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

为准确预测东部某油田机械钻速,在针对该油田某井机械钻速影响因素分析的基础上,根据现场经验对不同直径PDC钻头的机械钻速进行分级,运用随机森林算法、K近邻算法、支持向量机算法建立机械钻速分类预测模型,并运用遗传算法优化模型参数,得到了满足施工设计及现场作业需要的机械钻速分类预测方法。结果表明,运用遗传算法优化后的随机森林模型预测机械钻速分类准确率为82.1%,明显高于K近邻算法和支持向量机算法,该方法可指导该区块钻井施工参数优化,以提高钻井施工效益。

Classification and prediction method for ROP based on genetic algorithm optimization random forest model

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 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 showed 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.