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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:China Univ Petr East China Key Lab Unconvent Oil & Gas Dev Minist Educ Qingdao 266580 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Shengli Oil Field Explorat & Dev Res Inst Dongying 257000 Peoples R China
出 版 物:《JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING》 (石油科学和石油工程杂志)
年 卷 期:2021年第205卷
页 面:108879-108879页
核心收录:
学科分类:0820[工学-石油与天然气工程] 08[工学]
基 金:Fundamental Research Funds for the Central Universities [19CX02069A]
主 题:Production optimization Machine learning Proxy model Extreme gradient boosting Differential evolution algorithm
摘 要:Production Optimization is a significant method for oilfields to control water cut and stabilize oil production. When the oilfield enters the high or ultra-high water cut stage, it becomes particularly important to use production optimization methods for improving the water-flooding efficiency. Currently, the commonly used production optimization methods are based on reservoir simulators. Such methods require lots of forward simulations during optimizing, which results in low computational efficiency. It s not applicable to the reservoir without numerical simulation models. Thus, a new production optimization method based on the reservoir proxy model is proposed in this work. Firstly, the dynamic production data of the oilfield are collected and preprocessed for training the Extreme Gradient Boosting model, and constructing the proxy model for water cut prediction of producers and the reservoir. Then, an optimal control model for minimizing the water cut of the reservoir can be constructed based on the proxy model. Finally, an optimal injection-production scheme can be obtained by using the differential evolution algorithm. For the evaluation and verification purposes, the proposed method is applied to a well block from SL oilfield, China. Empirical results demonstrated that the proposed method can effectively improve the water-flooding efficiency.