Abstract
The use effect of a bit has a direct influence the on rate of penetration, drilling cost, and wellbore quality. Conventional drill bit selection methods suffer from uncertainties in formation anti-drilling ability, lack of reference data in new areas, and overreliance on data, which results in overfitting in machine learning, inaccurate selection, and difficulty in meeting on-site construction requirements. In light of these challenges, this paper proposes a novel drill bit selection method based on the principal-factor three-dimensional golden-section method and considering the drillability similarity of formation resistance to drilling. First, grey relational analysis is employed to study the drillability similarity of encountered formations in each spud and formations with high grey relational degree and good drillability similarity are divided into the same category to expand the drill bit selection database. Second, a principal-factor three-dimensional golden-section drill bit optimization model is developed by comprehensively considering the effects of mechanical and hydraulic parameters on drill bit performance, and a three-dimensional golden-section discriminant surface is plotted. Finally, the preferred drill bit is selected from the region above the golden-section surface for on-site applications. The results indicate that formations with a grey relational degree exceeding 0.55 exhibit good similarity of anti-drilling ability. The TS1678BP drill bit, selected through the principal-factor three-dimensional golden-section method, exhibits exceptional performance in a two-step drilling operation. It attains an IADC wear grade of 1-2-BU-A-X-IX-WT-PR, achieves an average mechanical drilling speed of 10.50 m/h, surpassing adjacent wells by 31.25% (8 m/h), and exceeding construction requirements by 16.67% (9 m/h). These findings illustrate that the drill bit selection method proposed in this paper provides scientific guidance for an on-site drill bit selection.
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