个人信息Personal Information
副教授
硕士生导师
性别:女
毕业院校:法国里尔中央理工大学
学位:博士
所在单位:交通运输系
学科:交通运输规划与管理
办公地点:大连理工大学土木实验4号楼516房间
电子邮箱:lian.lian@dlut.edu.cn
Modeling oil production based on symbolic regression
点击次数:
论文类型:期刊论文
发表时间:2015-07-01
发表刊物:ENERGY POLICY
收录刊物:SCIE、EI、SSCI
卷号:82
期号:1
页面范围:48-61
ISSN号:0301-4215
关键字:Oil production; Hubbert theory; Symbolic regression
摘要:Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. (c) 2015 Elsevier Ltd. All rights reserved.