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  2. 地质与地球物理研究所
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  4. 中国科学院页岩气与地质工程重点实验室
Drilling formation perception by supervised learning: Model evaluation and parameter analysis

文献类型：期刊论文

作者Ye, Zhihui1; Guo, Siyao1; Chen, Dong2,3; Wang, Han1; Li, Shouding4
刊名JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING
出版日期2021-06-01
卷号90页码:12
ISSN号1875-5100
关键词Lithology identification Machine learning Well logging data Adaboost Optimal parameter combination
DOI10.1016/j.jngse.2021.103923
英文摘要Formation and lithology identification based on well logging curves reflecting geophysical response characteristics is fundamental for drilling planning and reservoir recovery. For the purpose of providing efficient, accurate, and comprehensive insights for drilling operation decisions, the present research evaluates three typical supervised learning algorithms based on machine learning, e.g. Adaboost, decision tree and support vector machine (SVM). By comparing the prediction results from three typical classification algorithms based on performance metrics such as accuracy, precision, recall, F1 score, Adaboost and decision tree are found to present more accurate prediction with relatively higher accuracy, precision, recall and F1 score. The prediction accuracy is positively related to the training data set proportion for all three approaches. Decision tree approach spends less computation time while still provides favorable prediction scores. The accuracy of prediction gradually increases as the number of logging features increases. Accuracy for most parameter combinations beyond four logging parameters can be up to 90%. The neutron porosity and the spontaneous potential are considered as the most influential parameters affecting the prediction accuracy.
WOS关键词LITHOLOGY ; IDENTIFICATION
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA14040402] ; CNPC[ZLZX2020-03] ; CUPB[ZLZX2020-03] ; National Key Research and Development Project[2019YFA0708300]
WOS研究方向Energy & Fuels ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000646200600005
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; CNPC ; CNPC ; CUPB ; CUPB ; National Key Research and Development Project ; National Key Research and Development Project ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; CNPC ; CNPC ; CUPB ; CUPB ; National Key Research and Development Project ; National Key Research and Development Project ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; CNPC ; CNPC ; CUPB ; CUPB ; National Key Research and Development Project ; National Key Research and Development Project ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; CNPC ; CNPC ; CUPB ; CUPB ; National Key Research and Development Project ; National Key Research and Development Project
源URL[http://ir.iggcas.ac.cn/handle/132A11/101154]  
专题地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室
通讯作者Chen, Dong
作者单位1.China Univ Petr, Coll Safety & Ocean Engn, Beijing 102249, Peoples R China
2.China Univ Petr, Coll Petr Engn, State Key Lab Petr Resources & Prospecting, Beijing 102249, Peoples R China
3.China Univ Petr, Coll Petr Engn, Beijing 102249, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Ye, Zhihui,Guo, Siyao,Chen, Dong,et al. Drilling formation perception by supervised learning: Model evaluation and parameter analysis[J]. JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING,2021,90:12.
APA Ye, Zhihui,Guo, Siyao,Chen, Dong,Wang, Han,&Li, Shouding.(2021).Drilling formation perception by supervised learning: Model evaluation and parameter analysis.JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING,90,12.
MLA Ye, Zhihui,et al."Drilling formation perception by supervised learning: Model evaluation and parameter analysis".JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 90(2021):12.

入库方式： OAI收割

来源：地质与地球物理研究所

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