Identification of thermally mature total organic carbon-rich layers in shale formations using an effective machine-learning approach
| العنوان: | Identification of thermally mature total organic carbon-rich layers in shale formations using an effective machine-learning approach |
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| المؤلفون: | Adewale Amosu, Yuefeng Sun |
| المصدر: | Interpretation. 9:T735-T745 |
| بيانات النشر: | Society of Exploration Geophysicists, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Total organic carbon, 020209 energy, Wireline, Mineralogy, Geology, 02 engineering and technology, 010502 geochemistry & geophysics, 01 natural sciences, Support vector machine, Identification (information), Geophysics, 0202 electrical engineering, electronic engineering, information engineering, Oil shale, 0105 earth and related environmental sciences |
| الوصف: | We have developed a support vector machine (SVM) method that relies on core-measured data as well as gamma-ray, deep resistivity, sonic, and density wireline well-log data in identifying thermally mature total organic carbon (TOC)-rich layers at depth intervals with missing geochemical data in unconventional resource plays. We first test the SVM method using the Duvernay Shale Formation data. The SVM method successfully classifies the TOC data set into TOC-rich and TOC-poor classes and the [Formula: see text] data set into thermally mature and thermally immature classes when the optimal features are selected. To further test the SVM approach, we generate depth-separated training and test data sets from a well in the Duvernay Shale Formation and successfully use the approach to identify thermally mature TOC-rich intervals. We also examine the successful cross basin application of the SVM approach in predicting TOC using data from the Barnett and Duvernay Shale Formations as the training and test data sets, respectively. |
| تدمد: | 2324-8866 2324-8858 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::63fc3bd6855f5be26e4891118fec819a https://doi.org/10.1190/int-2020-0184.1 |
| رقم الأكسشن: | edsair.doi...........63fc3bd6855f5be26e4891118fec819a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23248866 23248858 |
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