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• W3115358103 endingPage "120006" @default.
• W3115358103 startingPage "120006" @default.
• W3115358103 abstract "• Combining machine learning with NMR spectra to predict the kerogen components and types. • Propose a solution to convert the 2D graph into the available form for machine learning models. • Build an automatic labeling platform and generate over 22,000 training samples. • The prediction accuracy of indicators in the optimal model can achieve 90% in total. • The method has advantages of intelligentization, high-throughput prediction, high accuracy. This study aims to develop a new method that combines machine learning with nuclear magnetic resonance (NMR) spectra to predict the kerogen components and types. Kerogen is the primary hydrocarbon source of shale oil/gas, and nearly half of the hydrocarbons in shale are adsorbed in kerogen. The adsorption and hydrocarbon generation capacity of kerogen is directly related to its types, molecular components, and structures. Fruitful researches studying kerogen at the molecular level have been conducted. Unfortunately, these methods are complicated, time-consuming, and labor-intensive. Our method has the advantages of high-throughput prediction, high accuracy, and time savings compared with the existing methods. Additionally, this method simplifies the operations from repetitive trial and error. This study proposes a solution to convert non-uniform two-dimensional (2D) graph into a uniform one-dimensional (1D) matrix, which makes 2D graph data available for machine learning models. An automatic labeling platform is constructed that annotated over 22,000 groups of organic matter molecules and their NMR spectra. The results show that the carbon, hydrogen, and oxygen element prediction accuracy reach 96.1%, 94.8%, and 81.7%, respectively. In addition, the accuracy of the three kerogen types is approximately 90% in total. These results reflect the excellent performance of the machine learning method. Therefore, our work provides an automated and intelligent prediction and analysis method, which is a powerful and superior tool in kerogen studies at the molecular level." @default.
• W3115358103 created "2021-01-05" @default.
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• W3115358103 date "2021-04-01" @default.
• W3115358103 modified "2023-10-17" @default.
• W3115358103 title "Predicting the components and types of kerogen in shale by combining machine learning with NMR spectra" @default.
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• W3115358103 doi "https://doi.org/10.1016/j.fuel.2020.120006" @default.
• W3115358103 hasPublicationYear "2021" @default.
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