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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.19724 |
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| _version_ | 1866910624358858752 |
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| author | Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed Kotenko, Igor Sletov, Maksim Hassard, Phil |
| author_facet | Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed Kotenko, Igor Sletov, Maksim Hassard, Phil |
| contents | Accurate real-time prediction of formation pressure and kick detection is crucial for drilling operations, as it can significantly improve decision-making and the cost-effectiveness of the process. Data-driven models have gained popularity for automating drilling operations by predicting formation pressure and detecting kicks. However, the current literature does not make supporting datasets publicly available to advance research in the field of drilling rigs, thus impeding technological progress in this domain. This paper introduces two new datasets to support researchers in developing intelligent algorithms to enhance oil/gas well drilling research. The datasets include data samples for formation pressure prediction and kick detection with 28 drilling variables and more than 2000 data samples. Principal component regression is employed to forecast formation pressure, while principal component analysis is utilized to identify kicks for the dataset's technical validation. Notably, the R2 and Residual Predictive Deviation scores for principal component regression are 0.78 and 0.922, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DataDRILL: Formation Pressure Prediction and Kick Detection for Drilling Rigs Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed Kotenko, Igor Sletov, Maksim Hassard, Phil Machine Learning Accurate real-time prediction of formation pressure and kick detection is crucial for drilling operations, as it can significantly improve decision-making and the cost-effectiveness of the process. Data-driven models have gained popularity for automating drilling operations by predicting formation pressure and detecting kicks. However, the current literature does not make supporting datasets publicly available to advance research in the field of drilling rigs, thus impeding technological progress in this domain. This paper introduces two new datasets to support researchers in developing intelligent algorithms to enhance oil/gas well drilling research. The datasets include data samples for formation pressure prediction and kick detection with 28 drilling variables and more than 2000 data samples. Principal component regression is employed to forecast formation pressure, while principal component analysis is utilized to identify kicks for the dataset's technical validation. Notably, the R2 and Residual Predictive Deviation scores for principal component regression are 0.78 and 0.922, respectively. |
| title | DataDRILL: Formation Pressure Prediction and Kick Detection for Drilling Rigs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.19724 |