YUAN Ruofei, WU Zebing, ZHANG Wenxi. Scalable biomimetic polycrystalline diamond compact bit[J]. Oil Drilling & Production Technology, 2023, 45(3): 296-306. DOI: 10.13639/j.odpt.202303056
Citation: YUAN Ruofei, WU Zebing, ZHANG Wenxi. Scalable biomimetic polycrystalline diamond compact bit[J]. Oil Drilling & Production Technology, 2023, 45(3): 296-306. DOI: 10.13639/j.odpt.202303056

Scalable biomimetic polycrystalline diamond compact bit

  • In response to the challenges of existing PDC (Polycrystalline Diamond Compact) drill bits, such as difficulty in drilling hard rocks and encountering sticking and sliding issues, a novel approach is taken based on biomimicry principles. Inspired by the digging mechanisms of mole claws and the cutting efficiency of shark teeth, a scalable biomimetic PDC drill bit was designed by combining the advantages of dual-stage and composite drill bits. By using numerical simulation methods, a comparative analysis was performed between biomimetic PDC teeth and conventional teeth in terms of rock cutting forces and mechanical specific energy, as well as an analysis on drilling progress and reaction torque between the scalable biomimetic PDC drill bit and conventional PDC drill bit. To facilitate subsequent design of the scalable structure, by using Box-Behnken method, a multi-factor finite element simulation experiment was performed on parameters such as drilling pressure, rotational speed, scalable length, rock removal volume, and torque. And quadratic regression mathematical models were established to capture the relationships among these parameters. The results show that biomimetic PDC teeth achieve higher efficiency in rock fragmentation compared to conventional teeth, with reductions of approximately 19% in mechanical specific energy and 12.7% in cutting forces during the rock-breaking process. The scalable biomimetic PDC drill bit outperforms the conventional PDC drill bit, showing a 1.25-time increase in drilling progress, 21% improvement in drilling speed, and a 33% reduction in reaction torque from the rock, effectively mitigating the sticking and sliding problems. The accuracy of the obtained quadratic regression models was validated through numerical simulations, revealing an average error of less than 6%. These research findings can provide a theoretical support for subsequent field experiments and optimization designs of scalable structure.
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