Research on the Optimization Design of Drilling Parameters for Dynamic Point-the-Bit Rotary Steerable Systems

To improve the operational efficiency of the dynamic point-the-bit rotary steerable system (DPB-RSS) in deep and complex formations, this paper proposes a build-up rate (BUR) prediction model, a trajectory control model, and a mechanism–data fusion model for rate of penetration (ROP) prediction. Validation using field data from Well A indicates that the BUR and ROP models achieve prediction accuracies of 91.45% and 91.34%, respectively, demonstrating the reliability of the proposed models. Based on the validated models, a parameter sensitivity analysis was conducted for Well B to investigate the effects of weight on bit (WOB), rotary speed, and flow rate on drilling performance, thereby identifying a recommended operational parameter combination (WOB ≥ 60 kN, rotary speed = 105 rpm, and flow rate = 65 L/s). In addition, well trajectory control was implemented by dynamically adjusting the tool face angle and steering ratio using a compound control algorithm. Field application results further indicate that the proposed scheme can improve tool performance and provide useful guidance for efficient drilling with DPB-RSS.

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Publication Details

Journal
Applied Sciences
Published
2026-04-15
DOI
https://doi.org/10.3390/app16083856
Primary Topic
Drilling and Well Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on the Optimization Design of Drilling Parameters for Dynamic Point-the-Bit Rotary Steerable Systems

Jincheng Huang, Y Chen, Wen Tian, Mingchi Zhu et al.
Applied Sciences
Drilling and Well Engineering
article

Research on the Optimization Design of Drilling Parameters for Dynamic Point-the-Bit Rotary Steerable Systems

Jincheng Huang, Y Chen, Wen Tian, Mingchi Zhu, Yulin Zhang, Deli Gao
article en

Abstract

To improve the operational efficiency of the dynamic point-the-bit rotary steerable system (DPB-RSS) in deep and complex formations, this paper proposes a build-up rate (BUR) prediction model, a trajectory control model, and a mechanism–data fusion model for rate of penetration (ROP) prediction. Validation using field data from Well A indicates that the BUR and ROP models achieve prediction accuracies of 91.45% and 91.34%, respectively, demonstrating the reliability of the proposed models. Based on the validated models, a parameter sensitivity analysis was conducted for Well B to investigate the effects of weight on bit (WOB), rotary speed, and flow rate on drilling performance, thereby identifying a recommended operational parameter combination (WOB ≥ 60 kN, rotary speed = 105 rpm, and flow rate = 65 L/s). In addition, well trajectory control was implemented by dynamically adjusting the tool face angle and steering ratio using a compound control algorithm. Field application results further indicate that the proposed scheme can improve tool performance and provide useful guidance for efficient drilling with DPB-RSS.

Applied SciencesVol. 16(8)
China University of Petroleum, Beijing (CN), China National Petroleum Corporation (China) (CN)
National Natural Science Foundation of China, Science Foundation of China University of Petroleum, Beijing, National Key Research and Development Program of China
Openalex Percentile: Top 22%
Drilling and Well Engineering
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