Simulation-Driven Rate-of-Penetration Prediction and Multi- Objective Optimization of Drilling Parameters for Tricone Bits

Optimizing drilling parameters is a critical challenge for improving both the efficiency and cost-effectiveness of deep and complex strata drilling. The core objective lies in achieving a multi-objective balance among maximizing the rate of penetration (ROP), minimizing specific energy consumption, and prolonging bit life. Conventional approaches, which typically rely on semi-empirical ROP prediction models and classical optimization algorithms (e.g., NSGA-II), often suffer from limited model adaptability and suboptimal convergence performance when confronted with highly nonlinear and strongly coupled optimization problems. To address these limitations, this study proposes a simulation-driven modeling and optimization framework for a tricone bit–sandstone formation system. First, a dynamic drilling simulation model is developed in ABAQUS, and parametric analyses (with weight on bit ranging from 20–60 kN and rotational speed from 60–120 r/min) are conducted to obtain the bit’s dynamic response data. Based on these data, a response surface methodology (RSM)-based ROP prediction model is constructed, achieving an average relative error of only 4.4%. Second, the simulation-informed mechanical model is integrated with Teale’s specific energy theory and a bit wear model to formulate a multi-objective optimization problem targeting ROP, specific energy, and bit life. To efficiently solve this problem, the dung beetle optimization (DBO) algorithm is introduced. By incorporating dynamic weight adaptation, elite-chaotic initialization, and a spiral search enhancement strategy, the algorithm effectively balances global exploration and local exploitation. Optimization results demonstrate that, in terms of the IGD metric, DBO outperforms NSGA-II and MOPSO by 10% and 70%, respectively, while also yielding a more uniform and diverse Pareto front. Overall, this study verifies the superior optimization performance of the proposed simulation-driven modeling framework and the DBO algorithm under complex operational conditions, providing a robust theoretical foundation and methodological support for intelligent decision-making in deep drilling.