基于遗传算法优化随机森林模型的 机械钻速分类预测方法.

In order to predict the drilling rate of a certain oil field in the east accurately, based on the analysis of factors affecting the drilling rate of a well in the oil field, the rate of penetration (ROP) of PDC bits with different diameters was classified according to the field experience. The penetration prediction model was set up using the random forest algorithm, K-nearest Neighbour algorithm, support vector machine (SVM) algorithm, and the model parameters were optimized using the genetic algorithm, the classification and prediction method for ROP was obtained to meet the requirements of construction design and field operation. The results show that the accuracy of prediction for ROP by the random forest model optimized by genetic algorithm is 82.1%, which is significantly higher than the K-nearest neighbor algorithm and support vector machine algorithm. This method can guide the optimization of drilling parameters in this block to improve drilling efficiency. [ABSTRACT FROM AUTHOR]