Summary of Datadrill: Formation Pressure Prediction and Kick Detection For Drilling Rigs, by Murshedul Arifeen et al.
DataDRILL: Formation Pressure Prediction and Kick Detection for Drilling Rigs
by Murshedul Arifeen, Andrei Petrovski, Md Junayed Hasan, Igor Kotenko, Maksim Sletov, Phil Hassard
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A data-driven approach is proposed to improve drilling operations by predicting formation pressure and detecting kicks in real-time. The current literature lacks publicly available datasets, hindering progress in this domain. Two new datasets are introduced, containing over 2000 samples with 28 variables, to support the development of intelligent algorithms for oil/gas well drilling research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting formation pressure and detecting kicks is crucial for efficient drilling operations. The paper proposes a data-driven approach using two new publicly available datasets to improve decision-making and reduce costs. The datasets include over 2000 samples with 28 variables, making it easier for researchers to develop intelligent algorithms for oil/gas well drilling research. |
