Research on the Optimization Design of Drilling Parameters for Dynamic Point-the-Bit Rotary Steerable Systems
Source abstract
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.
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Research context
OpenAlex topics: Drilling and Well Engineering · Tunneling and Rock Mechanics · Grouting, Rheology, and Soil Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Ministry of Education Key Laboratory of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
CNOOC China Oilfield Services Limited, Langfang 065201, China
Funding
National Key Research and Development Program of China · Natural Science Foundation of China · Science Foundation of China University of Petroleum, Beijing
References (23)
Show source-provided references
1. Theory, technology and practice of unconventional petroleum geology10.1007/s12583-023-2000-8 ↗
2. Some research advances in well engineering technology for unconventional hydrocarbon10.1016/j.ngib.2021.08.016 ↗
3. Research on the measurement and control system of dynamic point-the-bit rotary steerable drilling tools
4. Application of rotary steering technology in shale oil horizontal wells and relevant understanding
5. Dynamic modeling and suppression parameter optimization for high-frequency vibrations in deepwater push-the-bit rotary steerable systems10.1007/s11071-025-11412-9 ↗
6. Research on data completion and generation of downhole drilling tool attitude based on Long Short-Term Memory neural networks10.1007/s13202-025-01989-7 ↗
7. A novel drilling parameter optimization method based on big data of drilling10.1016/j.petsci.2025.03.002 ↗
8. Al Dushaishi, M.F., Abbas, A.K., Al Saba, M.T., and Wise, J. (2025). Drilling Optimization Using Artificial Neural Networks and Empirical Models. ChemEngineering, 9.10.3390/chemengineering9020037 ↗
9. Optimization of drilling parameters based on improved ant colony algorithm
10. The effect of formation thickness on the performance of deterministic and machine learning models for rate of penetration management in inclined and horizontal wells10.1016/j.petrol.2020.107160 ↗
11. Ashena, R., Rabiei, M., Rasouli, V., and Mohammadi, A.H. (2020, January 17–19). Optimization of drilling parameters using an innovative GA-PS artificial intelligence model. Proceedings of the SPE Asia Pacific Oil & Gas Conference and Exhibition, Virtual.10.2118/202325-MS ↗
12. Data-driven drilling acceleration in Bohai XX Block
13. A new methodology for optimization and prediction of rate of penetration during drilling operations10.1007/s00366-019-00715-2 ↗
14. Drilling parameter optimization of shale gas well based on saw-tooth genetic algorithm
15. Real-time ROP optimization technology based on big data and artificial intelligence
16. A real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on intelligent optimization algorithm and machine learning10.1016/j.oceaneng.2023.116375 ↗
17. A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: A case study in Xinjiang10.1007/s13202-021-01402-z ↗
18. Improving the drilling parameter optimization method based on the fireworks algorithm10.1021/acsomega.2c05692 ↗
19. Optimization of drilling parameters using combined multi-objective method and presenting a practical factor10.1016/j.cageo.2023.105359 ↗
20. Generalized beam-column method for non-continuous rotary steering drilling of bottom-hole assembly
21. Predicting rate of penetration of horizontal drilling by combining physical model with machine learning method in the China Jimusar Oil Field10.2118/212294-PA ↗
22. Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin10.1038/s41598-024-56640-y ↗
23. A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction10.1016/j.jece.2025.119675 ↗