A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

作     者：Hongfei Ma Wenqi Zhao Yurong Zhao Yu He 

作者机构：School of Energy ResourcesChina University of Geosciences(Beijing)Beijing100083China Research Institute of Petroleum Exploration and DevelopmentChina National Petroleum CorporationBeijing100083China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第134卷第3期

页      面：1773-1790页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0711[理学-系统科学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：China University of Geosciences  Beijing  CUGB 

主　　题：Gradient boosting decision tree production prediction data analysis 

摘      要：Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.