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This is a Quantitative Study study.

Research on the Quantitative Evaluation and Methods of Drilling Overflow Risk Based on While-Drilling Information

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This analysis reveals improved overflow risk prediction in complex geological environments, highlighting robust interpretability and adaptability.

The proposed method shows a remarkable accuracy of 99.95% in assessing overflow risk.
Using CNN-LSTM combined with fuzzy reasoning enhances the interpretability of the risk assessment.
Dynamic calibration of risk probability thresholds effectively categorizes risk levels into low, medium, and high.
Integration of expert experience through fuzzy rules provides a flexible and transparent risk evaluation process.

Yang Hu (2025) studied this question.

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View Full Paper

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Authors

This analysis reveals improved overflow risk prediction in complex geological environments, highlighting robust interpretability and adaptability.

The proposed method shows a remarkable accuracy of 99.95% in assessing overflow risk.
Using CNN-LSTM combined with fuzzy reasoning enhances the interpretability of the risk assessment.
Dynamic calibration of risk probability thresholds effectively categorizes risk levels into low, medium, and high.
Integration of expert experience through fuzzy rules provides a flexible and transparent risk evaluation process.

Yang Hu (2025) studied this question.

View Full Paper

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Bookmark