ArticleScientific reports2026
Predicting drilling fluid lost circulation volume and radius using petrophysical well logs and machine learning approaches.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Lost circulation, which refers to the unintended loss of drilling fluid into underground formations, presents a significant challenge in drilling operations. This issue often leads to considerable non-productive time and financial losses. To effectively address this problem, it is crucial to accurately predict loss zones, volumes, and the extent of fluid invasion. This study introduces an innovative approach by integrating petrophysical well logs with drilling operational parameters and mud characteristics within a machine learning framework. Using a comprehensive dataset of 246,620 data points from 73 wells in an Iranian oil field, we developed three advanced predictive models: multi-layer perceptron (MLP), Kolmogorov-Arnold network (KAN), and a novel hybrid MLP-KAN architecture. Performance evaluations revealed that the hybrid model significantly outperformed the standalone models, achieving a determination coefficient (R
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