NAOC Open IR
A data-driven shale gas production forecasting method based on the multi-objective random forest regression
Xue, Liang1,2; Liu, Yuetian1,2; Xiong, Yifei3,4; Liu, Yanli5; Cui, Xuehui5; Lei, Gang6
2021
Source PublicationJOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
ISSN0920-4105
Volume196Pages:13
AbstractShale gas is an important unconventional natural gas resource existing in shale reservoir with huge reserves. Due to the ultralow porosity and permeability, it requires the horizontal well drilling and the multi-stage hydraulic fracturing technology to successfully produce the shale gas. The accurate prediction of shale gas production is crucial to the reasonable design of the development plan. However, due to the complex hydraulic fracture network and the gas flow mechanism, the physics-based shale gas production prediction model is still under way. The data-driven model provide an alternative way to deal with the production prediction problem. The multi-objective random forest method is proposed to predict the dynamic production data. The geological and hydraulic fracturing properties are used as input feature. Its prediction performance is evaluated based on the R squared values after determining the appropriate hyper-parameters. The ranking of variable importance can be helpful to improve the interpretability of the data-driven model. The initial peak production rate before declining can be also used as an additional input feature. With the initial peak production rate augmented into the feature set, it can greatly improve the prediction of shale gas production. The variable importance analysis results show that it can be the most influencing factor to prediction accuracy and the ranking of other factors can be altered significantly. The performance of multi-objective random forest (MORF) and multi-output regression chain (MORC) methods are compared, and the comparison result indicates MORC requires a relatively smaller random forest structure, but the prediction performance of MORF is better than MORC. More sample data with less measurement errors can increase the accuracy of the data-driven shale gas production model but there exists a threshold value to improve the accuracy of the data-driven shale gas production model but there exists a threshold value to improve the accuracy gain.
KeywordShale gas Production forecasting Machine learning Random forest
Funding OrganizationNational Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing ; National Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing ; National Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing ; National Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing
DOI10.1016/j.petrol.2020.107801
WOS KeywordDISCRETE-FRACTURE MODEL ; PERFORMANCE PREDICTION ; RESERVOIR SIMULATION ; FLOW ; PERMEABILITY ; OPTIMIZATION ; PRESSURE ; WORKFLOW ; FACIES ; BASIN
Language英语
Funding ProjectNational Science and Technology Major Project of China[2016ZX05037003-003] ; National Science and Technology Major Project of China[2017ZX05032004-002] ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project[P18086-5] ; National Natural Science Foundation of China[51374222] ; Major Science and Technology Project of CNPC[2017E-0405] ; SINOPEC Science and Technology Key Program[P18049-1] ; Science Foundation of China University of Petroleum, Beijing[2462018QZDX13] ; Science Foundation of China University of Petroleum, Beijing[2462020YXZZ028]
Funding OrganizationNational Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing ; National Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing ; National Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing ; National Science and Technology Major Project of China ; National Science and Technology Major Project of China ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; SINOPEC Ministry of Science and Technology Basic Prospective Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major Science and Technology Project of CNPC ; Major Science and Technology Project of CNPC ; SINOPEC Science and Technology Key Program ; SINOPEC Science and Technology Key Program ; Science Foundation of China University of Petroleum, Beijing ; Science Foundation of China University of Petroleum, Beijing
WOS Research AreaEnergy & Fuels ; Engineering
WOS SubjectEnergy & Fuels ; Engineering, Petroleum
WOS IDWOS:000600808100109
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/79930
Collection中国科学院国家天文台
Corresponding AuthorLei, Gang
Affiliation1.China Univ Petr, State Key Lab Petr Resources & Prospecting, Beijing 102249, Peoples R China
2.China Univ Petr, Dept Oil Gas Field Dev Engn, Coll Petr Engn, Beijing 102249, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Shanghai Astron Observ, Shanghai 200030, Peoples R China
5.China Univ Petr, Coll Sci, Beijing 102249, Peoples R China
6.King Fahd Univ Petr & Minerals, Dept Petr Engn, CPG, Dhahran 31261, Saudi Arabia
Recommended Citation
GB/T 7714
Xue, Liang,Liu, Yuetian,Xiong, Yifei,et al. A data-driven shale gas production forecasting method based on the multi-objective random forest regression[J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,2021,196:13.
APA Xue, Liang,Liu, Yuetian,Xiong, Yifei,Liu, Yanli,Cui, Xuehui,&Lei, Gang.(2021).A data-driven shale gas production forecasting method based on the multi-objective random forest regression.JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,196,13.
MLA Xue, Liang,et al."A data-driven shale gas production forecasting method based on the multi-objective random forest regression".JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 196(2021):13.
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