full_unstemmed | Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models |
| title_short | Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models |
| title_sort | data driven prediction of rate of penetration rop in drilling operations using advanced machine learning models |
| topic | Rate of penetration Machine learning Drilling Random Forest SHAP |
| url | https://doi.org/10.1007/s13202-025-02018-3 |
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