【摘要】 钻井机械钻速的提高是降低钻井周期，减少作业成本的重要措施。目前，采用传统的改进工具工艺手段来提高机械钻速不仅投资成本高，而且在应用效果上差异性大。针对这一难题，本文提出基于遗传算法优化的随机森林机械钻速预测模型。首先对数据进行处理、筛选，并为提高数据在算法模型的拟合程度，使用卡尔曼滤波对其进行降噪处理。然后，对输入特征参数进行相关性分析，筛选出最终适合的特征参数，降低模型冗余。最后通过实验验证，结果表明本文提出的遗传算法-随机森林机械钻速模型具有较高的精度，同时具有良好的收敛性.更多还原

【Abstract】 The improvement of ROP of drilling machinery is an important measure to reduce drilling cycle and operation cost. At present, the traditional means of improving tool technology to improve the ROP has not only high investment cost, but also has great differences in application effects. To solve this problem, this paper proposes a random forest machinery ROP prediction model based on genetic algorithm optimization. First of all, the data is processed and screened, and in order to improve the fitting degree of the data in the algorithm model, Kalman filter is used for noise reduction. Then, after the correlation analysis of input characteristic parameters, the final suitable characteristic parameters are selected to reduce the redundancy of the model. Finally, the characteristic parameters are combined with the model through experiments. The results show that the genetic algorithm-random forest machinery ROP model proposed in this paper has high accuracy and good convergence.更多还原