Predicting the Molecular Models, Types, and Maturity of Kerogen in Shale Using Machine Learning and Multi-NMR Spectra
文献类型：期刊论文
| 作者 | Kang DL(康东亮)1,2 ; Zhao YP(赵亚溥)1,2
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| 刊名 | ENERGY & FUELS
 |
| 出版日期 | 2022-05-16 |
| 页码 | 13 |
| ISSN号 | 0887-0624 |
| DOI | 10.1021/acs.energyfuels.2c00738 |
| 通讯作者 | Zhao, Ya-Pu(yzhao@imech.ac.cn) |
| 英文摘要 | Kerogen is the primary hydrocarbon source of shale oil/gas. The kerogen types and maturity are the two most crucial indicators that can reflect the hydrocarbon generation potential of shale o il/gas reservoirs. These indicators and the other mechanochemical properties can be effectively studied in a bottom-up strategy using kerogen molecular models. Thus, the rapid construction of kerogen molecular models is the cornerstone of shale oil/gas exploitation research. Because of the combinatorial explosion problem, there are two inherent disadvantages of traditional methods: being time- and material-consuming and labor-intensive. We propose a new method that combines machine learning with multiple nuclear magnetic resonance spectra to intelligently and with a high throughput predict the kerogen structures, types, and maturity. Neither the manual analysis of experimental spectra nor the enormous trial-and-error process is required in our method. The 650,000 groups of samples are annotated as the sample datasets. Various spectral types can be analyzed comprehensively using the multi-spectral form, and the predictive capability beyond that of the single input form is obtained. The results demonstrate that the average similarity of prediction molecules and the targets is 91.78%. The prediction accuracy of kerogen components, types, and maturity indexes is better than 92.4%, and the coefficients of determination R-2 are all over 0.934. The results exhibit the excellent comprehensive performance and effectiveness of our method. Thus, we anticipate that this work will shorten the research cycle and tremendously reduce costs in constructing kerogen models and predicting kerogen properties. |
| 分类号 | 二类 |
| WOS关键词 | ORGANIC TYPE ; ADSORPTION ; OIL ; METHANE ; SIMULATION ; VITRINITE ; MIXTURES ; MOISTURE |
| 资助项目 | National Natural Science Foundation of China (NSFC)[12032019] ; National Natural Science Foundation of China (NSFC)[11872363] ; National Natural Science Foundation of China (NSFC)[51861145314] ; Chinese Academy of Sciences (CAS) Key Research Program of Frontier Sciences[QYZDJ-SSW-JSC019] ; CAS Strategic Priority Research Program[XDB22040401] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000819242600001 |
| 资助机构 | National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) Key Research Program of Frontier Sciences ; CAS Strategic Priority Research Program |
| 其他责任者 | Zhao, Ya-Pu |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/89727]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Kang DL,Zhao YP. Predicting the Molecular Models, Types, and Maturity of Kerogen in Shale Using Machine Learning and Multi-NMR Spectra[J]. ENERGY & FUELS,2022:13. |
| APA | 康东亮,&赵亚溥.(2022).Predicting the Molecular Models, Types, and Maturity of Kerogen in Shale Using Machine Learning and Multi-NMR Spectra.ENERGY & FUELS,13. |
| MLA | 康东亮,et al."Predicting the Molecular Models, Types, and Maturity of Kerogen in Shale Using Machine Learning and Multi-NMR Spectra".ENERGY & FUELS (2022):13. |
入库方式： OAI收割
来源：力学研究所
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