智能仿生PDC 钻头的破岩数值模拟.

In order to solve ordinary impregnated diamond bit (normal bit), low efficiency of rock fragmentation, drilling is not stable, not wear-resisting, and not according to choose a suitable form of broken rock stratum, combined with the bionic nonplanar structural design principle and the bottom of the tooth and a new type of polycrystalline diamond compact(PDC) bit lip surface, a bit bottom lip surface sensor installed on the lithology identification was proposed. The intelligent bionic PDC bit (intelligent bit) can be selected to break rock with appropriate shape. Machine learning was used to classify rock properties based on field drilling data. Based on the established cutting model and rock failure criteria, the rock breaking process of the intelligent bit and the common bit is numerically simulated and compared. The results show that the weighted KNN (K-nearest neighbor)algorithm based on machine learning can well avoid the problem of sample overlap and accurately classify the rock properties. Its training model can be provided to the sensor for lithology identification. When sandstone is broken, the bottom lip with PDC teeth and the bionic bottom lip of the intelligent bit are adjusted by the sensor to keep the same height of rock breaking. Compared with the ordinary bit, the rock breaking specific energy and cutting force are significantly smaller, indicating that its efficiency is higher and the cutting is more stable. When granite is broken, the intelligent bit will be adjusted by the sensor to lower the bottom lip with PDC teeth to break rock, and its rock breaking efficiency is significantly higher than that of the ordinary bit, but also more stable. The research results are of great significance to the intelligent development of oil drilling equipment. [ABSTRACT FROM AUTHOR]