Volume 41: Energy Transitions toward Carbon Neutrality: Part IV

Real-time prediction of oil and gas drilling rate based on physics-based model and particle filter method Chengkai Zhang, Xianzhi Song, Yinao Su



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, physics-based 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, face challenges with data availability and training costs in real-time settings. This paper introduces a novel approach combining a physics-based model with a particle filter algorithm for real-time ROP prediction. It adapts the Bourgoyne-Young ROP model and Markov assumptions into a state space model, using the particle filter to estimate elusive coefficients through probability theory. This enables real-time data updates for more accurate ROP predictions. The proposed framework is evaluated against traditional models using open-source and field drilling datasets in post-drilling and real-time scenarios. Results show conventional physics-based models fall short in both scenarios, while machine learning and the new particle filter model show significant improvements. In post-drilling analysis, these models achieve under 5% mean relative error. For real-time predictions, machine learning models have over 20% error, but the particle filter model reduces this to approximately 15%. This highlights the particle filter model’s superiority in accuracy and cost-effectiveness under dynamic and uncertain drilling conditions. This paper presents a robust, efficient solution for ROP prediction and optimization, marking a significant advancement in the drilling field.

Keywords rate of penetration, particle filter, real-time prediction, physics-based model, machine learning

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