钻井液与完井液2023，Vol.40Issue(6)：765-770,6.DOI:10.12358/j.issn.1001-5620.2023.06.010
基于XGBoost的钻井液体系分类预测模型研究
Study on Prediction Model for Drilling Fluid Classification Based on XGBoost
摘要
Abstract
A model for predicting the type of a drilling fluid system was established using a new machine learning method based on the principles of mud system design and by referencing the actual drilling fluid designs.By one-hot coding of the data concerning the classification of drilling fluid systems,twenty parameters for predicting the type of a drilling fluid were selected through grey relation analysis.Of these parameters pressure has the highest correlation degree,which is 0.8233.The selected geological parameters and engineering design parameters were used based on an extreme gradient boost(XGBoost)algorithm to predict the types of 4 drilling fluids.The results show that the accuracy of the training sets of the 4 drilling fluids are all 100%,the average percent accuracy of the test sets is 99.89%,the precision 99.97%,the recall rate 98.89%,and the F1 value 0.98.Applying this model to the M block in the Shengli Oilfield,the classification results met the drilling requirements,and was of help in selecting the suitable drilling fluids.This study has provided a help to the intelligent design of drilling fluid.关键词
钻井液体系设计/XGBoost/机器学习/灰色关联度分析Key words
Design of drilling fluid system/XGBoost/Machine learning/Grey relation analysis分类
能源科技引用本文复制引用
花露露,曹晓春,王劲草,王金,焦昱璇..基于XGBoost的钻井液体系分类预测模型研究[J].钻井液与完井液,2023,40(6):765-770,6.基金项目
东北石油大学大学生创新训练项目"大庆页岩油区块钻井液优化设计研究"(202210220149) （202210220149）
黑龙江省大学生创新创业训练计划项目"基于大数据的油气钻井工作液信息处理和设计平台"(202010220096). （202010220096）
