Аңдатпа
Efficient and cost-effective construction of process boreholes is essential to the mining industry, particularly when low-grade deposits are developed using borehole-based extraction methods. Increasingly challenging geological conditions, greater orebody depths, and complex interactions among downhole processes require increasingly precise control of drilling parameters. Conventional empirical approaches are often inadequate for comprehensive process optimization, effective mitigation of drilling risks, and achievement of the best operational and economic performance. Mathematical models that account for the coupled effects of mechanical, hydraulic, and geological factors are therefore needed to predict drilling performance and optimize operating parameters. This study develops a multicriteria framework for evaluating and selecting drilling parameters for process-borehole construction. The methodology comprised classification of geological, hydrogeological, and operational conditions; statistical analysis of geological and drilling-performance data; and mathematical modeling followed by parameter optimization. The input data—weight on bit G , rotary speed n, hydraulic power N d , and actual rate of penetration V m —were collected during field operation of an SBSh-160 rotary drill and analyzed as grouped frequency distributions. The modeling was performed in Statistica for three operating modes: optimal, constraint-limited, and special-purpose. Each mode represents a different set of operational objectives and constraints. The rate of penetration (ROP) was used as the objective function, with G , n , and N d treated as control variables. A separate empirical relationship was obtained for each mode. For the optimal mode, the model was v m = 0.394 G 0.76 n −0.7 N d 0.46 , R ² = 0.857. For the constraint-limited mode, the relationship was v m = 0.033 G 2.189 n −0.108 N d −0.722 , R ² = 0.843. For the special-purpose mode, which applies under challenging geological conditions such as rock anisotropy and inclined bedding, the model incorporating the relevant geomechanical constraints was v m = 1.52 G 0.94 n 0.21 N d 0.38 , R ² = 0.81. Pairwise regression analysis also identified logarithmic relationships among the principal drilling parameters. Under the optimal operating mode, weight on bit was related to rotary speed by G o = 190.7 ln n − 570.2 ( R ² = 0.86), while the rate of penetration depended on weight as V mo = 15.4 ln G − 58.6 ( R ² = 0.98). Model performance was evaluated by comparing predicted and measured ROP values; the root-mean-square deviation did not exceed 9.42%. The results provide practical operating ranges for drilling parameters and can support real-time monitoring and adaptive drilling control. In particular, the relationships obtained for the optimal mode can be used to select combinations of G , n , and N d that reduce unintentional wellbore deviation. More broadly, the models enable drilling parameters to be selected on a quantitative basis, thereby improving borehole quality, preserving the mechanical integrity of the bottomhole formation, extending equipment maintenance intervals, and improving the overall operational and economic performance of process-borehole construction. The proposed framework thus provides a quantitative basis for drilling optimization under specified geological and operating conditions.