计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 619-622.doi: 10.11896/jsjkx.201000070
许明泽, 韦明辉, 邓霜, 蔡卫
XU Ming-ze, WEI Ming-hui, DENG Shuang, CAI Wei
摘要： 钻井的机械钻速与钻井操作参数、钻井液性能以及钻具组合等因素有关。准确预测机械钻速可以有效计算钻井成本和钻进时间,从而优化钻进参数、合理安排钻机工作人员,并为钻井设计人员提供依据。结合目前机器学习和大数据处理,利用中国西部吐哈油田历史钻井数据,建立了一种基于集成学习的钻速预测模型。其成员包括KNN(K近邻)、DT(决策树)、SVR(支持向量机)、RF(随机森林),输入7个特征影响因素,包括井深、钻压、泵压、密度、粘度、排量和转速,将拟合优度作为机械钻速预测的评价指标,结果显示集成模型的预测输出优于任何一种单一模型的结果。以7-13井为例,拟合优度R2达到了0.93以上。文中还探讨了不同集成成员的组合,结合时间成本和拟合优度发现最优组合为KNN+SVR+RF,其拟合优度在7-13,8-17,4-10井分别达到了0.937 8,0.918 7,0.912 4。最后,以SVR为例,优化后的单一模型拟合准确性依旧低于任何一组组合模型。进一步的研究表明,有效的集成模型需要集成成员的多样性和较高的精度。这些预测结果表明,该模型为机械钻速预测提供了一种有前途的替代方案。
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