Real-time prediction of oil and gas drilling rate based on physics-based model and particle filter method
Chengkai Zhang,
Xianzhi Song,
Yinao Su
Abstract:Oil and gas drilling, essential for exploring and exploiting petroleum resources, involves significant time, labor, and costs, often exceeding $300,000 daily. Predicting the drilling rate (Rate of Penetration, ROP) accurately and promptly is crucial for improving efficiency and reducing expenses. In drilling, physicsbased and machine learning models are typically used for ROP forecasting. Physics-based models, while intuitive, often lack precision in complex conditions. Machine learning models, though precise,… Show more
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