A hybrid data-centric machine learning for lost circulation prediction using imbalanced drilling and geological datasets
Researchers have developed a hybrid machine learning framework to predict lost circulation in oil drilling, addressing the challenge of highly imbalanced geological datasets. By combining synthetic sampling with ensemble learning, the model significantly improves accuracy in identifying drilling risks.
Why it matters
Improved predictive modeling for drilling operations can reduce operational costs and environmental risks associated with oil and gas extraction.
Scientific Reports ( 2026 ) Cite this article
We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.
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