Application of machine learning algorithms to predict tubing pressure in intermittent gas lift wells

作     者：Nagham Amer Sami Nagham Amer Sami

作者机构：Department of Petroleum EngineeringFaculty of Earth ScienceUniversity of Miskolc3515MiskolcHungary 

出 版 物：《Petroleum Research》 (石油研究（英文）)

年 卷 期：2022年第7卷第2期

页      面：246-252页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 

主　　题：Machine learning Artificial intelligence Intermittent gas lift Tubing pressure Random forest Decision tree KNN 

摘      要：Tubing pressure at gas injection depth in intermittent wells is one of the most critical parameters for production engineers to evaluate the performance of the ***,monitoring of the tubing pressure is not usually carried out in real *** has been realized that the generally used correlations are not effective enough due to complexity of the intermittent process which involve many parameters and assumptions to develop such *** focus of this study is to utilize machine learning(ML)algorithms to develop a model that can accurately predict tubing pressure in artificial intermittent gas lift *** algorithms built on the field data provide a solution that is easy to use and universally applicable to the complex *** non-linear regression ML methods are employed in this study,namely,Decision Tree-regression(DT),Random Forest-regression(RF)and K Nearest Neighbors-regression(KNN).All the tubing pressures obtained from ML models were compared with the actual values to ensure the effectiveness of the *** developed models show that it can predict the pressure with more than 99.9%*** is an interesting result,as such outcome accuracy has not been reported usually in the open literature.