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Research on Drilling Overflow Feature Extraction and Data Processing Method based on Real-time Data

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Proposed method improves overflow early warning using big data and machine learning for drilling safety.

Data-driven feature engineering significantly enhances the overflow early warning model's performance, allowing for quicker well control response.
Optimized parameters identified using recursive feature elimination resulted in 9 core features that are crucial inputs for the predictive model.
The real-time drilling dataset consisted of 25 original features, which were effectively preprocessed through feature engineering techniques.
This method has significant potential to reduce drilling accident risks, especially in complex geological scenarios.

Wenjie Deng (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Proposed method improves overflow early warning using big data and machine learning for drilling safety.

Data-driven feature engineering significantly enhances the overflow early warning model's performance, allowing for quicker well control response.
Optimized parameters identified using recursive feature elimination resulted in 9 core features that are crucial inputs for the predictive model.
The real-time drilling dataset consisted of 25 original features, which were effectively preprocessed through feature engineering techniques.
This method has significant potential to reduce drilling accident risks, especially in complex geological scenarios.

Wenjie Deng (2025) studied this question.

View Full Paper

Ask AI

Bookmark