2023
DOI: 10.3389/feart.2023.1238121
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A data-driven method for total organic carbon prediction based on random forests

Jinyong Gui,
Jianhu Gao,
Shengjun Li
et al.

Abstract: The total organic carbon (TOC) is an important parameter for shale gas reservoir exploration. Currently, predicting TOC using seismic elastic properties is challenging and of great uncertainty. The inverse relationship, which acts as a bridge between TOC and elastic properties, is required to be established correctly. Machine learning especially for Random Forests (RF) provides a new potential. The RF-based supervised method is limited in the prediction of TOC because it requires large amounts of feature varia… Show more

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Cited by 2 publications
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