Data–driven decision–making for lost circulation treatments: A machine learning approach

作     者：Husam H.Alkinani Abo Taleb T.Al-Hameedi Shari Dunn-Norman 

作者机构：Missouri University of Science and Technology1201 N State StRollaMO 65409USA 

出 版 物：《能源与人工智能(英文)》 (Energy and AI)

年 卷 期：2020年第2卷第2期

页      面：125-135页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：The authors would like to thank Basra Oil Company from Iraq for providing us with real field data 

主　　题：Machine learning Lost circulation Data-driven Classification 

摘      要：Lost circulation is an expensive and critical problem in the drilling *** of dollars are spent every year to mitigate or stop this *** this work,data from over 3000 wells were collected from multiple *** data went through a processing step where all outliers were removed and decision rules were set *** machine learning methods(support vector machine,decision trees,logistic regression,artificial neural networks,and ensemble trees)were used to create a model that can predict the best lost circulation treatment based on the type of loss and the reason of loss.5-fold cross-validation was conducted to ensure no overfitting in the created *** using all the aforementioned machine learning methods to train models to choose the best lost circulation treatment,overall,the results showed that support vector machine had the highest accuracy among the other ***,it was selected to train the *** created model went through quality control/quality assurance(QC/QA)to limit the results of incorrect *** treatments were suggested to treat partial loss,four to treat severe loss,and seven for complete loss,based on the reason of *** addition,a formalized methodology to respond to lost circulation was provided to help the drilling personnel handling lost circulation in the field.