Publication: Sequential Data Approach for Rate of Penetration Prediction Using Machine Learning Models: A Case Study the Offshore Volve Oil Field, North Sea, Norway
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Issued Date
2024-01-01
Resource Type
ISSN
23673370
eISSN
23673389
Scopus ID
2-s2.0-85194251029
Journal Title
Lecture Notes in Networks and Systems
Volume
973 LNNS
Start Page
121
End Page
130
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes in Networks and Systems Vol.973 LNNS (2024) , 121-130
Suggested Citation
Pakawatthapana Y., Khonthapagdee S. Sequential Data Approach for Rate of Penetration Prediction Using Machine Learning Models: A Case Study the Offshore Volve Oil Field, North Sea, Norway. Lecture Notes in Networks and Systems Vol.973 LNNS (2024) , 121-130. 130. doi:10.1007/978-3-031-58561-6_12 Retrieved from: https://hdl.handle.net/20.500.14740/20250
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Abstract
In drilling operations, the Rate of Penetration directly impacts drilling efficiency. Accurately predicting this rate is crucial as it affects the cost-effectiveness of the process. In this study, ensemble machine learning models such as Random Forest, Gradient Boosting, Extreme Gradient Boosting, Adaptive Boosting, and K-Nearest Neighbor were used to predict ROP using a Volve dataset from the Norwegian North Sea provided by Equinor Company. To simulate real-world scenarios, especially in cases with limited amounts of data, we utilized sequential data or depth-dependent data to trained and tested various machine learning models using sequential split such as continuous learning and sliding window split techniques. The model’s performances were evaluated by calculating mean absolute error. The results of this study showed that the ensemble machine learning models, especially Extreme Gradient Boosting with sliding window split technique outperformed in predicting the Rate of Penetration. Moreover, the various sizes of the sliding window were studied to find the optimal size.