Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

作     者：Wen-Peng Bai Shi-Qing Cheng Xin-Yang Guo Yang Wang Qiao Guo Chao-Dong Tan 

作者机构：State Key Laboratory of Petroleum Resources and EngineeringChina University of Petroleum(Beijing)Beijing102249China 

出 版 物：《Petroleum Science》 (石油科学（英文版）)

年 卷 期：2024年第21卷第4期

页      面：2554-2570页

核心收录：

学科分类：0820[工学-石油与天然气工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 082002[工学-油气田开发工程] 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）] 

基　　金：supported by the National Natural Science Fund of China (No.52104049) the Science Foundation of China University of Petroleum,Beijing (No.2462022BJRC004) 

主　　题：Data mining technique Analogy parameters Oilfield analogy Productivity prediction Software platform 

摘      要：In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.